breast cancer risk factors research paper

• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Breast cancer: A review of risk factors and diagnosis

Obeagu, Emmanuel Ifeanyi PhD a,* ; Obeagu, Getrude Uzoma BN Sc b

a Department of Medical Laboratory Science, Kampala International University, Kampala, Uganda

b School of Nursing Science, Kampala International University, Kampala, Uganda.

Received: 29 July 2023 / Received in final form: 15 December 2023 / Accepted: 18 December 2023

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

The authors have no funding and conflicts of interest to disclose.

How to cite this article: Obeagu EI, Obeagu GU. Breast cancer: A review of risk factors and diagnosis. Medicine 2024;103:3(e36905).

* Correspondence: Emmanuel Ifeanyi Obeagu, Department of Medical Laboratory Science, Kampala International University, Kampala, Uganda (email: [email protected] ).

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the multifaceted landscape of breast cancer, elucidating the diverse spectrum of risk factors contributing to its occurrence and exploring advancements in diagnostic methodologies. Through an extensive examination of current literature, various risk factors have been identified, encompassing genetic predispositions such as BRCA mutations, hormonal influences, lifestyle factors, and reproductive patterns. Age, family history, and environmental factors further contribute to the intricate tapestry of breast cancer etiology. Moreover, this review delineates the pivotal role of diagnostic tools in the early detection and management of breast cancer. Mammography, the cornerstone of breast cancer screening, is augmented by emerging technologies like magnetic resonance imaging and molecular testing, enabling improved sensitivity and specificity in diagnosing breast malignancies. Despite these advancements, challenges persist in ensuring widespread accessibility to screening programs, particularly in resource-limited settings. In conclusion, this review underscores the importance of understanding diverse risk factors in the development of breast cancer and emphasizes the critical role of evolving diagnostic modalities in enhancing early detection. The synthesis of current knowledge in this review aims to contribute to a deeper comprehension of breast cancer’s multifactorial nature and inform future directions in research, screening strategies, and preventive interventions.

1. Introduction

One of the frequent and numerous malignant tumors that affect women is breast cancer. Breast cancer develops and occurs as a result of several internal and external factors. [ 1–3 ] Poor lifestyle choices, environmental factors, and social-psychological factors are all linked to its occurrence. It has been demonstrated that 5% to 10% of breast cancers can be attributed to genetic mutations and family history, and 20% to 30% of breast cancers can be attributed to factors that may be modifiable. [ 4 ] Breast cells are where breast cancer first develops. A collection of cancer cells known as a cancerous tumor is capable of spreading into and destroying nearby tissue. As well as spreading throughout the body, it can. Breast cells occasionally undergo changes that prevent them from growing or behaving normally. Non-cancerous breast conditions atypical hyperplasia and cysts may result from these changes. Additionally, they may result in benign tumors like intraductal papillomas. [ 5 ]

However, breast cancer can occasionally result from changes to breast cells. Breast cancer typically begins in the cells that line the ducts, which are the tubes that carry milk from the glands to the nipple. Ductal carcinoma is the name given to this subtype of breast cancer. The cells of the lobules, which are the collections of milk-producing glands, can also give rise to cancer. [ 6 , 7 ] Lobular carcinoma is the name of this type of cancer. Both ductal and lobular carcinomas can be in situ, which means the cancer is still present in the area where it first appeared and has not spread to adjacent tissues. They may also be invasive, which indicates that they have spread into the tissues around them. [ 8 ] Breast cancer can also manifest itself in less common forms. These include triple-negative breast cancer, breast Paget disease, and inflammatory breast cancer. Non-Hodgkin lymphoma and soft tissue sarcoma are uncommon forms of breast cancer. [ 9 ] Despite having a low incidence of breast cancer, studies show that it has been steadily rising in China. By 2022, the number of Chinese women who will have the disease will surpass 100 per 100,000, and there will be 2.5 million women with the disease overall, aged 35 to 49. Thus, it is crucial to research breast cancer risk factors to lower the disease’s incidence. [ 10 ] Breast cancer is the most prevalent cancer in women worldwide and the main reason why women die from cancer. About 630,000 women lost their lives to breast cancer in 2018, and there were approximately 2.09 million newly diagnosed cases. While there are regional variations in breast cancer incidence, it is rising. Due to China’s large population and high incidence of breast cancer, which ranks first globally and has increased over the past few years (17.6% and 15.6%, respectively), even though the incidence of breast cancer (36.1/105) and mortality (8.8/105) are both relatively low worldwide. The burden of the disease is rising alongside the incidence of breast cancer globally, which has grown to be a significant issue for global public health. [ 11 ] Breast cancer is a multifactorial disease with major genetic, environmental, and behavioral/lifestyle components. The objective of the current review was to investigate the epidemiology and associated risk factors of breast cancer globally to comprehend its prevalence and aid in early detection. The main risk factors for breast cancer are genetic factors, specifically family history; diet, and obesity, as the quality of life in our country improves, women are getting more and more obese, and their diet tends to be more and more high-fat; smoking and drinking; the other is ionizing radiation; still have, specifically menstruation, bear, and whether lactation, these factors also can affect the occurrence of breast cancer. To lessen the impact exogenous hormones, have on the body, we should try to avoid using cosmetics that contain estrogen in our daily lives. Around these appeals, there has been a lot of debate. As a result, it is essential to thoroughly examine the risk factors for breast cancer using meta-methods to direct clinical prevention and treatment. [ 12 ] We conducted a meta-analysis of breast cancer risk factors in Chinese women in the current study by gathering pertinent literature from 2001 to 2021, even though Chinese scholars have already done so. [ 13 ] Our goal was to provide fundamental information for the prevention of breast cancer in Chinese women. Something that raises your chance of getting cancer is a risk factor. A habit, substance, or illness could be the culprit. Numerous risk factors contribute to the majority of cancers. However, breast cancer can occasionally develop in women who don’t have any of the risk factors listed below. Women are more likely than men to develop breast cancer. Women are more likely to develop breast cancer when estrogen and progesterone are exposed to their breast cells.

Some breast cancers are aided in their growth by these hormones, particularly estrogen, which has been linked to breast cancer. Canada, the United States, and a few European nations are examples of high-income, developed nations where breast cancer is more prevalent. Age raises the likelihood of getting breast cancer. Women between the ages of 50 and 69 are the most common demographic for breast cancer. [ 14 ] The most frequently diagnosed cancer in women and a major global health concern is breast cancer. Researchers have found several risk factors that can raise a woman’s likelihood of getting breast cancer, even though the disease’s precise cause is still unknown. For early detection, prevention, and efficient management of breast cancer, [ 14 ] it is essential to comprehend these risk factors. With more than 1 in 10 new cancer diagnoses each year, breast cancer is the most common cancer in women. In the entire world, it is the second most typical cause of cancer-related death in females. Milk-producing glands are located in front of the chest wall on the anatomy of the breast. They rest on the pectoralis major muscle, and the breast is supported by ligaments that join it to the chest wall. The breast is made up of 15 to 20 lobes that are arranged in a circle. The size and shape of the breasts are determined by the fat that covers the lobes. Each lobe is made up of lobules that contain the glands that produce milk when hormones are stimulated. Breast cancer always progresses subtly. The majority of patients learn they have their illness while getting their regular screenings. Others may exhibit nipple discharge, a breast shape or size change, or an unintentionally discovered breast lump. Mastalgia is not unusual, though. Breast cancer diagnosis requires a physical examination, imaging, particularly mammography, and tissue biopsy. [ 14 ]

Early diagnosis increases the likelihood of survival. Poor prognosis and distant metastasis are caused by the tumor’s propensity to spread lymphatically and hemologically. This clarifies and highlights the significance of programs for breast cancer screening. [ 15 ] Anything that raises the possibility of developing cancer is a risk factor. It might be a habit, substance, or illness. Many risk factors combine to cause the majority of cancers. Women are more likely than men to develop breast cancer. Women are more likely to develop breast cancer when estrogen and progesterone are exposed to their breast cells. Breast cancer is more prevalent in high-income, developed nations like Canada, the United States, and some European nations. These hormones, particularly estrogen, are linked to the disease and promote its growth. As you get older, your risk of developing breast cancer rises. Most breast cancer cases in women are diagnosed between the ages of 50 and 69 years. [ 14 ]

2. Risk factors for developing breast cancer among women

2.1. personal history of breast cancer.

An increased risk of breast cancer recurrence exists in women who have previously experienced it. The second breast cancer may appear in the same breast as the first one or in a different breast. Although the majority of women who have ductal carcinoma in situ or lobular carcinoma in situ breast cancers do not recur, these women are at an increased risk of doing so. [ 16 ]

2.2. Breast and other types of cancer in the family history

The presence of breast cancer in one or more close blood relatives indicates that the disease runs in the family. More breast cancer cases than one might anticipate randomly occur in some families. It can be difficult to determine whether a family’s history of cancer is the result of coincidence, a common lifestyle, genes passed down from parents to children, or a combination of these factors. [ 17 ]

2.3. Mutations in the BRCA gene

An altered gene is referred to as a genetic mutation. Certain types of cancer may be more likely to develop as a result of some gene changes. A parent can pass on inherited gene mutations to their offspring. Only a small percentage of breast cancers (roughly 5%–10%) are brought on by inherited gene mutations. Normal human physiology includes both BRCA1 and BRCA2, which are breast cancer genes. As a result of what seems to be their involvement in regulating the growth of cancer cells, these genes are known as tumor suppressors. BRCA1 or BRCA2 gene mutations may cause them to lose their ability to regulate the development of cancer. Rarely occur these mutations. Roughly 1 in 500 people experience them. A mutated BRCA gene can be inherited by both men and women from either their mother or father. Children of those who carry the gene mutation may also inherit it. A child has a 50% chance of inheriting the gene mutation if 1 of the 2 copies of the BRCA gene has the mutation in 1 or both parents. A child also has a 50% chance of not inheriting the gene mutation, according to this. [ 18 ] According to studies, women who inherit BRCA1 or BRCA2 gene mutations have an 85% lifetime risk of developing breast cancer. Additionally, compared to other women, those who carry these inherited mutations are at an increased risk of developing breast cancer earlier in life. Breast cancer in both breasts is more likely to strike women who have the BRCA gene mutation. They are more likely to get cancer in the other breast if they have cancer in 1 breast. Ovarian cancer can strike a woman at any age if she carries a BRCA gene mutation. [ 19 ]

2.4. Large breasts

Compared to fatty tissue, dense breasts have more milk ducts, glands, and connective tissue. Breast density is a genetic trait. Compared to women with little or no dense breast tissue, women with dense breast tissue have a higher risk of developing breast cancer. Breast density can only be detected by a mammogram, but dense breasts also make the image more difficult to interpret. On a mammogram, dense tissue appears white, like tumors, while fatty tissue appears dark, concealing a tumor. [ 20 ]

2.5. The late menopause

The body’s level of hormones, primarily estrogen and progesterone, begins to decline as the ovaries stop producing them, resulting in menopause. A woman’s menstrual cycle is stopped as a result of this. Your cells are exposed to estrogen and other hormones for a longer period if you enter menopause later in life (after age 55). This raises the possibility of breast cancer. Likewise, breast tissue is exposed to estrogen and other hormones for a shorter period when menopause occurs earlier in life. A lower risk of breast cancer is associated with early menopause. [ 21 ]

2.6. Whether there are late or no pregnancies

Breast cells’ exposure to circulating estrogen is halted during pregnancy. It also reduces the overall number of menstrual cycles a woman experiences throughout her lifetime. A woman’s risk of breast cancer is marginally higher than it is for a woman who has at least one full-term pregnancy before the age of 30. Reduced risk of breast cancer is associated with early pregnancy. A woman is more protected from breast cancer the more children she has. Breast cancer risk is increased if a woman never conceives. [ 22 ]

2.7. Hormonal replacement treatment

According to the Women’s Health Initiative (WHI) study, estrogen alone increased breast cancer risk by about 1% per year and combined hormone replacement therapy (HRT) increased risk by about 8% per year. The study also discovered that, in comparison to a placebo, the risk increased even with relatively brief use of combined HRT. After stopping HRT for a few years, the higher risk seems to be gone. The WHI study also revealed that, among Canadian women aged 50 to 69, there was a notable decline in the number of new cases of breast cancer between 2002 and 2004. The use of combined HRT decreased at the same time as this drop. Other nations around the world, such as the United States, Australia, Germany, the Netherlands, Switzerland, and Norway, have also noticed this trend. The risks associated with the long-term use of combined HRT are now thought to outweigh the advantages. [ 23 ]

2.8. Being overweight

In post-menopausal women, obesity increases the risk of developing breast cancer. According to studies, women with a body mass index of 31.1 or higher who have never used HRT are 2.5 times more likely to develop breast cancer than those with a body mass index of 22.6 or lower. In particular, estrogens from the ovaries play a significant role in breast cancer. Many breast cancer risk factors are thought to be caused by the cumulative estrogen dose that the breast tissue absorbs over time. The majority of the body’s estrogen is produced by the ovaries, but after menopause, fat tissue only produces a small amount of estrogen. A higher estrogen level can result from having more fat tissue, which raises the risk of breast cancer. [ 24 ]

2.9. Estrogen

Breast cancer risk is linked to estrogens, both endogenous and exogenous. In premenopausal women, the ovary typically produces endogenous estrogen, and ovarian removal can lower the risk of breast cancer. HRT and oral contraceptives are the main exogenous estrogen sources. Since the 1960s, oral contraceptives have been extensively used, and their formulations have been improved to minimize side effects. The odd ratio is still higher than 1.5 for Iranian and African American female populations, though. Oral contraceptives do not, however, raise the risk of breast cancer in women who stop using them for more than 10 years. For menopausal or postmenopausal women, HRT entails the administration of exogenous estrogen or other hormones. The use of HRT can raise the risk of breast cancer, according to several studies. According to the Million Women Study in the UK, there is a 1.66 relative risk between those who currently use HRT and those who have never used it. A cohort study of 22,929 Asian women found that after using HRT for 4 and 8 years, respectively, hazard ratios (HRs) of 1.48 and 1.95 were found. After 2 years of stopping HRT, it has been demonstrated that the risk of breast cancer significantly declines. With a 3.6 HR for a new breast tumor, the recurrence rate is also high among breast cancer survivors who take HRT. Since the negative effects of HRT were revealed in 2003 based on the WHI randomized controlled trial, there has been a 7% decrease in the incidence rate of breast cancer in America. [ 25 ]

3. Breast cancer in women: Diagnosed through appended technologies

Breast tumors typically start as benign tumors or even metastatic carcinomas due to ductal hyperproliferation, which is then constantly stimulated by various carcinogenic factors. Breast cancer is initiated and progresses differently depending on the microenvironment of the tumor, such as stromal influences or macrophages. When only the stroma of the rat mammary gland was exposed to carcinogens—not the extracellular matrix or the epithelium—neoplasms could be induced. A mutagenic inflammatory microenvironment that macrophages can create can encourage angiogenesis and help cancer cells avoid immune rejection. Different DNA methylation patterns between the typical and tumor-associated microenvironments have been observed, suggesting that epigenetic changes in the tumor microenvironment can encourage carcinogenesis. Cancer stem cells (CSCs), a new subclass of malignant cells within tumors, have recently been identified and linked to tumor initiation, escape, and recurrence. This small population of cells, which may originate from stem cells or progenitor cells in healthy tissues, can regenerate itself and is resistant to traditional treatments like chemotherapy and radiotherapy. Ai Hajj was the first to identify breast cancer stem cells (bCSCs), and immunocompromised mice could develop new tumors from as few as 100 bCSCs. As opposed to basal stem cells, luminal epithelial progenitors are more likely to be the source of bCSCs. The self-renewal, proliferation, and invasion of bCSCs are mediated by signaling pathways that include Wnt, Notch, Hedgehog, p53, PI3K, and HIF. More research is nevertheless required to comprehend bCSCs and create fresh methods for their complete eradication. [ 19 ]

The CSC theory and the stochastic theory are 2 speculative theories for how breast cancer starts and spreads. According to the theory about CSCs, all subtypes of tumors are descended from the same stem cells or transit-amplifying cells. A variety of tumor phenotypes are caused by acquired genetic and epigenetic mutations in stem cells or progenitor cells. According to the stochastic theory, a single type of cell is the source of all tumor subtypes. Any breast cell can gradually develop random mutations, and when enough mutations have accumulated, the breast cell can transform into a tumor cell. Even though both theories have a lot of data to back them up, neither can fully explain how human breast cancer first developed. [ 26 ]

4. Biology-based breast cancer prevention

To enhance the quality of life for breast cancer patients, biological prevention, primarily known as monoclonal antibodies for the disease, has recently been developed. These monoclonal antibodies have human epidermal growth factor receptor 2 (HER2) as one of their primary targets. The HER2 protein is overexpressed or the HER2 gene is amplified in about 20% to 30% of all breast cancer cases. The first HER2-targeted medication to receive FDA approval is trastuzumab (Herceptin), a recombinant humanized monoclonal antibody. It can directly interact with the C-terminal region of domain IV in the extracellular region of HER2. Trastuzumab’s anti-tumor mechanism has not yet been fully understood. Trastuzumab may inhibit the growth and proliferation of cancer cells through several possible mechanisms, including activating the immune system against cancer cells through an effect known as antibody-dependent cell-mediated cytotoxicity, inhibiting the MAPK and PI3K/Akt pathways, and enlisting ubiquitin to internalize and degrade HER2. With an objective response rate of 26%, trastuzumab was initially used to treat metastatic breast cancer. Trastuzumab interacts favorably with other anti-tumor medications, including nimotuzumab, carboplatin, 4-hydroxycyclophosphamide, docetaxel, and vinorelbine, according to in vitro studies. According to the HERA and TRAIN trials, chemotherapy given in combination with adjuvant trastuzumab for a year can prolong disease-free survival in HER2+ breast cancer patients (HR = 0.76). Trastuzumab plus docetaxel was shown to be more effective than docetaxel alone in treating HER2-positive metastatic breast cancer, with an objective response rate of 50% versus 32%, in a randomized phase II trial carried out by Marty. Patients receiving trastuzumab, however, also experienced adverse effects like congestive heart failure and a decline in their left ventricular ejection fraction. [ 4 ]

5. Breast cancer in women is diagnosed

5.1. mammography.

Diagnostic mammography is an x-ray that creates an image of the breast using low radiation doses. It is used to follow up on unexpected findings from a clinical breast exam or a screening mammogram. It is also possible to use mammography during a biopsy to identify an abnormal area. [ 27 ]

5.2. Ultrasound

An ultrasound creates images of various body parts using high-frequency sound waves. It is used to determine whether a lump in the breast is a solid tumor or a cyst. Additionally, ultrasound can be used by medical professionals to direct them to the biopsy site. An ultrasound may be performed on women with advanced breast cancer to determine whether liver metastasis has occurred. [ 28 ]

5.3. Biopsy

Breast cancer can only be accurately identified through a biopsy. The purpose of a biopsy is to remove tissues or cells from the patient’s body for laboratory testing. The pathologist’s report will determine whether or not cancer cells were discovered in the sample. The type of biopsy performed will depend on whether the lump is palpable, meaning you can feel it, or non-palpable, meaning you can’t. To locate the area to be tested, the doctor may use ultrasound or mammography. The majority of biopsies are performed in a hospital, and once they are complete, you can leave for home. [ 29 ]

5.4. The core biopsy

Removes tissue from the body using a unique hollow needle. It is employed by doctors to obtain a sample from a breast region that is thought to be suspicious. During the procedure, they might take several samples from the area. To remove more tissue through the hollow needle, doctors occasionally use a special vacuum. Vacuum-assisted core biopsy is the name of this method. [ 17 ]

5.5. A lymph node biopsy

A lymph node biopsy is a surgical procedure that involves the removal of lymph nodes so they can be examined under a microscope to determine if they contain cancer. Breast cancer cells can separate from the tumor and move through the lymphatic system. Lymph nodes beneath the arm are where they might spread first. To help determine the stage of breast cancer, doctors count the number of lymph nodes that contain the disease. [ 30 ]

5.6. Fine needle aspiration

Removes a small amount of tissue from a lump using a syringe and a very thin needle. It helps doctors determine whether a lump is a cyst or a solid tumor. Whether a cancer is non-invasive or invasive cannot be determined by fine needle aspiration (FNA). [ 31 ] During the procedure, a healthcare professional inserts a thin needle into the breast lump, guided by palpation or imaging techniques such as ultrasound. A syringe attached to the needle is used to suction out cells or fluid from the lump. These cells or fluid samples are then examined under a microscope by a pathologist to determine if they are cancerous (malignant) or noncancerous (benign). FNA is a minimally invasive procedure that can provide valuable information about the nature of the breast lump. It helps in the diagnosis of breast cancer by analyzing the characteristics of the cells, aiding in determining the presence of cancerous cells, and guiding further diagnostic or treatment procedures. However, depending on the situation, additional tests like a core needle biopsy or surgical biopsy may be recommended for a more comprehensive evaluation.

6. Preventative measures and ongoing research efforts

Certainly, preventive measures and ongoing research efforts are crucial components in the fight against breast cancer.

7. Preventative measures

Encouraging women to undergo regular mammograms and screenings based on age and risk factors can aid in early detection, leading to better treatment outcomes. [ 32 ] Promoting a healthy lifestyle that includes maintaining a balanced diet, regular exercise, limiting alcohol consumption, avoiding smoking, and maintaining a healthy weight can reduce the risk of developing breast cancer. Encouraging breastfeeding, which has been shown to have protective effects against breast cancer, can be promoted as a preventive measure. [ 33 ] Providing comprehensive and accessible education on breast cancer risks, symptoms, and the importance of early detection can empower individuals to take proactive measures and seek timely medical attention. For individuals with a family history or known genetic mutations (like BRCA1 or BRCA2), genetic counseling and testing can help in assessing risks and making informed decisions about preventive measures. [ 34 ] Understanding the risks associated with certain hormone therapies and discussing alternatives with healthcare providers, particularly for menopausal symptoms, is important.

8. Ongoing research efforts

Research continues to develop targeted therapies that focus on specific genetic mutations or molecular markers associated with breast cancer, improving treatment efficacy while reducing side effects. [ 35 ] Investigating the role of immunotherapy in breast cancer treatment, harnessing the body’s immune system to target cancer cells, is an area of active research. Ongoing research aims to develop more sensitive and specific screening methods beyond mammography, including molecular imaging and blood-based biomarkers, for earlier and more accurate diagnosis. Studying genetic and epigenetic factors influencing breast cancer development helps in identifying new targets for therapy and understanding individual susceptibility. [ 36 ] Research focuses on identifying additional lifestyle modifications, medications, or interventions that can further reduce the risk of developing breast cancer. Collaborative research initiatives between countries and institutions aim to share data, resources, and expertise, advancing our understanding of breast cancer and improving treatment outcomes globally. By continuing to prioritize prevention through lifestyle modifications, early detection through effective screening, and investing in cutting-edge research, the hope is to reduce the incidence, morbidity, and mortality associated with breast cancer worldwide.

9. Conclusion

Breast cancer remains a significant global health concern, impacting millions of individuals each year. This review has underscored the multifaceted nature of breast cancer, highlighting various risk factors and diagnostic approaches crucial in understanding and managing this disease. Moreover, advancements in diagnostic techniques have significantly improved early detection and treatment outcomes. Mammography, alongside emerging technologies like magnetic resonance imaging and molecular testing, plays a pivotal role in identifying breast cancer at its early stages, enabling prompt intervention and potentially improving patient prognoses. Moving forward, continued research into identifying additional risk factors, enhancing screening methods, and developing targeted therapies remains imperative. Furthermore, promoting awareness, advocating for increased screening accessibility, and fostering global collaboration among medical professionals and researchers is crucial in the ongoing fight against breast cancer. A comprehensive approach that integrates research, education, early detection, and accessible healthcare services is essential in combating breast cancer and reducing its impact on individuals and societies worldwide.

Author contributions

Conceptualization: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Methodology: Emmanuel Ifeanyi Obeagu.

Resources: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Supervision: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Visualization: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Writing – original draft: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Writing – review & editing: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Validation: Getrude Uzoma Obeagu.

Abbreviations:

• Cited Here |
• Google Scholar
• PubMed | CrossRef |
• View Full Text | PubMed |
• View Full Text | PubMed | CrossRef |

breast cancer; cancers; diagnosis; mortality; prevention; risk factors

• + Favorites
• View in Gallery

Readers Of this Article Also Read

The impact of bmi on breast cancer – an updated systematic review and..., nomophobia, loneliness and depressive symptom levels of adults living in a..., glucocorticoid therapy and the risk of infection in patients with newly..., severe measles infection: the spectrum of disease in 36 critically ill adult..., impact of cryoglobulinemic syndrome on the outcome of chronic hepatitis c virus ....

REVIEW article

A contemporary review of breast cancer risk factors and the role of artificial intelligence.

• 1 Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
• 2 Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile

Background: Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics.

Methods: A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses.

Results: Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies.

Conclusion: The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

1 Introduction

Over the past decade, breast cancer has remained a leading cause of mortality among women globally, driving an intensive search for effective prevention and early detection strategies. During 2020, more than 2.3 million women were diagnosed, of which 33.5% died ( 1 ). Despite significant advances in understanding biological mechanisms and risk factors of breast cancer, substantial challenges persist in the personalized clinical management and preventive intervention. This work aims to evaluate and synthesize the evidence available on breast cancer risk factors, ranging from genetic predispositions and lifestyle to environmental influences, with a particular interest in recent technological advancements, including AI, in predicting and detecting the disease. We pose two critical research questions: 1) What are the main risk factors associated with the development of breast cancer, and how do these vary among different populations and age groups? 2) How do recent technological advancements based on Artificial Intelligence (AI) help the detection and prevention of breast cancer? Guided by the hypothesis that the variability in breast cancer risk factors among different populations suggests that prevention and early detection strategies must be personalized, considering genetic, lifestyle, and environmental factors to be effective, this review seeks to identify areas of consensus and discrepancy in the scientific literature. Highlighting the need for personalized strategies that consider variability among populations and age groups, we aim to provide clear recommendations that guide future research and clinical practices towards more effective prevention and early detection of breast cancer.

The paper is organized as follows. In Section 2, the methodology for selecting and reviewing papers is described. Section 3 shows the results with particularly emphasis to the bibliometric study and risk factor categories. A discussion and some conclusions are in Sections 5 and 6, respectively.

2 Methodology

The methodology of the paper involved a comprehensive bibliographic development and analysis, which steps are described in Figure 1 .

Figure 1 Flow chart of the methodology.

2.1 Literature search and eligibility criteria

Our review concentrated on studies published between 2020 and 2024, with a focus on breast cancer risk factors. We sourced these from databases like PubMed, Scopus, and Web of Science. We included research papers that provided insights into demographic, genetic, lifestyle, and environmental influences on breast cancer risk, alongside studies utilizing AI for enhancing risk prediction and classification. Exclusion criteria were set for articles published prior to 2020 and those not directly examining the outlined risk factors. English language has been mainly used for the selection.

2.2 Study selection and data extraction

The study selection process meticulously filtered approximately 250 article by titles, abstracts and keywords, to determine their relevance to breast cancer risk factors and AI applications. A deeper process based on a complete reading of the papers narrowed the focus to 112 articles that met our inclusion criteria and offered important information on the topic. This approach ensured that only the most relevant studies were included, providing a detailed exploration of breast cancer risk factors and the role of AI in risk management. A bibliometric analysis was realized for setting frequencies and relationships among risk factors. Finally, these risk factors were systematically classified into categories, as detailed in Table 1 .

Table 1 Keywords and descriptions for breast cancer risk factors and AI research.

2.3 Analysis and classification

This classification was based on the analysis of risk factors available in various articles, which were then grouped according to characteristics to derive the respective classifications. Regarding risk factors, they were classified into groups corresponding to “Demographic and Genetic Factors”, “Reproductive and Hormonal Factors”, “Metabolic Factors”, “Medical History” and “Lifestyle and Environmental Factors.” Additionally, a new independent category was created to group papers that include studies with artificial intelligence models, named “Use of AI in Risk Prediction”. A simple Natural Language Processing (NLP) word count was used to identify the risk factors most frequently mentioned in each paper.

2.4 Documentation and conclusion

This methodology involved the following steps: conducting an exhaustive literature search across major scientific databases; applying inclusion and exclusion criteria, and to narrow down the selection from approximately 250 papers to 112 most relevant papers; employing techniques for a more deep analysis of the risk factors mentioned across the selected papers and categorizing the identified risk factors into specific groups for a structured analysis. This methodology not only ensures a comprehensive understanding of the existing research landscape but also supports the identification of key risk factors for breast cancer, facilitating a more precise and evidence-based analysis.

By applying the above methodology, we show the results of the a systematic literature review of the selected 112 papers and we describe the main findings for each category of risk according to Table 2 .

Table 2 Summary of risk factors and characteristics in breast cancer research literature.

3.1 Bibliometric analysis

In this section we provide a bibliometric analysis using the Bibliometrix package of R software ( 114 ).

In order to facilitate a deeper understanding of how keywords interconnect across the collection of reviewed papers, a keyword network graph is shown in Figure 2 . The graph highlights the thematic ties and focal points within the research landscape under examination. In the Figure 2 we can see the most interconnected and frequent keywords are: female, breast tumor, breast cancer and breast neoplasms.

Figure 2 Keyword network visualization in breast cancer research.

Figure 3 displays the distribution of bibliographic authors by country. In this chart, ‘MCP’ represents Multiple Country Publications, indicating research papers co-authored by individuals from various nations, while ‘SCP’ signifies Single Country Publications, denoting research executed solely by authors within the same country. This visual representation clearly indicates that the United States is at the forefront in terms of the volume of scientific publications, with significant contributions in both national (SCP) and international (MCP) collaborations, followed by China, evidencing a robust level of scientific output and cooperative engagement in these nations.

Figure 3 Distribution of countries of bibliographic authors.

Conversely, the author network depicted in Figure 4 illustrates clustering among authors who have contributed to more than five publications. Those with a higher publication frequency are represented by larger circles, visually highlighting the most prolific contributors within the network.

Figure 4 Co-authorship network analysis in scientific research.

3.2 Breast cancer risk factors

In this Section, we provide a detailed analysis of breast cancer risk factors identified by the reviewed works as represented in Table 2 .

3.3 Demographic and genetic factors

● Age: Age plays a crucial role in breast cancer incidence and outcomes, particularly impacting middle-aged and older women. Studies like ( 53 ) and ( 33 ) investigate treatment efficacy and risk factors, especially in younger women. Demographic factors, including age, are highlighted by ( 67 ) and ( 110 ). Mortality rates, notably rising in women under 50 and over 70, are observed by ( 65 ), underscoring age’s significance. Associations between reproductive history and breast cancer subtypes in women aged ≤50 are explored by ( 24 ) ( 42 ). focuses on mammographic density’s relation to risk in women aged 40 to 74. Lastly ( 46 ), emphasizes age-specific preventive measures for women aged 30–39.

● Race or ethnicity and geographic location: Research underscores significant variations in breast cancer predisposition across ethnicities and geographic locations, influenced by genetic, environmental, and socioeconomic factors. Studies like ( 112 ) emphasize diverse risk prediction models’ necessity, especially for Asian women. Disparities persist despite similar treatments, as shown by ( 4 ) among Black and White women. Meanwhile ( 12 ), and ( 18 ) identify genetic susceptibility in Egyptian and Arab populations. Geographical variations, highlighted by ( 29 ), highlight the need to adopt personalized approaches. These findings emphasize the multifaceted nature of breast cancer risk and treatment strategies across diverse populations.

● Family History: The presence of a family history significantly impacts the assessment and management of breast cancer risk ( 110 ). reveals that 35.5% of women with a familial history face a high lifetime risk, yet only 23.9% receive enhanced screening ( 13 ). demonstrates the effectiveness of machine learning, achieving 77.78% precision in risk prediction. In addition ( 77 ), identifies specific germline variants linked to susceptibility. Furthermore, the integration of polygenic risk scores with family history, as demonstrated by ( 91 ), significantly alters surveillance recommendations. Overall, these findings underscore the crucial role of family history in personalized breast cancer care and risk management.

● Genetic mutations, such as BRCA1 (Breast Cancer Gene 1) and BRCA2 (Breast Cancer Gene 2): Genetic mutations, particularly in BRCA1 and BRCA2 genes, significantly increase hereditary breast cancer risk. Studies like ( 92 ) analyze the role of germline CHEK2 (Checkpoint Kinase 2) variants, while ( 97 ) advocate personalized prevention strategies ( 98 ). identifies genetic loci associated with contralateral breast cancer risk, and ( 3 ) explores molecular links between obesity and breast cancer. These findings emphasize the multifactorial nature of breast cancer, requiring tailored risk assessment and management.

● Economic factors: Economic factors significantly impact breast cancer risk and outcomes ( 86 ). reveals disparities in access to systemic anticancer therapies based on geographic and sociodemographic factors. Similarly ( 36 ), notes a social gradient in cancer incidence in Costa Rica ( 51 ). links higher education levels to increased breast cancer risk ( 2 ). emphasizes local demographic factors in TNBC (Triple-Negative Breast Cancer) treatment, while ( 32 ) highlights access disparities in Colombia. Finally ( 70 ), stresses the importance of socio-demographic indices and public health policies in addressing breast cancer burden in developing countries.

3.4 Reproductive and hormonal factors

● Menarche (age at first menstruation): Early menarche increases breast cancer risk due to prolonged hormonal exposure ( 26 ). links higher anti-Müllerian hormone levels to early menarche, indicating elevated risk. Conversely ( 72 ), suggests later menarche protects against certain breast cancer subtypes. Lifestyle changes, like plant-based diets, are crucial in mitigating risk, as emphasized by ( 49 ).

● Menopause (age at menopause): Late menopause increases breast cancer risk due to prolonged hormonal exposure ( 111 ). links menopausal hormonal changes to chemotherapy side effects severity. Conversely ( 20 ), emphasizes fat distribution’s role in postmenopausal breast cancer risk ( 26 ). associates lower anti-Müllerian hormone levels with earlier menopause, indicating elevated risk. Conversely ( 72 ), suggests later menopause as a risk factor for certain breast cancer subtypes. Lifestyle factors like higher BMI and caloric intake heighten post-menopausal breast cancer risks, as noted by ( 49 ).

● Breastfeeding and Parity (number of full-term pregnancies): Parity and breastfeeding reduce breast cancer risk ( 80 ). analyzes parity’s influence across birth cohorts, showing changing risk patterns ( 26 ). links anti-Müllerian hormone levels to age at menarche and parity, aiding risk assessment ( 64 ). studies parity’s impact on breast cancer incidence, highlighting rising rates in younger women ( 72 ). meta-analysis reveals subtype-specific risks, emphasizing tailored prevention strategies.

● Hormonal factors (use of hormone replacement therapy, contraceptives, etc.): Hormonal factors like hormone replacement therapy and contraceptives influence breast cancer risk ( 3 ). highlights obesity’s role in breast cancer, especially in postmenopausal women ( 10 ). emphasizes hormonal imbalances’ impact, urging further research ( 59 ). finds no significant difference in breast cancer risk with Hormone Replacement Therapy among BRCA mutation carriers. These findings emphasize the importance of hormonal markers like estrogen and progesterone receptors in breast cancer treatment ( 3 , 10 , 59 ). Additionally ( 21 ), and ( 72 ) explore lifestyle factors like diet and reproductive behaviors, highlighting hormonal influences on breast cancer risk.

3.5 Metabolic factors

● Diabetes: Elevated levels of insulin can promote cellular proliferation and reduce apoptosis, thus facilitating the development and progression of mammary neoplasms ( 3 ). elucidate obesity’s pivotal role in breast cancer (BC) risk, particularly postmenopausal women, citing hormonal imbalances and insulin resistance among its mechanisms. They reveal how obesity-driven molecular changes, like increased estrogen and insulin levels, contribute to BC via specific signaling pathways. Conversely ( 34 ), find a significant correlation between genetic predisposition to Type 2 Diabetes Mellitus (T2DM) and poorer breast cancer-specific survival (HR = 1.10, 95% CI = 1.04–1.18, P = 0.003), emphasizing the potential causal impact of T2DM on BC outcomes.

● Metabolism: Metabolic processes play a crucial role in modulating breast cancer risk, significantly influencing hormonal levels and cellular dynamics. Alterations in metabolism, including imbalances in lipid and glucose metabolism, can lead to endocrine changes and alterations in the cellular microenvironment that favor mammary carcinogenesis. Metabolism plays a crucial role in breast cancer risk, with various factors influencing susceptibility ( 113 ). found that high-density lipoprotein cholesterol (HDL-C) significantly affects breast cancer risk, suggesting a metabolic component to cancer development ( 9 ). identified associations between insulin-like growth factor 1 (IGF-1) levels and fasting blood glucose with breast cancer risk, emphasizing the complexity of metabolic factors. Additionally ( 13 ), integrated genetic mutations and demographic factors to predict breast cancer risk, highlighting the importance of considering metabolic pathways in risk assessment. These findings underscore the multifaceted nature of metabolism-related risk factors in breast cancer susceptibility ( 113 ) ( 9 ) and ( 13 ).

3.6 Medical history

● Breast density: Breast density complicates cancer detection in the sense that it can make more difficult for mammograms to identify cancerous tumors due to the tissue’s thickness or opaqueness. Additionally, high breast density is considered an independent risk factor for developing breast cancer. This is because denser breast tissue contains more connective and glandular tissues, which can potentially hide tumors and it is also associated with a higher likelihood of cancer development ( 11 ). found a sixfold risk difference between densest and least dense categories ( 42 ). investigated this relationship across a cohort of 21,150 women, confirming the effectiveness of automated density assessments in predicting breast cancer risk. Similarly ( 69 ) emphasizes higher risk in younger women with lower BMI ( 46 ). explores mammography-based risk assessment for early screening. These studies underscore the importance of considering mammographic density in breast cancer risk assessment and screening.

● Other cancers and diseases: The presence of other cancers may indicate heightened risk for breast cancer ( 107 ). developed prognostic nomograms for breast cancer patients with lung metastasis ( 66 ). addressed disparities in colorectal and breast cancer screenings ( 83 ). revealed screening rate disparities among females with schizophrenia ( 106 ). noted a slight increase in primary lung cancer risk post-radiotherapy for breast cancer.

3.7 Lifestyle factors

● Alcohol consumption: Alcohol consumption significantly increases breast cancer risk, even with moderate intake ( 85 ). revealed odds ratios between 1.82 to 5.67, indicating a notable association ( 40 ). highlighted a high prevalence (18.34%) of risky drinking among Australian women, exceeding weekly guidelines. These studies emphasize the importance of preventive measures. These findings underscore the link between alcohol intake and breast cancer risk, highlighting the need for preventive measures ( 35 , 51 ).

● Cigarette smoking: Cigarette smoking contributes to breast cancer risk, with global estimates from ( 41 ) showing it accounted for 5.1% of deaths and 5.2% of DALYs in 2019. They emphasize anti-tobacco policies, particularly in low SDI regions ( 80 ). found smoking’s heightened impact in younger Asian cohorts, highlighting the need for tailored prevention strategies.

● Obesity and Body Mass Index (BMI): Obesity, particularly postmenopause, significantly increases breast cancer risk, impacting hormonal levels and inflammation. Studies like ( 3 ) highlight obesity’s role in altering molecular pathways, while ( 102 ) emphasize its association with higher estrogen levels, especially in postmenopausal women ( 19 ). stresses lifestyle interventions for reducing breast cancer risk in obese postmenopausal women. Additionally ( 71 ), found BMI significantly influences breast cancer prognosis, particularly in premenopausal women with specific cancer subtypes.

● Poor nutrition: Poor nutrition, characterized by diets high in fats and sugars, increases breast cancer risk. Studies like ( 103 ) highlight the positive impact of tailored lifestyle interventions, while ( 16 ) suggest higher plasma vitamin D levels may offer protection ( 21 ). and ( 49 ) emphasize the association between Western diets and increased risk, contrasting with the protective effect of plant-based diets. Additionally ( 62 ), and ( 94 ) address dietary misconceptions and socio-demographic factors influencing nutritional risk, advocating for comprehensive approaches in breast cancer care.

● Physical inactivity: Physical inactivity increases breast cancer risk, while exercise helps regulate hormones and maintain a healthy weight. Studies like ( 19 ) emphasize its benefits in reducing recurrence risk. Tailored interventions, as shown by ( 103 ), positively impact survivors’ quality of life ( 49 ). link low physical activity to higher risk, especially in post-menopausal women. Additionally ( 91 ), propose personalized surveillance integrating lifestyle factors for better outcomes.

● Stress, anxiety, or depression: Chronic stress may impact breast cancer risk ( 57 ). links stress, anxiety, and depression to reduced quality of life in survivors ( 103 ). shows positive outcomes in QoL (Quality of Life) indicators with home-based interventions despite pandemic challenges.

3.8 Environmental factors

● Exposure to radiation: Exposure to ionizing radiation, like from radiotherapy, elevates breast cancer risk, especially when received at a young age. Studies explore various factors ( 38 ): concluded that exposure to chest radiation therapy significantly elevates breast cancer risk, with individuals who have undergone such treatments facing a notably higher likelihood of developing the disease. Similarly ( 57 ), mention that receiving chest radiation therapy was significantly associated with a higher risk of breast cancer, with an Adjusted Odds Ratio (AOR) of 6.43, indicating a more than sixfold increase in risk compared to those who had not received such therapy ( 98 ). found that genetic variations can influence an individual’s susceptibility to radiation toxicity ( 106 ). discusses lung cancer risk post-radiotherapy ( 111 ); links menopause to chemotherapy side effects; and ( 22 ) reported a high radiodermatitis incidence (98.2%) in breast cancer patients undergoing radiotherapy, with BMI and statin use affecting severity, and hydrogel showing protective effects.

● Exposure to chemicals: Chemicals like endocrine disruptors may disrupt hormonal balance, potentially contributing to breast cancer ( 105 ). evaluates CDK4/6 inhibitors’ toxicity in metastatic breast cancer, stressing personalized treatment strategies due to varying drug profiles.

● Environmental pollutants, specific exposures and heavy metals: Environmental pollutants, including heavy metals and air pollution, contribute to breast cancer risk ( 6 ). found altered levels of metals like copper and cadmium in breast cancer patients ( 96 ). investigated air pollution’s association with postmenopausal breast cancer risk, finding a significant 18% risk increase with a 10 µg/m3 rise in PM10 levels in 2007.

4 The role of artificial intelligence models for detecting breast cancer

The integration of artificial intelligence (AI) in breast cancer management spans various aspects, including diagnosis, recurrence prediction, survival rate estimation, and treatment response assessment. Studies like ( 5 ) demonstrate the effectiveness of machine learning models, achieving 80.23% accuracy in diagnosing early-stage breast cancer. Key risk factors identified for breast cancer included levels of glucose, age, and resistin. This approach demonstrates the potential of machine learning in enhancing breast cancer diagnostic processes by effectively selecting critical risk factors. Similarly ( 8 ), utilizes NLP and machine learning to predict breast cancer recurrence, emphasizing the efficacy of the OneR algorithm. The main clinical data used in the paper for predicting breast cancer recurrence involve a wide range of factors extracted from electronic health records (EHR). These include diagnostic symptoms, medications, lab results, medical recommendations, past medical history, procedures, family history, imaging, endoscopic assessments, anesthesia types, allergies, and other clinical documents. NLP algorithms were developed to extract these key features from the medical records. Notably ( 81 ), highlights Support Vector Machine (SVM) as the most accurate algorithm for breast cancer prediction, achieving an accuracy of 97.2%. The characteristics of the cell nuclei present in the images, are used as inputs for the SVM. They include, Radius, Texture, Area, Perimeter, Smoothness, Compactness, Concavity, Concave points, Symmetry, and Fractal dimension. These attributes are determined from the digitized images and serve as the basis for the SVM model to classify instances into benign or malignant categories.

For detection purposes, most of the papers use mammography images for training deep learning models, by assuming these algorithms are able to detect anomalies in the breast tissue. In this context, a comprehensive review is provided by ( 14 ) focusing on various ANN models such as Spiking Neural Network (SNN), Deep Belief Network (DBN), Convolutional Neural Network (CNN), Multilayer Neural Network (MLNN), Stacked Autoencoders (SAE), and Stacked De-noising Autoencoders (SDAE). The review highlights the effectiveness of these models in improving diagnosis accuracy, precision, recall, and other metrics, with particular success noted in models like ResNet-50 and ResNet-101 within the CNN algorithm framework. Instead, clinical data have been considered by ( 17 ) which developed a Machine Learning (ML) system for classifying breast cancer and diagnosing cancer metastases using clinical data extracted from Electronic Medical Records (EMRs). The best results have been obtained by a decision tree classifier which achieved 83% accuracy and an AUC (Area Under the Curve) of 0.87, demonstrating the potential of ML models based on blood profile data to aid professionals in identifying high-risk metastases breast cancer patients, thereby improving survival outcomes.

Regarding treatment response assessment ( 28 ), employs CNNs to predict treatment response in breast cancer patients undergoing chemotherapy, achieving high accuracies for various parameters. The study integrates both imaging and non-imaging data for the inputs of the models included longitudinal multiparametric MRI data (dynamic-contrast-enhanced MRI and T2-weighted MRI), demographics, and molecular subtypes. The use of advanced imaging techniques alongside clinical and molecular data indicates the need for a personalized treatment planning and assessment in breast cancer care ( 73 ). demonstrates deep learning’s superior performance in risk identification compared to traditional Machine Learning (ML) methods. Important inputs for their models include age, resistin levels, global burden of disease (GBD) relative risk upper values, glucose, adiponectin, high BMI (binary), MCP-1, leptin, relative risks from meta-analyses, obesity (binary), and insulin levels. These inputs were selected based on their relevance and low redundancy for predicting breast cancer, highlighting the potential of deep learning to complement traditional screening methods by identifying individuals at risk non-invasively and affordably. In survival rate prediction ( 63 ), evaluates ML’s role, highlighting challenges like data preprocessing and model validation. review 31 studies, mainly from Asia, to predict the 5-year survival rate of breast cancer. It is highlighted that among the papers reviewed, the most used algorithms are decision trees (61.3%), artificial neural networks (58.1%) and support vector machines (51.6%), where clinical and molecular information was used to build predictive models ( 73 ). used a database of 116 women, of which 52 were healthy and 64 had been diagnosed with breast cancer. The information included demographic and anthropometric data. The application of Deep Learning was considered the best evaluated method for breast cancer prediction, among algorithms such as SVM, Neural Networks, Logistic Regression, XGBoost, Random Forest, Naive Bayes and Stochastic Gradient. Lastly, studies like ( 88 ) predict patient satisfaction post-mastectomy, revealing that 45.2% of women experienced improved satisfaction with their breasts. These findings underscore the potential of AI in enhancing various aspects of breast cancer management, from diagnosis to patient satisfaction assessment. A novel approach that integrates Machine Learning (ML) algorithms with Explainable Artificial Intelligence (XAI) has been recently developed to enhance the understanding and interpretation of predictions made by ML models. In the context of breast cancer research ( 95 ), introduced a Hybrid Algorithm combining ML and XAI techniques aimed at preventing breast cancer. This innovative methodology enables the identification and extraction of key risk factors, such as high-fat diets and breastfeeding habits, to accurately differentiate between patients with and without breast cancer among Indonesian women. Risk indicators, such as auxiliary nodes and breast density, can also be extracted by the images by using deep learning ( 7 , 56 , 84 ).

5 Discussion

Upon reviewing multiple studies on breast cancer and its associated risk factors, several key findings emerge.

● Demographic and genetic factors play a crucial role in influencing breast cancer risk. This review highlights the crucial impact of age, with a notable increase in breast cancer incidence and outcomes, particularly affecting middle-aged and older women, as well as younger demographics in certain contexts. The significance of race, ethnicity, and geographic location is underscored, emphasizing the variability in breast cancer predisposition across different populations due to a mix of genetic, environmental, and socioeconomic factors. Family history and specific genetic mutations, such as BRCA1 and BRCA2, are identified as key risk determinants, necessitating personalized prevention and management strategies. Economic factors also emerge as crucial, with disparities in access to care and outcomes spotlighted. Collectively, these findings underscore the necessity for tailored breast cancer prevention and treatment approaches that consider the intricate interplay of demographic and genetic factors.

● Early menarche, late menopause, parity, breastfeeding, and hormonal therapies like hormone replacement therapy and contraceptives highly influence breast cancer risk. These factors are intricately linked with hormonal exposure over a woman’s lifetime, affecting her breast cancer susceptibility. This review emphasizes the need for awareness and consideration of these factors in breast cancer risk assessment, suggesting lifestyle modifications and preventive strategies tailored to individual reproductive histories and hormonal exposure profiles.

● The relationship between metabolic factors, such as diabetes and overall metabolism, play an important role in the context of breast cancer risk. In particular, conditions like insulin resistance and alterations in lipid and glucose metabolism can influence breast cancer development by affecting hormonal levels and cellular processes. Our review suggests that understanding the impact of these metabolic factors is crucial for developing targeted prevention strategies and emphasizes the need for further research to explore the intricate connections between metabolic health and breast cancer risk.

● Medical history, specifically breast density and the history of other cancers, can influence breast cancer risk. In particular, dense breast tissue can obscure mammograms, making detection more challenging, and emphasizes the independent risk factor that high breast density presents. Additionally, the history of other cancers may indicate an elevated risk for breast cancer. This work underscores the importance of considering an individual’s medical history in breast cancer risk assessments and the need for personalized screening strategies.

● Lifestyle factors such as alcohol consumption, cigarette smoking, obesity, poor nutrition, and physical inactivity, highlight their significant roles in increasing breast cancer risk and the necessity of addressing these modifiable risk factors through public health interventions and individual lifestyle changes to reduce breast cancer incidence. This review underscores the potential of preventive measures and lifestyle modifications in mitigating breast cancer risk, emphasizing the importance of holistic approaches in breast cancer prevention strategies.

● Environmental factors like radiation exposure, chemicals, and pollutants, play a significant role in breast cancer risk. The cited works emphasize the need for awareness and protective measures against these exposures. Highlighting the complexity of breast cancer etiology, our work calls for comprehensive research to better understand the interactions between environmental factors and genetic predisposition, and for public health strategies to minimize exposure and mitigate breast cancer risk.

● The description of role of artificial intelligence (AI) models in detecting breast cancer illustrates the significant potential AI has in enhancing diagnostic accuracy, predicting recurrence, estimating survival rates, and assessing treatment response. Highlighting various studies, this review shows that machine learning algorithms, such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNNs), have achieved notable success. This discussion emphasizes AI’s transformative impact on breast cancer management, advocating for further research and integration of AI technologies to tailor detection and treatment approaches, ultimately improving patient outcomes.

A detailed description of the results of each work will be presented in Section 3.2. This analysis advocates for a multifaceted approach to prevention, screening, and treatment, reflecting the complex nature of breast cancer risk factors.

6 Conclusion

Our research reveals a breakthrough in early detection of breast cancer with machine learning models demonstrating an impressive diagnostic accuracy of 80.23%. The bibliographic review and analysis of the last 5 years in this field allowed us to identify the transformative impact of AI both in the identification of risk factors and in the improvement of diagnostic accuracy. Our analysis, unlike previous studies such as those by ( 69 ) ( 89 ), and ( 35 ), goes beyond updating risk factor inventories to show the fundamental role of sophisticated risk algorithms. AI. These tools, particularly SVM, have achieved an accuracy rate of up to 97.2% in locating breast cancer, which is a significant leap over traditional diagnostic methods by using a wider range of datasets, including images and clinical details including risk factors for your diagnosis.

Future explorations should delve into AI’s ability to tailor breast cancer detection and treatments, thereby improving patient-specific outcomes.

Author contributions

ON: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Conceptualization. DA: Writing – original draft, Software, Investigation, Formal analysis, Data curation, Conceptualization. CT: Writing – review & editing, Project administration, Funding acquisition.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the ANID FONDAP 152220002 (CECAN).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2024.1356014/full#supplementary-material

1. World Health Organization. Breast cancer . Available online at: https://www.who.int/news-room/fact-sheets/detail/breast-cancer (Accessed 2024-01-03).

Google Scholar

2. Acevedo F, Walbaum B, Medina L, Merino T, Camus M, Puschel K, et al. Clinical characteristics, risk factors, and outcomes in Chilean triple negative breast cancer patients: a real-world study. Breast Cancer Res Treat . (2023) 197:449–59. doi: 10.1007/s10549-022-06814-x

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Ajabnoor GM. The molecular and genetic interactions between obesity and breast cancer risk. Medicina . (2023) 59:1338. doi: 10.3390/medicina59071338

4. Albain KS, Gray RJ, Makower DF, Faghih A, Hayes DF, Geyer CE Jr., et al. Race, ethnicity, and clinical outcomes in hormone receptor-positive, her2-negative, node-negative breast cancer in the randomized tailorx trial. JNCI: J Natl Cancer Institute . (2021) 113:390–9. doi: 10.1093/jnci/djaa148

CrossRef Full Text | Google Scholar

5. Alfian G, Syafrudin M, Fahrurrozi I, Fitriyani NL, Atmaji FTD, Widodo T, et al. Predicting breast cancer from risk factors using svm and extra-trees-based feature selection method. Computers . (2022) 11:136. doi: 10.3390/computers11090136

6. Ali AS, Nazar ME, Mustafa RM, Hussein S, Qurbani K, Ahmed SK. Impact of heavy metals on breast cancer. World Acad Sci J . (2024) 6:1–12. doi: 10.3892/wasj

7. Almansour NM. Triple-negative breast cancer: a brief review about epidemiology, risk factors, signaling pathways, treatment and role of artificial intelligence. Front Mol Biosci . (2022) 9:836417. doi: 10.3389/fmolb.2022.836417

8. Alzu’bi A, Najadat H, Doulat W, Al-Shari O, Zhou L. Predicting the recurrence of breast cancer using machine learning algorithms. Multimedia Tools Appl . (2021) 80:13787–800. doi: 10.1007/s11042-020-10448-w

9. Arafat HM, Omar J, Shafii N, Naser IA, Al Laham NA, Muhamad R, et al. The association between the serum level of igf-1 and igfbp-3 and the risk of breast cancer among women in the gaza strip. Asian Pacific J Cancer prevention: APJCP . (2023) 24:717. doi: 10.31557/APJCP.2023.24.2.717

10. Arceo-Martínez MT, López-Meza JE, Ochoa-Zarzosa A, Palomera-Sanchez Z. Estado actual del cáncer de mama en méxico: principales tipos y factores de riesgo. Gaceta mexicana oncología . (2021) 20:101–10. doi: 10.24875/j.gamo.21000134

11. Atakpa EC, Buist DS, Aiello Bowles EJ, Cuzick J, Brentnall AR. Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study. Breast Cancer Res . (2023) 25:147. doi: 10.1186/s13058-023-01744-y

12. Azim HA, Loutfy SA, Azim HA Jr., Kamal NS, Abdel Fattah NF, Elberry MH, et al. The landscape of brca mutations among Egyptian women with breast cancer. Oncol Ther . (2023) 11:445–59. doi: 10.1007/s40487-023-00240-9

13. Behravan H, Hartikainen JM, Tengström M, Kosma VM, Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep . (2020) 10:11044. doi: 10.1038/s41598-020-66907-9

14. Bharati S, Podder P, Mondal M. Artificial neural network based breast cancer screening: a comprehensive review. arXiv preprint arXiv:2006.01767 . (2020) 12:125–37. doi: 10.48550/arXiv.2006.01767

15. Bhatt R, van den Hout A, Antoniou AC, Shah M, Ficorella L, Steggall E, et al. Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors. Cancer . (2024) 2–10. doi: 10.1002/cncr.35183

16. Bissan AD, Ly M, Amegonou AEH, Sidibe FM, Koné BS, Barry NOK, et al. Plasma 25-hydroxyvitamin d and 1, 25-dihydroxyvitamin d levels in breast cancer risk in Mali: A case–control study. Diagnostics . (2023) 13:3664. doi: 10.3390/diagnostics13243664

17. Botlagunta M, Botlagunta MD, Myneni MB, Lakshmi D, Nayyar A, Gullapalli JS, et al. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Sci Rep . (2023) 13:485. doi: 10.1038/s41598-023-27548-w

18. Bu R, Siraj AK, Al-Rasheed M, Iqbal K, Azam S, Qadri Z, et al. Identification and characterization of atm founder mutation in brca-negative breast cancer patients of arab ethnicity. Sci Rep . (2023) 13:20924. doi: 10.1038/s41598-023-48231-0

19. Campbell NJ, Barton C, Cutress RI, Copson ER. Impact of obesity, lifestyle factors and health interventions on breast cancer survivors. Proc Nutr Soc . (2023) 82:47–57. doi: 10.1017/S0029665122002816

20. Cao Y, Xia B, Zhang Z, Hu D, Huang X, Yuan J, et al. Association of body fat distribution and risk of breast cancer in pre-and postmenopausal women. Obes Facts . (2023) 16:356–63. doi: 10.1159/000529834

21. Castelló A, Rodríguez-Barranco M, Lope V, Guevara M, Colorado-Yohar S, Dorronsoro A, et al. High adherence to western dietary pattern increases breast cancer risk (an epic-Spain study). Maturitas . (2024) 179:107868. doi: 10.1016/j.maturitas.2023.107868

22. Cavalcante LG, Domingues RAR, de Oliveira Junior B, Fernandes MAR, Pessoa EC, Abbade LPF. Incidence of radiodermatitis and factors associated with its severity in women with breast cancer: a cohort study. Anais Brasileiros Dermatologia . (2024) 99:57–65. doi: 10.1016/j.abd.2023.01.004

23. Cheung BHH, Man VCM, Sham GTW, Chow L, Co M, Kwong A. Pregnancy-related breast cancer: 14-year experience in a tertiary institution in hong kong. Cancer Treat Res Commun . (2024) 38:100783. doi: 10.1016/j.ctarc.2023.100783

24. Chitkara A, Mesa-Eguiagaray I, Wild SH, Hall PS, Cameron DA, Sims AH, et al. Reproductive history differs by molecular subtypes of breast cancer among women aged 50 years in scotland diagnosed 2009–2016: a cross-sectional study. Breast Cancer Res Treat . (2022) 196:379–87. doi: 10.1007/s10549-022-06721-1

25. Christakoudi S, Tsilidis KK, Dossus L, Rinaldi S, Weiderpass E, Antoniussen CS, et al. A body shape index (absi) is associated inversely with post-menopausal progesterone-receptor-negative breast cancer risk in a large european cohort. BMC Cancer . (2023) 23:1–12. doi: 10.1186/s12885-023-11056-1

26. Clendenen TV, Ge W, Koenig KL, Afanasyeva Y, Agnoli C, Bertone-Johnson E, et al. Breast cancer risk factors and circulating anti-müllerian hormone concentration in healthy premenopausal women. J Clin Endocrinol Metab . (2021) 106:e4542–53. doi: 10.1210/clinem/dgab461

27. Crispo A, Augustin LS, Luongo A, Calderaio C, Breda J, Coluccia S, et al. Central obesity, body mass index, metabolic syndrome and mortality in mediterranean breast cancer patients. Sci Rep . (2023) 13:21208. doi: 10.1038/s41598-023-45439-y

28. Dammu H, Ren T, Duong TQ. Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. PloS One . (2023) 18:e0280148. doi: 10.1371/journal.pone.0280148

29. Dehesh T, Fadaghi S, Seyedi M, Abolhadi E, Ilaghi M, Shams P, et al. The relation between obesity and breast cancer risk in women by considering menstruation status and geographical variations: A systematic review and meta-analysis. BMC Women’s Health . (2023) 23:392. doi: 10.1186/s12905-023-02543-5

30. DeRouen MC, Yang J, Li Y, Franke AA, Tome AN, White KK, et al. Circulating 27-hydroxycholesterol, lipids, and steroid hormones in breast cancer risk: a nested case–control study of the multiethnic cohort study. Breast Cancer Res . (2023) 25:95. doi: 10.1186/s13058-023-01693-6

31. Digkas E, Smith DR, Wennstig AK, Matikas A, Tegnelius E, Valachis A. Incidence and risk factors of hypothyroidism after treatment for early breast cancer: a population-based cohort study. Breast Cancer Res Treat . (2023) 204:79–87. doi: 10.21203/rs.3.rs-3112497/v1

32. Duarte C, Salazar A, Strasser-Weippl K, de Vries E, Wiesner C, Arango-Gutiérrez A, et al. Breast cancer in Colombia: a growing challenge for the healthcare system. Breast Cancer Res Treat . (2021) 186:15–24. doi: 10.1007/s10549-020-06091-6

33. Eren S, Arslan A, Çalişkan E, Akay E, Özhan N, Topuz Ö, et al. Comparison of clinical features and the impact of reproductive factors on by age at diagnosis young and elderly breast cancer patients in the middle anatolian region of Turkey. Eur Rev Med Pharmacol Sci . (2022) 26(7):2227–37. doi: 10.26355/eurrev_202204_28453

34. Escala-Garcia M, Morra A, Canisius S, Chang-Claude J, Kar S, Zheng W, et al. Breast cancer risk factors and their effects on survival: a mendelian randomisation study. BMC Med . (2020) 18:1–10. doi: 10.1186/s12916-020-01797-2

35. Fakhri N, Chad MA, Lahkim M, Houari A, Dehbi H, Belmouden A, et al. Risk factors for breast cancer in women: an update review. Med Oncol . (2022) 39:197. doi: 10.1007/s12032-022-01804-x

36. Fantin R, Ulloa CS, Solís CB. Social gradient in cancer incidence in Costa Rica: Findings from a national population-based cancer registry. Cancer Epidemiol . (2020) 68:101789. doi: 10.1016/j.canep.2020.101789

37. Fei F, Zhang K, Siegal GP, Wei S. A simplified breast cancer prognostic score: comparison with the ajcc clinical prognostic staging system. Modern Pathol . (2021) 34:2141–7. doi: 10.1038/s41379-021-00890-y

38. Fentie H, Ntenda PAM, Tiruneh FN. Dietary pattern and other factors of breast cancer among women: a case control study in northwest Ethiopia. BMC Cancer . (2023) 23:1050. doi: 10.1186/s12885-023-11501-1

39. Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, et al. Breast cancer statistics, 2022. CA: Cancer J Clin . (2022) 72:524–41. doi: 10.3322/caac.21754

40. Grigg J, Manning V, Cheetham A, Greenwood CJ, Youssef G, Lockie D, et al. Risky alcohol consumption among women in Australia attending breast screening services: an exploratory cross-sectional study. Addiction . (2023) 118:1493–506. doi: 10.1111/add.16191

41. Guo Q, Lu Y, Liu W, Lan G, Lan T. The global, regional, and national disease burden of breast cancer attributable to tobacco from 1990 to 2019: a global burden of disease study. BMC Public Health . (2024) 24:107. doi: 10.1186/s12889-023-17405-w

42. Habel LA, Alexeeff SE, Achacoso N, Arasu VA, Gastounioti A, Gerstley L, et al. Examination of fully automated mammographic density measures using libra and breast cancer risk in a cohort of 21,000 non-hispanic white women. Breast Cancer Res . (2023) 25:92. doi: 10.1186/s13058-023-01685-6

43. Hathaway CA, Rice MS, Collins LC, Chen D, Frank DA, Walker S, et al. Prolactin levels and breast cancer risk by tumor expression of prolactin-related markers. Breast Cancer Res . (2023) 25:24. doi: 10.1186/s13058-023-01618-3

44. Herrán OF, Álvarez DC, Quintero-Lesmes DC. Dietary patterns and breast cancer in Colombia: an ecological study. Int Health . (2020) 12:317–24. doi: 10.1093/inthealth/ihz085

45. Hickman SE, Baxter GC, Gilbert FJ. Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer . (2021) 125:15–22. doi: 10.1038/s41416-021-01333-w

46. Hindmarch S, Howell SJ, Usher-Smith JA, Gorman L, Evans DG, French DP. Feasibility and acceptability of offering breast cancer risk assessment to general population women aged 30–39 years: a mixed-methods study protocol. BMJ Open . (2024) 14:e078555. doi: 10.1136/bmjopen-2023-078555

47. Hirko KA, Rocque G, Reasor E, Taye A, Daly A, Cutress RI, et al. The impact of race and ethnicity in breast cancer—disparities and implications for precision oncology. BMC Med . (2022) 20:1–12. doi: 10.1186/s12916-022-02260-0

48. Hook C, Chatterjee U, Sheng H, Zhu Q, Robinson T, Roh JM, et al. A polygenic score associated with fracture risk in breast cancer patients treated with aromatase inhibitors. NPJ Breast Cancer . (2024) 10:9. doi: 10.1038/s41523-024-00615-9

49. Huang MC, Huang TT, Feng HC, Chen IC, Chang CI, Wang TN, et al. Lifestyle factors and energy intakes with risks of breast cancer among pre-and post-menopausal women in Taiwan. Nutrients . (2023) 15:3900. doi: 10.3390/nu15183900

50. Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN. Breast cancer prediction: a comparative study using machine learning techniques. SN Comput Sci . (2020) 1:1–14. doi: 10.1007/s42979-020-00305-w

51. Jiang R, Wang X, Sun Z, Wu S, Chen S, Cai H. Association of education level with the risk of female breast cancer: a prospective cohort study. BMC Women’s Health . (2023) 23:1–6. doi: 10.1186/s12905-023-02245-y

52. Jin J, Li J, Liu Y, Shi Q, Zhang B, Ji Y, et al. Thyroid hormone changes correlate to combined breast cancer with primary thyroid cancer. Breast Cancer: Targets Ther . (2024) 16:15–22. doi: 10.2147/BCTT.S442707

53. Ju J, Gao SL, Wang JY, Sang D, Kang YK, Wang X, et al. Prognostic factors and benefit populations of ovarian function suppression in premenopausal hr+/her2+ early-stage breast cancer patients who received trastuzumab: evidence from a real-world study with long-term follow-up. Thorac Cancer . (2024) 15:439–47. doi: 10.1111/1759-7714.15211

54. Kamburova ZB, Popovska SL, Kovacheva KS, Dimitrov DD, Nikolova SE. Genetic predisposition in female patients with triple-negative breast cancer. World Acad Sci J . (2024) 6:1–8. doi: 10.3892/wasj.2023.217

55. Kang E, Jung JJ, Lim C, Kim HK, Lee HB, Han W, et al. Increased risk of contralateral breast cancer for brca1/2 wild-type, high-risk korean breast cancer patients: a retrospective cohort study. Breast Cancer Res . (2024) 26:14. doi: 10.1186/s13058-024-01769-x

56. Kim G, Bahl M. Assessing risk of breast cancer: a review of risk prediction models. J Breast Imaging . (2021) 3:144–55. doi: 10.1093/jbi/wbab001

57. Kim SH, Jo HY. Factors associated with poor quality of life in breast cancer survivors: a 3-year follow-up study. Preprint . (2023) 15:2–12. doi: 10.20944/preprints202310.1934.v1

58. Kim N, Kim H, Park W, Cho WK, Kim TG, Im YH, et al. Impact of high dose radiotherapy for breast tumor in locoregionally uncontrolled stage iv breast cancer: a need for a risk-stratified approach. Radiat Oncol . (2023) 18:168. doi: 10.1186/s13014-023-02357-7

59. Kim HY, Park J, Moon SJ, Jeong S, Hong JH, Lee JK, et al. Short-term impact of hormone replacement therapy on risk of breast cancer in brca mutation carriers: A nationwide study in South Korea. Cancer Res Treat . (2024) 56:143. doi: 10.4143/crt.2023.653

60. Ko C, Brody JP. Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer. Hum Genomics . (2023) 17:1–8. doi: 10.1186/s40246-023-00482-8

61. Kyalwazi B, Yau C, Campbell MJ, Yoshimatsu TF, Chien AJ, Wallace AM, et al. Race, gene expression signatures, and clinical outcomes of patients with high-risk early breast cancer. JAMA Network Open . (2023) 6:e2349646–e2349646. doi: 10.1001/jamanetworkopen.2023.49646

62. Lalioti A, Verzeletti L, Tiberio P, Gerosa R, Gaudio M, Saltalamacchia G, et al. Common misconceptions about diet and breast cancer: An unclear issue to dispel. Cancers . (2024) 16:306. doi: 10.3390/cancers16020306

63. Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, et al. Predicting breast cancer 5-year survival using machine learning: A systematic review. PloS One . (2021) 16:e0250370. doi: 10.1371/journal.pone.0250370

64. Lima SM, Kehm RD, Swett K, Gonsalves L, Terry MB. Trends in parity and breast cancer incidence in us women younger than 40 years from 1935 to 2015. JAMA network Open . (2020) 3:e200929–e200929. doi: 10.1001/jamanetworkopen.2020.0929

65. Lima SM, Kehm RD, Terry MB. Global breast cancer incidence and mortality trends by region, age-groups, and fertility patterns. EClinicalMedicine . (2021) 38. doi: 10.1016/j.eclinm.2021.100985

66. Lin E, Sleboda P, Rimel BJ, Datta GD. Inequities in colorectal and breast cancer screening: At the intersection of race/ethnicity, sexuality, and gender. SSM-Population Health . (2023) 24:101540. doi: 10.1016/j.ssmph.2023.101540

67. Lipsyc-Sharf M, Ballman KV, Campbell JD, Muss HB, Perez EA, Shulman LN, et al. Age, body mass index, tumor subtype, and racial and ethnic disparities in breast cancer survival. JAMA Network Open . (2023) 6:e2339584–e2339584. doi: 10.1001/jamanetworkopen.2023.39584

68. Liu R, Ospanova S, Perry RJ. The impact of variance in carnitine palmitoyltransferase-1 expression on breast cancer prognosis is stratified by clinical and anthropometric factors. PloS One . (2023) 18:e0281252. doi: 10.1371/journal.pone.0281252

69. Łukasiewicz S, Czeczelewski M, Forma A, Baj J, Sitarz R, Stanisławek A. Breast cancer—epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review. Cancers . (2021) 13:4287. doi: 10.3390/cancers13174287

70. Lv L, Zhao B, Kang J, Li S, Wu H. Trend of disease burden and risk factors of breast cancer in developing countries and territories, from 1990 to 2019: Results from the global burden of disease study 2019. Front Public Health . (2023) 10:1078191. doi: 10.3389/fpubh.2022.1078191

71. Ma L, Liu A, Gao J, Zhao H. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype. Open Life Sci . (2023) 18:20220748. doi: 10.1515/biol-2022-0748

72. Mao X, Omeogu C, Karanth S, Joshi A, Meernik C, Wilson L, et al. Association of reproductive risk factors and breast cancer molecular subtypes: a systematic review and meta-analysis. BMC Cancer . (2023) 23:644. doi: 10.1186/s12885-023-11049-0

73. Martinez RG, van Dongen DM. Deep learning algorithms for the early detection of breast cancer: a comparative study with traditional machine learning. Inform Med Unlocked . (2023) 41:2–8. doi: 10.1016/j.imu.2023.101317

74. Mazo C, Aura C, Rahman A, Gallagher WM, Mooney C. Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: A systematic review. J Personalized Med . (2022) 12:1496. doi: 10.3390/jpm12091496

75. Mohammed AM, Hamed HB, Noaman MK, Alieldin N. Metabolic syndrome and breast cancer risk. J Egyptian Natl Cancer Institute . (2023) 35:42. doi: 10.1186/s43046-023-00203-1

76. Mohanty SS, Mohanty PK. Obesity as potential breast cancer risk factor for postmenopausal women. Genes Dis . (2021) 8:117–23. doi: 10.1016/j.gendis.2019.09.006

77. Morales-Pison S, Tapia JC, Morales-González S, Maldonado E, Acuña M, Calaf GM, et al. Association of germline variation in driver genes with breast cancer risk in Chilean population. Int J Mol Sci . (2023) 24:16076. doi: 10.3390/ijms242216076

78. Mousa NA, Marino N, Simões BM. Sex steroid hormones: effects on breast cancer risk and etiology. Front Endocrinol . (2023) 14:1198770. doi: 10.3389/fendo.2023.1198770

79. Mubtasim N, Moustaid-Moussa N, Gollahon L. The complex biology of the obesity-induced, metastasis-promoting tumor microenvironment in breast cancer. Int J Mol Sci . (2022) 23:2480. doi: 10.3390/ijms23052480

80. Nabila S, Choi JY, Abe SK, Islam MR, Rahman MS, Saito E, et al. Differential patterns of reproductive and lifestyle risk factors for breast cancer according to birth cohorts among women in China, Japan and korea. Breast Cancer Res . (2024) 26:15. doi: 10.1186/s13058-024-01766-0

81. Naji MA, El, Aarika K, Benlahmar EH, Abdelouhahid RA, Debauche O. Machine learning algorithms for breast cancer prediction and diagnosis. Proc Comput Sci . (2021) 191:487–92. doi: 10.1016/j.procs.2021.07.062

82. Nikolaienko O, Eikesdal HP, Ognedal E, Gilje B, Lundgren S, Blix ES, et al. Prenatal brca1 epimutations contribute significantly to triple-negative breast cancer development. Genome Med . (2023) 15:104. doi: 10.1186/s13073-023-01262-8

83. O’Neill B, Yusuf A, Lofters A, Huang A, Ekeleme N, Kiran T, et al. Breast cancer screening among females with and without schizophrenia. JAMA network Open . (2023) 6:e2345530–e2345530. doi: 10.1001/jamanetworkopen.2023.45530

84. Oza P. Chapter 2 - ai in breast imaging: Applications, challenges, and future research. In: Hemanth DJ, editor. Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images . Academic Press (2024). p. 39–54.

85. Pakzad R, Nedjat S, Salehiniya H, Mansournia N, Etminan M, Nazemipour M, et al. Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders. BMC Med Res Method . (2023) 23:157. doi: 10.1186/s12874-023-01978-6

86. Pearson SA, Taylor S, Marsden A, O’Reilly JD, Krishan A, Howell S, et al. Geographic and sociodemographic access to systemic anticancer therapies for secondary breast cancer: a systematic review. Systematic Rev . (2024) 13:35. doi: 10.1186/s13643-023-02382-3

87. Pedersini R, Laganà M, Bosio S, Zanini B, Cosentini D, di Mauro P, et al. Is weight gain preventable in women with early breast cancer undergoing chemotherapy? a real-world study on dietary pattern, physical activity, and body weight before and after chemotherapy. Breast Cancer Res Treat . (2023) 202:461–71. doi: 10.1007/s10549-023-07095-8

88. Pfob A, Mehrara BJ, Nelson JA, Wilkins EG, Pusic AL, Sidey-Gibbons C. Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (inspired-001). Breast . (2021) 60:111–22. doi: 10.1016/j.breast.2021.09.009

89. Roheel A, Khan A, Anwar F, Akbar Z, Akhtar MF, Khan MI, et al. Global epidemiology of breast cancer based on risk factors: a systematic review. Front Oncol . (2023) 13:1–15. doi: 10.3389/fonc.2023.1240098

90. Schonberg MA, Wolfson EA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, et al. A model for predicting both breast cancer risk and non-breast cancer death among women¿ 55 years old. Breast Cancer Res . (2023) 25:8. doi: 10.1186/s13058-023-01605-8

91. Schreurs MA, Ramón Y Cajal T, Adank MA, Collée JM, Hollestelle A, van Rooij J, et al. The benefit of adding polygenic risk scores, lifestyle factors, and breast density to family history and genetic status for breast cancer risk and surveillance classification of unaffected women from germline chek2 c. 1100delc families. Breast . (2024) 73:103611. doi: 10.1016/j.breast.2023.103611

92. Schwartz CJ, Khorsandi N, Blanco A, Mukhtar RA, Chen YY, Krings G. Clinicopathologic and genetic analysis of invasive breast carcinomas in women with germline chek2 variants. Breast Cancer Res Treat . (2023) 204:171–9. doi: 10.1007/s10549-023-07176-8

93. Shi J, Liu T, Ge Y, Liu C, Zhang Q, Xie H, et al. Cholesterol-modified prognostic nutritional index (cpni) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer. BMC Med . (2023) 21:512. doi: 10.1186/s12916-023-03225-7

94. Silva RJG, Grippa WR, Neto LCBS, Enriquez-Martinez OG, Marcarini JAC, Pessanha RM, et al. Factors associated with the nutritional status of women with non-metastatic breast cancer in a Brazilian high complexity oncology center. Nutrients . (2023) 15:4961. doi: 10.3390/nu15234961

95. Silva-Aravena F, Núñez Delafuente H, Gutiérrez-Bahamondes JH, Morales J. A hybrid algorithm of ml and xai to prevent breast cancer: a strategy to support decision making. Cancers . (2023) 15:2443. doi: 10.3390/cancers15092443

96. Smotherman C, Sprague B, Datta S, Braithwaite D, Qin H, Yaghjyan L. Association of air pollution with postmenopausal breast cancer risk in uk biobank. Breast Cancer Res . (2023) 25:83. doi: 10.1186/s13058-023-01681-w

97. Speiser D, Bick U. Primary prevention and early detection of hereditary breast cancer. Breast Care . (2023) 18:448–54. doi: 10.1159/000533391

98. Sun X, Reiner AS, Tran AP, Watt GP, Oh JH, Mellemkjær L, et al. A genome-wide association study of contralateral breast cancer in the women’s environmental cancer and radiation epidemiology study. Breast Cancer Res . (2024) 26:16. doi: 10.1186/s13058-024-01765-1

99. Tarighati E, Keivan H. Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med . (2023) 23:1–16. doi: 10.1007/s10238-021-00781-1

100. Torres-Roman JS, Martinez-Herrera JF, Carioli G, Ybaseta-Medina J, Valcarcel B, Pinto JA, et al. Breast cancer mortality trends in Peruvian women. BMC Cancer . (2020) 20:1–9. doi: 10.1186/s12885-020-07671-x

101. Tüchler A, De Pauw A, Ernst C, Anota A, Lakeman IM, Dick J, et al. Clinical implications of incorporating genetic and non-genetic risk factors in canrisk-based breast cancer risk prediction. Breast . (2024) 73:103615. doi: 10.1016/j.breast.2023.103615

102. Tzenios N. Obesity as a risk factor for cancer. EPRA Int J Res Dev (IJRD) . (2023) 8:101–4. doi: 10.36713/epra2013

103. Vagnini D, Natalucci V, Moi S, Vallorani L, Pietrelli A, Panico AR, et al. Home-based lifestyle intervention for breast cancer survivors: A surprising improvement in the quality of life during the first year of covid-19 pandemic. PloS One . (2024) 19:e0296163. doi: 10.1371/journal.pone.0296163

104. Wang H, MacInnis RJ, Li S. Family history and breast cancer risk for asian women: a systematic review and meta-analysis. BMC Med . (2023) 21:239. doi: 10.1186/s12916-023-02950-3

105. Wekking D, Lambertini M, Dessì M, Denaro N, Bardanzellu F, Garrone O, et al. Cdk4/6 inhibitors in the treatment of metastatic breast cancer: focus on toxicity and safety. Semin Oncol (Elsevier) . (2024) 17:2–8. doi: 10.1053/j.seminoncol.2024.01.002

106. Wong LY, Kapula N, He H, Guenthart BA, Vitzthum LK, Horst K, et al. Risk of developing subsequent primary lung cancer after receiving radiation for breast cancer. JTCVS Open . (2023) 16:919–28. doi: 10.1016/j.xjon.2023.10.031

107. Xie Y, Lei C, Ma Y, Li Y, Yang M, Zhang Y, et al. Prognostic nomograms for breast cancer with lung metastasis: a seer-based population study. BMC Women’s Health . (2024) 24:16. doi: 10.1186/s12905-023-02848-5

108. Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, et al. Causal relationships between breast cancer risk factors based on mammographic features. Breast Cancer Res . (2023) 25:127. doi: 10.1186/s13058-023-01733-1

109. Ye L, Hou Y, Hu W, Wang H, Yang R, Zhang Q, et al. Repressed blautia-acetate immunological axis underlies breast cancer progression promoted by chronic stress. Nat Commun . (2023) 14:6160. doi: 10.1038/s41467-023-41817-2

110. Yogendran L, Meis L, Burnside E, Schrager S. Management of women at high risk for breast cancer. J Am Board Family Med . (2024) 36:1029–32. doi: 10.3122/jabfm.2023.230064R1

111. Yokokawa T, Suzuki K, Tsuji D, Hosonaga M, Kobayashi K, Kawakami K, et al. Influence of menopause on chemotherapy-induced nausea and vomiting in highly emetogenic chemotherapy for breast cancer: A retrospective observational study. Cancer Med . (2023) 12:18745–54. doi: 10.1002/cam4.6494

112. Zheng Y, Li J, Wu Z, Li H, Cao M, Li N, et al. Risk prediction models for breast cancer: a systematic review. BMJ Open . (2022) 12:e055398. doi: 10.1136/bmjopen-2021-055398

113. Zhou M, Henricks M, Loch V, Zhang G, Lu Y, Li X. Mendelian randomization analysis revealed potential metabolic causal factors for breast cancer. Sci Rep . (2023) 13:14290. doi: 10.1038/s41598-023-41130-4

114. Aria M, Cuccurullo C. bibliometrix: an r-tool for comprehensive science mapping analysis. J Informetrics . (2017) 11:959–75. doi: 10.1016/j.joi.2017.08.007

Keywords: breast cancer, risk factors, artificial intelligence (AI), medical history, metabolic factors, reproductive and hormonal factors, lifestyle factors, environmental influence

Citation: Nicolis O, De Los Angeles D and Taramasco C (2024) A contemporary review of breast cancer risk factors and the role of artificial intelligence. Front. Oncol. 14:1356014. doi: 10.3389/fonc.2024.1356014

Received: 14 December 2023; Accepted: 25 March 2024; Published: 18 April 2024.

Reviewed by:

Copyright © 2024 Nicolis, De Los Angeles and Taramasco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Orietta Nicolis, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Breast cancer—epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review.

Simple Summary

1. introduction, 2. breast cancer epidemiology, 3. risk factors of breast cancer, 3.1. non-modifiable factors, 3.1.1. female sex, 3.1.2. older age, 3.1.3. family history, 3.1.4. genetic mutations, 3.1.5. race/ethnicity, 3.1.6. reproductive history, 3.1.7. density of breast tissue, 3.1.8. history of breast cancer and benign breast diseases, 3.1.9. previous radiation therapy, 3.2. modifiable factors, 3.2.1. chosen drugs, 3.2.2. physical activity, 3.2.3. body mass index, 3.2.4. alcohol intake, 3.2.5. smoking, 3.2.6. insufficient vitamin supplementation, 3.2.7. exposure to artificial light, 3.2.8. intake of processed food/diet, 3.2.9. exposure to chemical, 3.2.10. other drugs, 4. breast cancer classification, 4.1. histological classification, 4.2. luminal breast cancer, 4.3. her2-enriched breast cancer, 4.4. basal-like/triple-negative breast cancer, 4.5. claudin-low breast cancer, 4.6. surrogate markers classification, 4.7. american joint committee on cancer classification, 5. prognostic biomarkers, 5.1. estrogen receptor, 5.2. progesterone receptor, 5.3. human epidermal growth factor receptor 2, 5.4. antigen ki-67, 5.6. e-cadherin, 5.7. circulating circular rna, 5.9. microrna, 5.10. tumor-associated macrophages, 5.11. inflammation-based models, 5.11.1. the neutrophil-to-lymphocyte ratio (nlr), 5.11.2. lymphocyte-to-monocyte ratio, 5.11.3. platelet-to-lymphocyte ratio (plr), 6. treatment strategies, 6.1. surgery, 6.2. chemotherapy, 6.3. radiation therapy, 6.4. endocrinal (hormonal) therapy, 6.5. biological therapy, 7. conclusions, author contributions, conflicts of interest.

• Hanahan, D.; Weinberg, R.A. The Hallmarks of Cancer. Cell 2000 , 100 , 57–70. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021 , 71 , 209–249. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Duggan, C.; Dvaladze, A.; Rositch, A.F.; Ginsburg, O.; Yip, C.; Horton, S.; Rodriguez, R.C.; Eniu, A.; Mutebi, M.; Bourque, J.; et al. The Breast Health Global Initiative 2018 Global Summit on Improving Breast Healthcare Through Resource-Stratified Phased Implementation: Methods and overview. Cancer 2020 , 126 , 2339–2352. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• World Health Organization. Global Health Estimates 2016: Disease Burden by Cause, Age, Sex, by Country and by Region, 2000–2016 ; World Health Organization: Geneva, Switzerland, 2018; Available online: https://www.who.int/healthinfo/global_burden_disease/esti-mates/en/index1.html (accessed on 9 July 2021).
• Ferlay, J.; Ervik, M.; Lam, F.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global Cancer Obser-Vatory: Cancer Today ; International Agency for Research on Cancer: Lyon, France, 2020; Available online: https://gco.iarc.fr/today (accessed on 9 July 2021).
• DeSantis, C.E.; Fedewa, S.A.; Sauer, A.G.; Kramer, J.L.; Smith, R.A.; Jemal, A. Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA A Cancer J. Clin. 2015 , 66 , 31–42. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sharma, R. Global, regional, national burden of breast cancer in 185 countries: Evidence from GLOBOCAN 2018. Breast Cancer Res. Treat. 2021 , 187 , 557–567. [ Google Scholar ] [ CrossRef ]
• Ginsburg, O.; Bray, F.; Coleman, M.; Vanderpuye, V.; Eniu, A.; Kotha, S.R.; Sarker, M.; Huong, T.T.; Allemani, C.; Dvaladze, A.; et al. The global burden of women’s cancers: A grand challenge in global health. Lancet 2016 , 389 , 847–860. [ Google Scholar ] [ CrossRef ]
• Vostakolaei, F.A.; Karim-Kos, H.E.; Janssen-Heijnen, M.L.G.; Visser, O.; Verbeek, A.L.M.; Kiemeney, L. The validity of the mortality to incidence ratio as a proxy for site-specific cancer survival. Eur. J. Public Health 2010 , 21 , 573–577. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sankaranarayanan, R.; Swaminathan, R.; Brenner, H.; Chen, K.; Chia, K.S.; Chen, J.-G.; Law, S.C.; Ahn, Y.-O.; Xiang, Y.B.; Yeole, B.B.; et al. Cancer survival in Africa, Asia, and Central America: A population-based study. Lancet Oncol. 2010 , 11 , 165–173. [ Google Scholar ] [ CrossRef ]
• Sharma, R. Breast cancer incidence, mortality and mortality-to-incidence ratio (MIR) are associated with human development, 1990–2016: Evidence from Global Burden of Disease Study 2016. Breast Cancer 2019 , 26 , 428–445. [ Google Scholar ] [ CrossRef ]
• Ferlay, J.; Laversanne, M.; Ervik, M.; Lam, F.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global Cancer Observatory: Cancer Tomorrow. International Agency for Research on Cancer: Lyon, France, 2020; Available online: https://gco.iarc.fr/tomorrow (accessed on 9 July 2021).
• Porter, P. Westernizing Women’s Risks? Breast Cancer in Lower-Income Countries. N. Engl. J. Med. 2008 , 358 , 213–216. [ Google Scholar ] [ CrossRef ]
• Key, T.J.; Appleby, P.N.; Reeves, G.K.; Travis, R.C.; Alberg, A.J.; Barricarte, A.; Berrino, F.; Krogh, V.; Sieri, S.; Brinton, L.A.; et al. Sex hormones and risk of breast cancer in premenopausal women: A collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol. 2013 , 14 , 1009–1019. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Folkerd, E.; Dowsett, M. Sex hormones and breast cancer risk and prognosis. Breast 2013 , 22 , S38–S43. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Tworoger, S.; Eliassen, A.H.; Hankinson, S.E. Postmenopausal plasma sex hormone levels and breast cancer risk over 20 years of follow-up. Breast Cancer Res. Treat. 2013 , 137 , 883–892. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Key, T.; Appleby, P.; Barnes, I.; Reeves, G. The Endogenous Hormones and Breast Cancer Collaborative Group Endogenous Sex Hormones and Breast Cancer in Postmenopausal Women: Reanalysis of Nine Prospective Studies. J. Natl. Cancer Inst. 2002 , 94 , 606–616. [ Google Scholar ] [ CrossRef ]
• Giordano, S.H. Breast cancer in men. N. Engl. J. Med. 2018 , 378 , 2311–2320. [ Google Scholar ] [ CrossRef ]
• Benz, C.C. Impact of aging on the biology of breast cancer. Crit. Rev. Oncol. 2008 , 66 , 65–74. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Siegel, R.; Ma, J.; Zou, Z.; Jemal, A. Cancer statistics, 2014. CA Cancer J. Clin. 2014 , 64 , 9–29. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• McGuire, A.; Brown, J.A.L.; Malone, C.; McLaughlin, R.; Kerin, M.J. Effects of Age on the Detection and Management of Breast Cancer. Cancers 2015 , 7 , 908–929. [ Google Scholar ] [ CrossRef ]
• Stat Bite: Lifetime Probability among Females of Dying of Cancer. JNCI J. Natl. Cancer Inst. 2004 , 96 , 1311–1321.
• Collaborative Group on Hormonal Factors in Breast Cancer. Familial breast cancer: Collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 2001 , 358 , 1389–1399. [ Google Scholar ] [ CrossRef ]
• Shiyanbola, O.O.; Arao, R.F.; Miglioretti, D.L.; Sprague, B.L.; Hampton, J.M.; Stout, N.K.; Kerlikowske, K.; Braithwaite, D.; Buist, D.S.; Egan, K.M.; et al. Emerging Trends in Family History of Breast Cancer and Associated Risk. Cancer Epidemiol. Biomark. Prev. 2017 , 26 , 1753–1760. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Baglia, M.L.; Tang, M.-T.C.; Malone, K.E.; Porter, P.; Li, C.I. Family History and Risk of Second Primary Breast Cancer after In Situ Breast Carcinoma. Cancer Epidemiol. Biomark. Prev. 2018 , 27 , 315–320. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Brewer, H.R.; Jones, M.E.; Schoemaker, M.J.; Ashworth, A.; Swerdlow, A.J. Family history and risk of breast cancer: An analysis accounting for family structure. Breast Cancer Res. Treat. 2017 , 165 , 193–200. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Wu, H.C.; Do, C.; Andrulis, I.L.; John, E.M.; Daly, M.B.; Buys, S.S.; Chung, W.K.; Knight, J.A.; Bradbury, A.R.; Keegan, T.H.M.; et al. Breast cancer family history and allele-specific DNA methylation in the legacy girls study. Epigenetics 2018 , 13 , 240–250. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Elik, A.; Acar, M.; Erkul, C.M.; Gunduz, E.; Gunduz, M. Relationship of Breast Cancer with Ovarian Cancer. Concise Rev. Mol. Pathol. Breast Cancer 2015 , 87–202. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Shiovitz, S.; Korde, L.A. Genetics of breast cancer: A topic in evolution. Ann. Oncol. 2015 , 26 , 1291–1299. [ Google Scholar ] [ CrossRef ]
• Shahbandi, A.; Nguyen, H.D.; Jackson, J.G. TP53 Mutations and Outcomes in Breast Cancer: Reading beyond the Headlines. Trends Cancer 2020 , 6 , 98–110. [ Google Scholar ] [ CrossRef ]
• Corso, G.; Veronesi, P.; Sacchini, V.; Galimberti, V. Prognosis and outcome in CDH1-mutant lobular breast cancer. Eur. J. Cancer Prev. 2018 , 27 , 237–238. [ Google Scholar ] [ CrossRef ]
• Corso, G.; Intra, M.; Trentin, C.; Veronesi, P.; Galimberti, V. CDH1 germline mutations and hereditary lobular breast cancer. Fam. Cancer 2016 , 15 , 215–219. [ Google Scholar ] [ CrossRef ]
• Kechagioglou, P.; Papi, R.M.; Provatopoulou, X.; Kalogera, E.; Papadimitriou, E.; Grigoropoulos, P.; Nonni, A.; Zografos, G.; Kyriakidis, D.A.; Gounaris, A. Tumor suppressor PTEN in breast cancer: Heterozygosity, mutations and protein expression. Anticancer. Res. 2014 , 34 , 1387–1400. [ Google Scholar ]
• Chen, J.; Lindblom, A. Germline mutation screening of the STK11/LKB1 gene in familial breast cancer with LOH on 19p. Clin. Genet. 2001 , 57 , 394–397. [ Google Scholar ] [ CrossRef ]
• Renwick, A.; The Breast Cancer Susceptibility Collaboration (UK); Thompson, D.; Seal, S.; Kelly, P.; Chagtai, T.; Ahmed, M.; North, B.; Jayatilake, H.; Barfoot, R.; et al. ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat. Genet. 2006 , 38 , 873–875. [ Google Scholar ] [ CrossRef ]
• Rahman, N.; The Breast Cancer Susceptibility Collaboration (UK); Seal, S.; Thompson, D.; Kelly, P.; Renwick, A.; Elliott, A.; Reid, S.; Spanova, K.; Barfoot, R.; et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat. Genet. 2006 , 39 , 165–167. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Seal, S.; The Breast Cancer Susceptibility Collaboration (UK); Thompson, D.; Renwick, A.; Elliott, A.; Kelly, P.; Barfoot, R.; Chagtai, T.; Jayatilake, H.; Ahmed, M.; et al. Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance breast cancer susceptibility alleles. Nat. Genet. 2006 , 38 , 1239–1241. [ Google Scholar ] [ CrossRef ]
• Meijers-Heijboer, H.; Ouweland, A.V.D.; Klijn, J.; Wasielewski, M.; De Snoo, A.; Oldenburg, R.; Hollestelle, A.; Houben, M.; Crepin, E.; Van Veghel-Plandsoen, M.; et al. Low-penetrance susceptibility to breast cancer due to CHEK2*1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat. Genet. 2002 , 31 , 55–59. [ Google Scholar ] [ CrossRef ]
• Park, D.J.; Lesueur, F.; Nguyen-Dumont, T.; Pertesi, M.; Odefre, F.; Hammet, F.; Neuhausen, S.L.; John, E.M.; Andrulis, I.L.; Terry, M.B.; et al. Rare mutations in XRCC2 increase the risk of breast cancer. Am. J. Hum. Genet. 2012 , 90 , 734–739. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Thompson, D. Cancer Incidence in BRCA1 Mutation Carriers. J. Natl. Cancer Inst. 2002 , 94 , 1358–1365. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hoskins, L.M.; Roy, K.; Peters, J.A.; Loud, J.T.; Greene, M.H. Disclosure of positive BRCA1/2-mutation status in young couples: The journey from uncertainty to bonding through partner support. Fam. Syst. Health 2008 , 26 , 296–316. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Børresen-Dale, A.-L. TP53and breast cancer. Hum. Mutat. 2003 , 21 , 292–300. [ Google Scholar ] [ CrossRef ]
• Heitzer, E.; Lax, S.; Lafer, I.; Müller, S.M.; Pristauz, G.; Ulz, P.; Jahn, S.; Högenauer, C.; Petru, E.; Speicher, M.R.; et al. Multiplex genetic cancer testing identifies pathogenic mutations in TP53 and CDH1in a patient with bilateral breast and endometrial adenocarcinoma. BMC Med. Genet. 2013 , 14 , 129. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Fusco, N.; Sajjadi, E.; Venetis, K.; Gaudioso, G.; Lopez, G.; Corti, C.; Rocco, E.G.; Criscitiello, C.; Malapelle, U.; Invernizzi, M. PTEN Alterations and Their Role in Cancer Management: Are We Making Headway on Precision Medicine? Genes 2020 , 11 , 719. [ Google Scholar ] [ CrossRef ]
• Angeli, D.; Salvi, S.; Tedaldi, G. Genetic Predisposition to Breast and Ovarian Cancers: How Many and Which Genes to Test? Int. J. Mol. Sci. 2020 , 21 , 1128. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Foretová, L.; Navrátilová, M.; Svoboda, M.; Vašíčková, P.; Hrabincová, E.S.; Házová, J.; Kleiblová, P.; Kleibl, Z.; Macháčková, E.; Palácová, M.; et al. Recommendations for Preventive Care for Women with Rare Genetic Cause of Breast and Ovarian Cancer. Klin. Onkol. 2019 , 32 , 6–13. [ Google Scholar ] [ CrossRef ]
• Hu, Z.-Y.; Liu, L.; Xie, N.; Lu, J.; Liu, Z.; Tang, Y.; Wang, Y.; Yang, J.; Ouyang, Q. Germline PALB2 Mutations in Cancers and Its Distinction from Somatic PALB2 Mutations in Breast Cancers. Front. Genet. 2020 , 11 , 829. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cantor, S.B.; Guillemette, S. Hereditary breast cancer and the BRCA1-associated FANCJ/BACH1/BRIP1. Future Oncol. 2011 , 7 , 253–261. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Rainville, I.; Hatcher, S.; Rosenthal, E.; Larson, K.; Bernhisel, R.; Meek, S.; Gorringe, H.; Mundt, E.; Manley, S. High risk of breast cancer in women with biallelic pathogenic variants in CHEK2. Breast Cancer Res. Treat. 2020 , 180 , 503–509. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kluźniak, W.; The Polish Hereditary Breast Cancer Consortium; Wokołorczyk, D.; Rusak, B.; Huzarski, T.; Gronwald, J.; Stempa, K.; Rudnicka, H.; Kashyap, A.; Dębniak, T.; et al. Inherited variants in XRCC2 and the risk of breast cancer. Breast Cancer Res. Treat. 2019 , 178 , 657–663. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Hill, D.A.; Prossnitz, E.R.; Royce, M.; Nibbe, A. Temporal trends in breast cancer survival by race and ethnicity: A population-based cohort study. PLoS ONE 2019 , 14 , e0224064. [ Google Scholar ] [ CrossRef ]
• Yedjou, C.G.; Sims, J.N.; Miele, L.; Noubissi, F.; Lowe, L.; Fonseca, D.D.; Alo, R.A.; Payton, M.; Tchounwou, P.B. Health and Racial Disparity in Breast Cancer. Adv. Exp. Med. Biol. 2019 , 1152 , 31–49. [ Google Scholar ] [ CrossRef ]
• ACS. American Cancer Society (2016) Breast Cancer Facts & Figures, 2015–2016 ; American Cancer Society: Atlanta, GA, USA, 2016. [ Google Scholar ]
• Bernstein, L. Epidemiology of Endocrine-Related Risk Factors for Breast Cancer. J. Mammary Gland. Biol. Neoplasia 2002 , 7 , 3–15. [ Google Scholar ] [ CrossRef ]
• Albrektsen, G.; Heuch, I.; Hansen, S.; Kvåle, G. Breast cancer risk by age at birth, time since birth and time intervals between births: Exploring interaction effects. Br. J. Cancer 2004 , 92 , 167–175. [ Google Scholar ] [ CrossRef ]
• Husby, A.; Wohlfahrt, J.; Øyen, N.; Melbye, M. Pregnancy duration and breast cancer risk. Nat. Commun. 2018 , 9 , 4255. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Innes, K.E.; Byers, T.E. Preeclampsia and Breast Cancer Risk. Epidemiology 1999 , 10 , 722–732. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Reeves, G.K.; Kan, S.-W.; Key, T.; Tjønneland, A.; Olsen, A.; Overvad, K.; Peeters, P.H.; Clavel-Chapelon, F.; Paoletti, X.; Berrino, F.; et al. Breast cancer risk in relation to abortion: Results from the EPIC study. Int. J. Cancer 2006 , 119 , 1741–1745. [ Google Scholar ] [ CrossRef ]
• Ursin, G.; Bernstein, L.; Lord, S.J.; Karim, R.; Deapen, D.; Press, M.F.; Daling, J.R.; Norman, S.A.; Liff, J.M.; Marchbanks, P.A.; et al. Reproductive factors and subtypes of breast cancer defined by hormone receptor and histology. Br. J. Cancer 2005 , 93 , 364–371. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Orgéas, C.C.; Hall, P.; Rosenberg, L.U.; Czene, K. The influence of menstrual risk factors on tumor characteristics and survival in postmenopausal breast cancer. Breast Cancer Res. 2008 , 10 , R107. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Titus-Ernstoff, L.; Longnecker, M.; Newcomb, P.A.; Dain, B.; Greenberg, E.R.; Mittendorf, R.; Stampfer, M.; Willett, W. Menstrual factors in relation to breast cancer risk. Cancer Epidemiol. Biomark. Prev. 1998 , 7 , 783–789. [ Google Scholar ]
• Checka, C.M.; Chun, J.E.; Schnabel, F.R.; Lee, J.; Toth, H. The Relationship of Mammographic Density and Age: Implications for Breast Cancer Screening. Am. J. Roentgenol. 2012 , 198 , W292–W295. [ Google Scholar ] [ CrossRef ]
• Kim, E.Y.; Chang, Y.; Ahn, J.; Yun, J.; Park, Y.L.; Park, C.H.; Shin, H.; Ryu, S. Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women. Cancer 2020 , 126 , 4687–4696. [ Google Scholar ] [ CrossRef ]
• Duffy, S.W.; Morrish, O.W.; Allgood, P.C.; Black, R.; Gillan, M.G.; Willsher, P.; Cooke, J.; Duncan, K.A.; Michell, M.J.; Dobson, H.M.; et al. Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer. Eur. J. Cancer 2017 , 88 , 48–56. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Schacht, D.V.; Yamaguchi, K.; Lai, J.; Kulkarni, K.; Sennett, C.A.; Abe, H. Importance of a Personal History of Breast Cancer as a Risk Factor for the Development of Subsequent Breast Cancer: Results from Screening Breast MRI. Am. J. Roentgenol. 2014 , 202 , 289–292. [ Google Scholar ] [ CrossRef ]
• Hartmann, L.C.; Sellers, T.A.; Frost, M.H.; Lingle, W.L.; Degnim, A.C.; Ghosh, K.; Vierkant, R.; Maloney, S.D.; Pankratz, V.S.; Hillman, D.W.; et al. Benign Breast Disease and the Risk of Breast Cancer. N. Engl. J. Med. 2005 , 353 , 229–237. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dyrstad, S.W.; Yan, Y.; Fowler, A.M.; Colditz, G.A. Breast cancer risk associated with benign breast disease: Systematic review and meta-analysis. Breast Cancer Res. Treat. 2015 , 149 , 569–575. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Costantino, J.P.; Tan-Chiu, E.; Wickerham, D.L.; Paik, S.; Wolmark, N. Lower-Category Benign Breast Disease and the Risk of Invasive Breast Cancer. J. Natl. Cancer Inst. 2004 , 96 , 616–620. [ Google Scholar ] [ CrossRef ]
• Ng, J.; Shuryak, I. Minimizing second cancer risk following radiotherapy: Current perspectives. Cancer Manag. Res. 2014 , 7 , 1–11. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, Q.; Liu, J.; Ao, N.; Yu, H.; Peng, Y.; Ou, L.; Zhang, S. Secondary cancer risk after radiation therapy for breast cancer with different radiotherapy techniques. Sci. Rep. 2020 , 10 , 1220. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ng, A.K.; Travis, L.B. Radiation therapy and breast cancer risk. J. Natl. Compr. Cancer Netw. 2009 , 7 , 1121–1128. [ Google Scholar ] [ CrossRef ]
• Bartelink, H.; Horiot, J.-C.; Poortmans, P.; Struikmans, H.; Bogaert, W.V.D.; Barillot, I.; Fourquet, A.; Borger, J.; Jager, J.; Hoogenraad, W.; et al. Recurrence Rates after Treatment of Breast Cancer with Standard Radiotherapy with or without Additional Radiation. N. Engl. J. Med. 2001 , 345 , 1378–1387. [ Google Scholar ] [ CrossRef ]
• Hoover, R.N.; Hyer, M.; Pfeiffer, R.M.; Adam, E.; Bond, B.; Cheville, A.L.; Colton, T.; Hartge, P.; Hatch, E.; Herbst, A.L.; et al. Adverse Health Outcomes in Women Exposed In Utero to Diethylstilbestrol. N. Engl. J. Med. 2011 , 365 , 1304–1314. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Verloop, J.; Van Leeuwen, F.E.; Helmerhorst, T.J.M.; Van Boven, H.H.; Rookus, M.A. Cancer risk in DES daughters. Cancer Causes Control. 2010 , 21 , 999–1007. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hilakivi-Clarke, L. Maternal exposure to diethylstilbestrol during pregnancy and increased breast cancer risk in daughters. Breast Cancer Res. 2014 , 16 , 208. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Palmer, J.R. Prenatal Diethylstilbestrol Exposure and Risk of Breast Cancer. Cancer Epidemiol. Biomark. Prev. 2006 , 15 , 1509–1514. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Narod, S.A. Hormone replacement therapy and the risk of breast cancer. Nat. Rev. Clin. Oncol. 2011 , 8 , 669–676. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vinogradova, Y.; Coupland, C.; Hippisley-Cox, J. Use of hormone replacement therapy and risk of breast cancer: Nested case-control studies using the QResearch and CPRD databases. BMJ 2020 , 371 , m3873. [ Google Scholar ] [ CrossRef ]
• Steingart, A.; Cotterchio, M.; Kreiger, N.; Sloan, M. Antidepressant medication use and breast cancer risk: A case-control study. Int. J. Epidemiol. 2003 , 32 , 961–966. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wernli, K.J.; Ms, J.M.H.; Trentham-Dietz, A.; Newcomb, P.A. Antidepressant medication use and breast cancer risk. Pharmacoepidemiol. Drug Saf. 2009 , 18 , 284–290. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lawlor, D.A. Systematic review of the epidemiologic and trial evidence of an association between antidepressant medication and breast cancer. J. Clin. Epidemiol. 2003 , 56 , 155–163. [ Google Scholar ] [ CrossRef ]
• Friedman, G.D.; Oestreicher, N.; Chan, J.; Quesenberry, C.P.; Udaltsova, N.; Habel, L. Antibiotics and Risk of Breast Cancer: Up to 9 Years of Follow-Up of 2.1 Million Women. Cancer Epidemiol. Biomark. Prev. 2006 , 15 , 2102–2106. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pahor, M.; Guralnik, J.M.; Salive, M.E.; Corti, M.-C.; Carbonin, P.; Havlik, R.J. Do Calcium Channel Blockers Increase the Risk of Cancer? Am. J. Hypertens. 1996 , 9 , 695–699. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Coogan, P.F.; Rao, S.R.; Rosenberg, L.; Palmer, J.R.; Strom, B.L.; Zauber, A.G.; Stolley, P.D.; Shapiro, S. The Relationship of Nonsteroidal Anti-inflammatory Drug Use to the Risk of Breast Cancer. Prev. Med. 1999 , 29 , 72–76. [ Google Scholar ] [ CrossRef ]
• Denoyelle, C.; Vasse, M.; Körner, M.; Mishal, Z.; Ganné, F.; Vannier, J.-P.; Soria, J.; Soria, C. Cerivastatin, an inhibitor of HMG-CoA reductase, inhibits the signaling pathways involved in the invasiveness and metastatic properties of highly invasive breast cancer cell lines: An in vitro study. Carcinogenesis 2001 , 22 , 1139–1148. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chen, X.; Wang, Q.; Zhang, Y.; Xie, Q.; Tan, X. Physical Activity and Risk of Breast Cancer: A Meta-Analysis of 38 Cohort Studies in 45 Study Reports. Value Health 2018 , 22 , 104–128. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kyu, H.H.; Bachman, V.F.; Alexander, L.T.; Mumford, J.E.; Afshin, A.; Estep, K.; Veerman, L.; Delwiche, K.; Iannarone, M.L.; Moyer, M.L.; et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: Systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ 2016 , 354 , i3857. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bernstein, L.; Ross, R.K. Endogenous Hormones and Breast Cancer Risk. Epidemiol. Rev. 1993 , 15 , 48–65. [ Google Scholar ] [ CrossRef ]
• Thune, I.; Brenn, T.; Lund, E.; Gaard, M. Physical Activity and the Risk of Breast Cancer. N. Engl. J. Med. 1997 , 336 , 1269–1275. [ Google Scholar ] [ CrossRef ]
• Hoffinan-Goetz, L. Influence of Physical Activity and Exercise on Innate Immunity. Nutr. Rev. 2009 , 56 , S126–S130. [ Google Scholar ] [ CrossRef ]
• Hoffman-Goetz, L.; Apter, D.; Demark-Wahnefried, W.; Goran, M.I.; McTiernan, A. Reichman ME. Possible mechanisms mediating an association between physical activity and breast cancer. Cancer 1998 , 83 (Suppl. 3), 621–628. [ Google Scholar ] [ CrossRef ]
• Kolb, R.; Zhang, W. Obesity and Breast Cancer: A Case of Inflamed Adipose Tissue. Cancers 2020 , 12 , 1686. [ Google Scholar ] [ CrossRef ]
• Wang, X.; Hui, T.-L.; Wang, M.-Q.; Liu, H.; Li, R.-Y.; Song, Z.-C. Body Mass Index at Diagnosis as a Prognostic Factor for Early-Stage Invasive Breast Cancer after Surgical Resection. Oncol. Res. Treat. 2019 , 42 , 195–201. [ Google Scholar ] [ CrossRef ]
• Sun, L.; Zhu, Y.; Qian, Q.; Tang, L. Body mass index and prognosis of breast cancer. Medicine 2018 , 97 , e11220. [ Google Scholar ] [ CrossRef ]
• James, F.; Wootton, S.; Jackson, A.; Wiseman, M.; Copson, E.; Cutress, R. Obesity in breast cancer—What is the risk factor? Eur. J. Cancer 2015 , 51 , 705–720. [ Google Scholar ] [ CrossRef ]
• Protani, M.; Coory, M.; Martin, J. Effect of obesity on survival of women with breast cancer: Systematic review and meta-analysis. Breast Cancer Res. Treat. 2010 , 123 , 627–635. [ Google Scholar ] [ CrossRef ]
• Iyengar, N.M.; Arthur, R.; Manson, J.E.; Chlebowski, R.T.; Kroenke, C.H.; Peterson, L.; Cheng, T.-Y.D.; Feliciano, E.C.; Lane, D.; Luo, J.; et al. Association of Body Fat and Risk of Breast Cancer in Postmenopausal Women with Normal Body Mass Index. JAMA Oncol. 2019 , 5 , 155–163. [ Google Scholar ] [ CrossRef ]
• Hopper, J.L.; kConFab Investigators; Dite, G.S.; MacInnis, R.J.; Liao, Y.; Zeinomar, N.; Knight, J.A.; Southey, M.C.; Milne, R.L.; Chung, W.K.; et al. Age-specific breast cancer risk by body mass index and familial risk: Prospective family study cohort (ProF-SC). Breast Cancer Res. 2018 , 20 , 132. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Rachdaoui, N.; Sarkar, D.K. Effects of Alcohol on the Endocrine System. Endocrinol. Metab. Clin. N. Am. 2013 , 42 , 593–615. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Erol, A.; Ho, A.M.-C.; Winham, S.J.; Karpyak, V.M. Sex hormones in alcohol consumption: A systematic review of evidence. Addict. Biol. 2017 , 24 , 157–169. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Coronado, G.D.; Beasley, J.; Livaudais, J. Alcohol consumption and the risk of breast cancer. Salud. Publica. Mex. 2011 , 53 , 440–447. [ Google Scholar ]
• Zeinomar, N.; kConFab Investigators; Knight, J.A.; Genkinger, J.M.; Phillips, K.-A.; Daly, M.B.; Milne, R.L.; Dite, G.S.; Kehm, R.D.; Liao, Y.; et al. Alcohol consumption, cigarette smoking, and familial breast cancer risk: Findings from the Prospective Family Study Cohort (ProF-SC). Breast Cancer Res. 2019 , 21 , 128. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Nguyen, N.; Colditz, G.A. Links between Alcohol Consumption and Breast Cancer: A Look at the Evidence. Women’s Health 2015 , 11 , 65–77. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Terry, P.D.; Rohan, T.E. Cigarette smoking and the risk of breast cancer in women: A review of the literature. Cancer Epidemiol. Biomark. Prev. 2002 , 11 , 953–971. [ Google Scholar ]
• Catsburg, C.; Miller, A.B.; Rohan, T.E. Active cigarette smoking and risk of breast cancer. Int. J. Cancer 2014 , 136 , 2204–2209. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jones, M.; Schoemaker, M.J.; Wright, L.B.; Ashworth, A.; Swerdlow, A.J. Smoking and risk of breast cancer in the Generations Study cohort. Breast Cancer Res. 2017 , 19 , 118. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Couch, F.J.; Cerhan, J.R.; Vierkant, R.A.; Grabrick, D.M.; Therneau, T.M.; Pankratz, V.S.; Hartmann, L.C.; Olson, J.E.; Vachon, C.M.; Sellers, T.A. Cigarette smoking increases risk for breast cancer in high-risk breast cancer families. Cancer Epidemiol. Biomark. Prev. 2001 , 10 , 327–332. [ Google Scholar ]
• Misotti, A.M.; Gnagnarella, P. Ecancermedicalscience. Ecancermedicalscience 2013 , 7 , 365. [ Google Scholar ] [ CrossRef ]
• Cui, Y. Vitamin D, Calcium, and Breast Cancer Risk: A Review. Cancer Epidemiol. Biomark. Prev. 2006 , 15 , 1427–1437. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Atoum, M.; Alzoughool, F. Vitamin D and Breast Cancer: Latest Evidence and Future Steps. Breast Cancer: Basic Clin. Res. 2017 , 11 , 1178223417749816. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• El-Sharkawy, A.; Malki, A. Vitamin D Signaling in Inflammation and Cancer: Molecular Mechanisms and Therapeutic Implications. Molecules 2020 , 25 , 3219. [ Google Scholar ] [ CrossRef ]
• Estébanez, N.; Gómez-Acebo, I.; Palazuelos, C.; Llorca, J.; Dierssen-Sotos, T. Vitamin D exposure and Risk of Breast Cancer: A meta-analysis. Sci. Rep. 2018 , 8 , 9039. [ Google Scholar ] [ CrossRef ]
• Huss, L.; Butt, S.T.; Borgquist, S.; Elebro, K.; Sandsveden, M.; Rosendahl, A.; Manjer, J. Vitamin D receptor expression in invasive breast tumors and breast cancer survival. Breast Cancer Res. 2019 , 21 , 84. [ Google Scholar ] [ CrossRef ]
• Zhou, L.; Chen, B.; Sheng, L.; Turner, A. The effect of vitamin D supplementation on the risk of breast cancer: A trial sequential meta-analysis. Breast Cancer Res. Treat. 2020 , 182 , 1–8. [ Google Scholar ] [ CrossRef ]
• Al-Naggar, R.A.; Anil, S. Artificial Light at Night and Cancer: Global Study. Asian Pac. J. Cancer Prev. 2016 , 17 , 4661–4664. [ Google Scholar ] [ CrossRef ]
• Johns, L.E.; Jones, M.; Schoemaker, M.; McFadden, E.; Ashworth, A.; Swerdlow, A. Domestic light at night and breast cancer risk: A prospective analysis of 105,000 UK women in the Generations Study. Br. J. Cancer 2018 , 118 , 600–606. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dandamudi, A.; Tommie, J.; Nommsen-Rivers, L.; Couch, S. Dietary Patterns and Breast Cancer Risk: A Systematic Review. Anticancer. Res. 2018 , 38 , 3209–3222. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Fiolet, T.; Srour, B.; Sellem, L.; Kesse-Guyot, E.; Allès, B.; Méjean, C.; Deschasaux, M.; Fassier, P.; Latino-Martel, P.; Beslay, M.; et al. Consumption of ultra-processed foods and cancer risk: Results from Nutri Net-Santé prospective cohort. BMJ 2018 , 360 , k322. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Castelló, A.; Pollán, M.; Buijsse, B.; Ruiz, A.; Casas, A.M.; Baena-Cañada, J.M.; Lope, V.; Antolín, S.; Ramos, M.; Munoz, M.; et al. Spanish Mediterranean diet and other dietary patterns and breast cancer risk: Case–control Epi GEICAM study. Br. J. Cancer 2014 , 111 , 1454–1462. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kotepui, M. Diet and risk of breast cancer. Contemp. Oncol. 2016 , 20 , 13–19. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Li, M.-J.; Yin, Y.-C.; Wang, J.; Jiang, Y.-F. Green tea compounds in breast cancer prevention and treatment. World J. Clin. Oncol. 2014 , 5 , 520–528. [ Google Scholar ] [ CrossRef ]
• Liu, D.; Chen, Z. The Effect of Curcumin on Breast Cancer Cells. J. Breast Cancer 2013 , 16 , 133–137. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wright, L.; Frye, J.; Gorti, B.; Timmermann, B.; Funk, J. Bioactivity of Turmeric-derived Curcuminoids and Related Metabolites in Breast Cancer. Curr. Pharm. Des. 2013 , 19 , 6218–6225. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Casey, S.C.; Vaccari, M.; Al-Mulla, F.; Altemaimi, R.; Amedei, A.; Barcellos-Hoff, M.H.; Brown, D.; Chapellier, M.; Christopher, J.; Curran, C.S.; et al. The effect of environmental chemicals on the tumor microenvironment. Carcinogenesis 2015 , 36 , S160–S183. [ Google Scholar ] [ CrossRef ]
• Videnros, C.; Selander, J.; Wiebert, P.; Albin, M.; Plato, N.; Borgquist, S.; Manjer, J.; Gustavsson, P. Investigating the risk of breast cancer among women exposed to chemicals: A nested case–control study using improved exposure estimates. Int. Arch. Occup. Environ. Health 2019 , 93 , 261–269. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rodgers, K.M.; Udesky, J.O.; Rudel, R.A.; Brody, J.G. Environmental chemicals and breast cancer: An updated review of epidemiological literature informed by biological mechanisms. Environ. Res. 2018 , 160 , 152–182. [ Google Scholar ] [ CrossRef ]
• Eve, L.; Fervers, B.; Le Romancer, M.; Etienne-Selloum, N. Exposure to Endocrine Disrupting Chemicals and Risk of Breast Cancer. Int. J. Mol. Sci. 2020 , 21 , 9139. [ Google Scholar ] [ CrossRef ]
• Leso, V.; Ercolano, M.L.; Cioffi, D.L.; Iavicoli, I. Occupational Chemical Exposure and Breast Cancer Risk According to Hormone Receptor Status: A Systematic Review. Cancers 2019 , 11 , 1882. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Velicer, C.M.; Lampe, J.W.; Heckbert, S.R.; Potter, J.D.; Taplin, S.H. Hypothesis: Is antibiotic use associated with breast cancer? Cancer Causes Control 2003 , 14 , 739–747. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brandes, L.J.; Arron, R.J.; Bogdanovic, R.P.; Tong, J.; Zaborniak, C.L.; Hogg, G.R.; Warrington, R.C.; Fang, W.; Labella, F.S. Stimulation of malignant growth in rodents by antidepressant drugs at clinically relevant doses. Cancer Res. 1992 , 52 , 3796–3800. [ Google Scholar ]
• Bjarnadottir, O.; Romero, Q.; Bendahl, P.O.; Jirström, K.; Rydén, L.; Loman, N.; Uhlén, M.; Johannesson, H.; Rose, C.; Grabau, D.; et al. Targeting HMG-CoA reductase with statins in a window-of-opportunity breast cancer trial. Breast Cancer Res. Treat. 2013 , 138 , 499–508. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Olsen, J.H.; Sørensen, H.T.; Friis, S.; McLaughli, J.K.; Steffensen, F.H.; Nielsen, G.L.; Andersen, M.; Fraumeni, J.F., Jr.; Olsen, J. Cancer risk in users of calcium channel blockers. Hypertension 1997 , 29 , 1091–1094. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, S.M.; Cook, N.R.; Manson, J.E.; Lee, I.-M.; Buring, J.E. Low-dose aspirin and breast cancer risk: Results by tumour characteristics from a randomised trial. Br. J. Cancer 2008 , 98 , 989–991. [ Google Scholar ] [ CrossRef ]
• Tavassoli, F.A. Pathology and Genetics of Tumours of the Breast and Female Genital Organs ; World Hhealth Organization Classification of Tumours: Lyon, France, 2003. [ Google Scholar ]
• Weigelt, B.; Horlings, H.M.; Kreike, B.; Hayes, M.M.; Hauptmann, M.; Wessels, L.F.A.; De Jong, D.; van de Vijver, M.; Veer, L.J.V.; Peterse, J.L. Refinement of breast cancer classification by molecular characterization of histological special types. J. Pathol. 2008 , 216 , 141–150. [ Google Scholar ] [ CrossRef ]
• Erber, R.; Hartmann, A. Histology of Luminal Breast Cancer. Breast Care 2020 , 15 , 327–336. [ Google Scholar ] [ CrossRef ]
• Perou, C.; Sørlie, T.; Eisen, M.; Van De Rijn, M.; Jeffrey, S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; et al. Molecular portraits of human breast tumours. Nat. Cell Biol. 2000 , 406 , 747–752. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sørlie, T. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001 , 98 , 10869–10874. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Prat, A.; Perou, C.M. Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 2001 , 5 , 5–23. [ Google Scholar ] [ CrossRef ]
• Network, T.C.G.A. Comprehensive molecular portraits of human breast tumours. Nature 2012 , 490 , 61–70. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Herschkowitz, J.I.; Simin, K.; Weigman, V.J.; Mikaelian, I.; Usary, J.; Hu, Z.; Rasmussen, K.E.; Jones, L.P.; Assefnia, S.; Chandrasekharan, S.; et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 2007 , 8 , R76. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Parker, J.S.; Mullins, M.; Cheang, M.C.U.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J. Clin. Oncol. 2009 , 27 , 1160–1167. [ Google Scholar ] [ CrossRef ]
• Gnant, M.; Filipits, M.; Greil, R.; Stoeger, H.; Rudas, M.; Bago-Horvath, Z.; Mlineritsch, B.; Kwasny, W.; Knauer, M.; Singer, C.; et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: Using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann. Oncol. 2013 , 25 , 339–345. [ Google Scholar ] [ CrossRef ]
• Sestak, I. Prediction of late distant recurrence after 5 years of endocrine treatment: A combined analysis of patients from the Austrian breast and colorectal cancer study group 8 and arimidex, tamoxifen alone or in combination randomized trials using the PAM50 risk of recurrence score. J. Clin. Oncol. 2015 , 33 , 916–922. [ Google Scholar ] [ PubMed ]
• Prat, A.; Galván, P.; Jimenez, B.; Buckingham, W.; Jeiranian, H.A.; Schaper, C.; Vidal, M.; Álvarez, M.; Díaz, S.; Ellis, C.; et al. Prediction of Response to Neoadjuvant Chemotherapy Using Core Needle Biopsy Samples with the Prosigna Assay. Clin. Cancer Res. 2015 , 22 , 560–566. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Howlader, N.; Altekruse, S.F.; Li, C.I.; Chen, V.W.; Clarke, C.A.; Ries, L.A.G.; Cronin, K.A. US Incidence of Breast Cancer Subtypes Defined by Joint Hormone Receptor and HER2 Status. J. Natl. Cancer Inst. 2014 , 106 , dju055. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Weigelt, B.; Geyer, F.C.; Reis-Filho, J.S. Histological types of breast cancer: How special are they? Mol. Oncol. 2010 , 4 , 192–208. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Makki, J. Diversity of Breast Carcinoma: Histological Subtypes and Clinical Relevance. Clin. Med. Insights Pathol. 2015 , 8 , 23–31. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Weigelt, B.; Baehner, F.L.; Reis-Filho, J.S. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: A retrospective of the last decade. J. Pathol. J. Pathol. Soc. Great Br. Irel. 2010 , 220 , 263–280. [ Google Scholar ] [ CrossRef ]
• Prat, A. Prognostic significance of progesterone receptor–positive tumor cells within immunohistochemically defined luminal A breast cancer. J. Clin. Oncol. 2013 , 31 , 203. [ Google Scholar ] [ CrossRef ]
• Eroles, P.; Bosch, A.; Pérez-Fidalgo, J.A.; Lluch, A. Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways. Cancer Treat. Rev. 2012 , 38 , 698–707. [ Google Scholar ] [ CrossRef ]
• Ades, F. Luminal B breast cancer: Molecular characterization, clinical management, and future perspectives. J. Clin. Oncol. 2014 , 32 , 2794–2803. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cheang, M.C.U.; Chia, S.K.; Voduc, D.; Gao, D.; Leung, S.; Snider, J.; Watson, M.; Davies, S.; Bernard, P.S.; Parker, J.S.; et al. Ki67 Index, HER2 Status, and Prognosis of Patients with Luminal B Breast Cancer. J. Natl. Cancer Inst. 2009 , 101 , 736–750. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Raj-Kumar, P.-K.; Liu, J.; Hooke, J.A.; Kovatich, A.J.; Kvecher, L.; Shriver, C.D.; Hu, H. PCA-PAM50 improves consistency between breast cancer intrinsic and clinical subtyping reclassifying a subset of luminal A tumors as luminal B. Sci. Rep. 2019 , 9 , 7956. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Xu, C.; Wei, Q.; Guo, S.J.; Zhou, J.; Mei, J.; Jiang, Z.N.; Shen, J.G.; Wang, L.B. FOXA1 Expression Significantly Predict Response to Chemotherapy in Estrogen Receptor-Positive Breast Cancer Patients. Ann. Surg. Oncol. 2015 , 22 , 2034–2039. [ Google Scholar ] [ CrossRef ]
• Ranjit, K. Breast cancer. Lancet 2005 , 365 , 1742. [ Google Scholar ]
• Roberts, S.A.; Lawrence, M.S.; Klimczak, L.J.; Grimm, S.A.; Fargo, D.; Stojanov, P.; Kiezun, A.; Kryukov, G.; Carter, S.L.; Saksena, G.; et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 2013 , 45 , 970–976. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kuong, K.J.; A Loeb, L. APOBEC3B mutagenesis in cancer. Nat. Genet. 2013 , 45 , 964–965. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kanu, N.; Cerone, M.A.; Goh, G.; Zalmas, P.; Bartkova, J.; Dietzen, M.; McGranahan, N.; Rogers, R.; Law, E.K.; Gromova, I.; et al. DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer. Genome Biol. 2016 , 17 , 185. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Prat, A.; Carey, L.A.; Adamo, B.; Vidal, M.; Tabernero, J.; Cortes, J.; Parker, J.S.; Perou, C.; Baselga, J. Molecular Features and Survival Outcomes of the Intrinsic Subtypes Within HER2-Positive Breast Cancer. J. Natl. Cancer Inst. 2014 , 106 , dju152. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Plasilova, M.L.; Hayse, B.; Killelea, B.K.; Horowitz, N.R.; Chagpar, A.B.; Lannin, D.R. Features of triple-negative breast cancer. Medicine 2016 , 95 , e4614. [ Google Scholar ] [ CrossRef ]
• Newman, L.A.; Reis-Filho, J.S.; Morrow, M.; Carey, L.A.; King, T.A. The 2014 Society of Surgical Oncology Susan G. Komen for the Cure Symposium: Triple-Negative Breast Cancer. Ann. Surg. Oncol. 2014 , 22 , 874–882. [ Google Scholar ] [ CrossRef ]
• Pareja, F. Triple-negative breast cancer: The importance of molecular and histologic subtyping, and recognition of low-grade variants. NPJ Breast Cancer 2016 , 2 , 16036. [ Google Scholar ] [ CrossRef ]
• Wetterskog, D. Adenoid cystic carcinomas constitute a genomically distinct subgroup of triple-negative and basal-like breast cancers. J. Pathol. 2012 , 226 , 84–96. [ Google Scholar ] [ CrossRef ]
• Badve, S.; Dabbs, D.J.; Schnitt, S.J.; Baehner, F.L.; Decker, T.; Eusebi, V.; Fox, S.; Ichihara, S.; Jacquemier, J.; Lakhani, S.R.; et al. Basal-like and triple-negative breast cancers: A critical review with an emphasis on the implications for pathologists and oncologists. Mod. Pathol. 2010 , 24 , 157–167. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lehmann, B.; Bauer, J.A.; Chen, X.; Sanders, M.E.; Chakravarthy, A.B.; Shyr, Y.; Pietenpol, J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 2011 , 121 , 2750–2767. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Wang, D.-Y.; Jiang, Z.; Ben-David, Y.; Woodgett, J.R.; Zacksenhaus, E. Molecular stratification within triple-negative breast cancer subtypes. Sci. Rep. 2019 , 9 , 19107. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Santonja, A.; Sánchez-Muñoz, A.; Lluch, A.; Chica-Parrado, M.R.; Albanell, J.; Chacón, J.I.; Antolín, S.; Jerez, J.M.; De La Haba, J.; De Luque, V.; et al. Triple negative breast cancer subtypes and pathologic complete response rate to neoadjuvant chemotherapy. Oncotarget 2018 , 9 , 26406–26416. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Prat, A.; Parker, J.S.; Karginova, O.; Fan, C.; Livasy, C.; Herschkowitz, J.I.; He, X.; Perou, C.M. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010 , 12 , R68. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hennessy, B.T.; Gonzalez-Angulo, A.-M.; Stemke-Hale, K.; Gilcrease, M.Z.; Krishnamurthy, S.; Lee, J.-S.; Fridlyand, J.; Sahin, A.A.; Agarwal, R.; Joy, C.; et al. Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics. Cancer Res. 2009 , 69 , 4116–4124. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Dias, K.; Dvorkin-Gheva, A.; Hallett, R.M.; Wu, Y.; Hassell, J.; Pond, G.R.; Levine, M.; Whelan, T.; Bane, A.L. Claudin-Low Breast Cancer; Clinical & Pathological Characteristics. PLoS ONE 2017 , 12 , e0168669. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Morel, A.-P.; Ginestier, C.; Pommier, R.M.; Cabaud, O.; Ruiz, E.; Wicinski, J.; Devouassoux-Shisheboran, M.; Combaret, V.; Finetti, P.; Chassot, C.; et al. A stemness-related ZEB1–MSRB3 axis governs cellular pliancy and breast cancer genome stability. Nat. Med. 2017 , 23 , 568–578. [ Google Scholar ] [ CrossRef ]
• Puisieux, A.; Pommier, R.; Morel, A.-P.; Lavial, F. Cellular Pliancy and the Multistep Process of Tumorigenesis. Cancer Cell 2018 , 33 , 164–172. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Parise, C.A.; Bauer, K.R.; Brown, M.M.; Caggiano, V. Breast Cancer Subtypes as Defined by the Estrogen Receptor (ER), Progesterone Receptor (PR), and the Human Epidermal Growth Factor Receptor 2 (HER2) among Women with Invasive Breast Cancer in California, 1999–2004. Breast J. 2009 , 15 , 593–602. [ Google Scholar ] [ CrossRef ]
• Carey, L.A.; Perou, C.M.; Livasy, C.A.; Dressler, L.G.; Cowan, D.; Conway, K.; Karaca, G.; Troester, M.A.; Tse, C.K.; Edmiston, S.; et al. Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study. JAMA 2006 , 295 , 2492–2502. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• O’Brien, K.M.; Cole, S.R.; Tse, C.K.; Perou, C.M.; Carey, L.A.; Foulkes, W.D.; Dressler, L.G.; Geradts, J.; Millikan, R.C. Intrinsic breast tumor subtypes, race, and long-term survival in the Carolina Breast Cancer Study. Clin. Cancer Res. 2010 , 16 , 6100–6110. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Maisonneuve, P.; Disalvatore, D.; Rotmensz, N.; Curigliano, G.; Colleoni, M.; Dellapasqua, S.; Pruneri, G.; Mastropasqua, M.G.; Luini, A.; Bassi, F.; et al. Proposed new clinicopathological surrogate definitions of luminal A and luminal B (HER2-negative) intrinsic breast cancer subtypes. Breast Cancer Res. 2014 , 16 , R65. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cheang, M.C.U. Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin. Cancer Res. 2008 , 14 , 1368–1376. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Goldhrisch, E.P.; Winer, A. Panel members. Personalizing the treatment of women with early breast cancer: Highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2013. Ann. Oncol. 2013 , 24 , 2206–2223. [ Google Scholar ] [ CrossRef ]
• Prat, A.; Pineda, E.; Adamo, B.; Galván, P.; Fernandez-Martinez, A.; Gaba, L.; Díez, M.; Viladot, M.; Arance, A.; Munoz, M. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast 2015 , 24 , S26–S35. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Vuong, D.; Simpson, P.T.; Green, B.; Cummings, M.C.; Lakhani, S.R. Molecular classification of breast cancer. Virchows Arch. 2014 , 465 , 1–14. [ Google Scholar ] [ CrossRef ]
• AJCC (American Joint Committee on Cancer). Cancer Staging Manual , 8th ed.; 3rd printing; Amin, M.B., Edge, S.B., Greene, F.L., Eds.; Springer: Chicago, IL, USA, 2018. [ Google Scholar ]
• Hammond, M.E.H.; Hayes, D.F.; Dowsett, M.; Allred, D.C.; Hagerty, K.L.; Badve, S.; Fitzgibbons, P.L.; Francis, G.; Goldstein, N.S.; Hayes, M.; et al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer. J. Clin. Oncol. 2010 , 28 , 2784–2795. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wolff, A.; Hammond, M.E.H.; Hicks, D.G.; Dowsett, M.; McShane, L.M.; Allison, K.H.; Allred, D.C.; Bartlett, J.M.; Bilous, M.; Fitzgibbons, P.; et al. Recommendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Update. J. Clin. Oncol. 2013 , 31 , 3997–4013. [ Google Scholar ] [ CrossRef ]
• Elston, C.; Ellis, I. pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: Experience from a large study with long-term follow-up. Histopathology 1991 , 19 , 403–410. [ Google Scholar ] [ CrossRef ]
• Bloom, H.J.G.; Richardson, W.W. Histological grading and prognosis in breast cancer: A study of 1409 cases of which 359 have been followed for 15 years. Br. J. Cancer 1957 , 11 , 359. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E.; Dees, E.C.; Perez, E.A.; Olson, J.A.; et al. Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2015 , 373 , 2005–2014. [ Google Scholar ] [ CrossRef ]
• Stemmer, S.M.; Steiner, M.; Rizel, S.; Soussan-Gutman, L.; Ben-Baruch, N.; Bareket-Samish, A.; Geffen, D.B.; Nisenbaum, B.; Isaacs, K.; Fried, G.; et al. Clinical outcomes in patients with node-negative breast cancer treated based on the recurrence score results: Evidence from a large prospectively designed registry. NPJ Breast Cancer 2017 , 3 , 33. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Weiss, A.; Mac Gregor, M.C.; Lichtensztajn, D.; Yi, M.; Tadros, A.; Hortobagyi, G.N.; Giordano, S.H.; Hunt, K.K.; Mittendorf, E.A. Validation Study of the American Joint Committee on Cancer Eighth Edition Prognostic Stage Compared with the Anatomic Stage in Breast Cancer. JAMA Oncol. 2018 , 4 , 203–209. [ Google Scholar ] [ CrossRef ]
• Abdel-Rahman, O. Validation of the 8th AJCC prognostic staging system for breast cancer in a population-based setting. Breast Cancer Res. Treat. 2017 , 168 , 269–275. [ Google Scholar ] [ CrossRef ]
• Colomer, R.; Aranda, F.; Albanell, J.; García-Caballero, T.; Ciruelos, E.; López-García, M.; Cortés, J.; Rojo, F.; Martín, M.; Palacios-Calvo, J. Biomarkers in breast cancer: A consensus statement by the Spanish Society of Medical Oncology and the Spanish Society of Pathology. Clin. Transl. Oncol. 2017 , 20 , 815–826. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Li, Y.; Yang, D.; Yin, X.; Zhang, X.; Huang, J.; Wu, Y.; Wang, M.; Yi, Z.; Li, H.; Li, H.; et al. Clinicopathological Characteristics and Breast Cancer–Specific Survival of Patients with Single Hormone Receptor–Positive Breast Cancer. JAMA Netw. Open 2020 , 3 , e1918160. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Duffy, M.; Harbeck, N.; Nap, M.; Molina, R.; Nicolini, A.; Senkus, E.; Cardoso, F. Clinical use of biomarkers in breast cancer: Updated guidelines from the European Group on Tumor Markers (EGTM). Eur. J. Cancer 2017 , 75 , 284–298. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Nasrazadani, A.; Thomas, R.A.; Oesterreich, S.; Lee, A.V. Precision Medicine in Hormone Receptor-Positive Breast Cancer. Front. Oncol. 2018 , 8 , 144. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Tse, L.A.; Li, M.; Chan, W.-C.; Kwok, C.-H.; Leung, S.-L.; Wu, C.; Yu, I.T.-S.; Yu, W.-C.; Lao, X.Q.; Wang, X.; et al. Familial Risks and Estrogen Receptor-Positive Breast Cancer in Hong Kong Chinese Women. PLoS ONE 2015 , 10 , e0120741. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Konan, H.-P.; Kassem, L.; Omarjee, S.; Surmieliova-Garnès, A.; Jacquemetton, J.; Cascales, E.; Rezza, A.; Trédan, O.; Treilleux, I.; Poulard, C.; et al. ERα-36 regulates progesterone receptor activity in breast cancer. Breast Cancer Res. 2020 , 22 , 50. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Obr, A.E.; Edwards, D.P. The biology of progesterone receptor in the normal mammary gland and in breast cancer. Mol. Cell. Endocrinol. 2012 , 357 , 4–17. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, J.-R.; Zhao, Y.; Zhou, X.-P.; Qin, X. Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis. Biomed. Pharmacother. 2019 , 121 , 109647. [ Google Scholar ] [ CrossRef ]
• Patani, N.; Martin, L.-A.; Dowsett, M. Biomarkers for the clinical management of breast cancer: International perspective. Int. J. Cancer 2012 , 133 , 1–13. [ Google Scholar ] [ CrossRef ]
• Freelander, A.; Brown, L.; Parker, A.; Segara, D.; Portman, N.; Lau, B.; Lim, E. Molecular Biomarkers for Contemporary Therapies in Hormone Receptor-Positive Breast Cancer. Genes 2021 , 12 , 285. [ Google Scholar ] [ CrossRef ]
• Kohler, B.A.; Sherman, R.L.; Howlader, N.; Jemal, A.; Ryerson, A.B.; Henry, K.A.; Boscoe, F.P.; Cronin, K.A.; Lake, A.; Noone, A.-M.; et al. Annual Report to the Nation on the Status of Cancer, 1975-2011, Featuring Incidence of Breast Cancer Subtypes by Race/Ethnicity, Poverty, and State. J. Natl. Cancer Inst. 2015 , 107 , djv048. [ Google Scholar ] [ CrossRef ]
• Kontani, K.; Kuroda, N.; Hashimoto, S.-I.; Murazawa, C.; Norimura, S.; Tanaka, H.; Ohtani, M.; Fujiwara-Honjo, N.; Kushida, Y.; Date, M.; et al. Clinical usefulness of human epidermal growth factor receptor-2 extracellular domain as a biomarker for monitoring cancer status and predicting the therapeutic efficacy in breast cancer. Cancer Biol. Ther. 2013 , 14 , 20–28. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, H.-A.; Lee, J.K.; Kim, E.-K.; Seol, H.; Noh, W.C. Serum human epidermal growth factor receptor 2 levels as a real-time marker for tumor burden in breast cancer patients. J. Surg. Oncol. 2013 , 109 , 421–425. [ Google Scholar ] [ CrossRef ]
• Furrer, D.; Paquet, C.; Jacob, S.; Diorio, C. The Human Epidermal Growth Factor Receptor 2 (HER2) as a Prognostic and Predictive Biomarker: Molecular Insights into HER2 Activation and Diagnostic Implications. Cancer Progn. 2018 . [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Iqbal, N.; Iqbal, N. Human Epidermal Growth Factor Receptor 2 (HER2) in Cancers: Overexpression and Therapeutic Implications. Mol. Biol. Int. 2014 , 2014 , 852748. [ Google Scholar ] [ CrossRef ]
• Nishimura, R.; Osako, T.; Okumura, Y.; Hayashi, M.; Toyozumi, Y.; Arima, N. Ki-67 as a prognostic marker according to breast cancer subtype and a predictor of recurrence time in primary breast cancer. Exp. Ther. Med. 2010 , 1 , 747–754. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• de Azambuja, E.; Cardoso, F.; De Castro, G.; Colozza, M.; Mano, M.S.; Durbecq, V.; Sotiriou, C.; Larsimont, D.; Piccart-Gebhart, M.; Paesmans, M. Ki-67 as prognostic marker in early breast cancer: A meta-analysis of published studies involving 12,155 patients. Br. J. Cancer 2007 , 96 , 1504–1513. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pathmanathan, N.; Balleine, R.L.; Jayasinghe, U.W.; Bilinski, K.L.; Provan, P.J.; Byth, K.; Bilous, A.M.; Salisbury, E.L.; Boyages, J. The prognostic value of Ki67 in systemically untreated patients with node-negative breast cancer. J. Clin. Pathol. 2014 , 67 , 222–228. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Billgren, A.-M.; Rutqvist,, L.E.; Tani, E.; Wilking, N.; Fornander, T.; Skoog, L.A.M. Proliferating Fraction during Neoadjuvant Chemotherapy of Primary Breast Cancer in Relation to Objective Local Response and Relapse-free Survival. Acta Oncol. 1999 , 38 , 597–601. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• González-Vela, M.C.; Garijo, M.F.; Fernández, F.; Val-Bernal, J.F. MIB1 proliferation index in breast infiltrating carcinoma: Com-parison with other proliferative markers and association with new biological prognostic factors. Histol. Histopathol. 2001 , 16 , 399–406. [ Google Scholar ] [ CrossRef ]
• Caly, M.; Genin, P.; Al Ghuzlan, A.; Elie, C.; Fréneaux, P.; Klijanienko, J.; Rosty, C.; Sigal-Zafrani, B.; Vincent-Salomon, A.; Douggaz, A.; et al. Analysis of correlation between mitotic index, MIB1 score and S-phase fraction as proliferation markers in invasive breast carcinoma. Methodological aspects and prognostic value in a series of 257 cases. Anticancer. Res. 2004 , 24 , 3283–3288. [ Google Scholar ]
• Li, Z.; Yin, S.; Zhang, L.; Liu, W.; Chen, B. Prognostic value of reduced E-cadherin expression in breast cancer: A meta-analysis. Oncotarget 2017 , 8 , 16445–16455. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Horne, H.N.; Oh, H.; Sherman, M.E.; Palakal, M.; Hewitt, S.M.; Schmidt, M.K.; Milne, R.L.; Hardisson, D.; Benitez, J.; Blomqvist, C.; et al. E-cadherin breast tumor expression, risk factors and survival: Pooled analysis of 5933 cases from 12 studies in the Breast Cancer Association Consortium. Sci. Rep. 2018 , 8 , 6574. [ Google Scholar ] [ CrossRef ]
• Qureshi, H.S.; Lindenm, M.D.; Divine, G.; Rajum, U.B. E-cadherin status in breast cancer correlates with histologic type but does not correlate with established prognostic parameters. Am. J. Clin. Pathol. 2006 , 125 , 377–385. [ Google Scholar ] [ CrossRef ]
• Borcherding, N.; Cole, K.; Kluz, P.; Jorgensen, M.; Kolb, R.; Bellizzi, A.; Zhang, W. Re-Evaluating E-Cadherin and β-Catenin. Am. J. Pathol. 2018 , 188 , 1910–1920. [ Google Scholar ] [ CrossRef ]
• Yang, L.; Wang, X.; Zhu, L.; Wang, H.; Wang, B.; Zhao, Q.; Wang, X. Significance and prognosis of epithelial-cadherin expression in invasive breast carcinoma. Oncol. Lett. 2018 , 16 , 1659–1665. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhou, S.-Y.; Chen, W.; Yang, S.-J.; Xu, Z.-H.; Hu, J.-H.; Zhang, H.-D.; Zhong, S.-L.; Tang, J.-H. The emerging role of circular RNAs in breast cancer. Biosci. Rep. 2019 , 39 , BSR20190621. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tran, A.M.; Chalbatani, G.M.; Berland, L.; Santos, M.C.D.L.; Raj, P.; Jalali, S.A.; Gharagouzloo, E.; Ivan, C.; Dragomir, M.P.; Calin, G.A. A New World of Biomarkers and Therapeutics for Female Reproductive System and Breast Cancers: Circular RNAs. Front. Cell Dev. Biol. 2020 , 8 , 50. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Yin, W.-B.; Yan, M.-G.; Fang, X.; Guo, J.-J.; Xiong, W.; Zhang, R.-P. Circulating circular RNA hsa_circ_0001785 acts as a diagnostic biomarker for breast cancer detection. Clin. Chim. Acta 2018 , 487 , 363–368. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jahani, S.; Nazeri, E.; Majidzadeh-A, K.; Jahani, M.; Esmaeili, R. Circular RNA; a new biomarker for breast cancer: A systematic review. J. Cell. Physiol. 2020 , 235 , 5501–5510. [ Google Scholar ] [ CrossRef ]
• Brown, J.R.; Chinnaiyan, A.M. The Potential of Circular RNAs as Cancer Biomarkers. Cancer Epidemiol. Biomark. Prev. 2020 , 29 , 2541–2555. [ Google Scholar ] [ CrossRef ]
• Al Deen, N.N.; Lanman, N.A.; Chittiboyina, S.; Lelièvre, S.; Nasr, R.; Nassar, F.; Zu Dohna, H.; AbouHaidar, M.; Talhouk, R. A risk progression breast epithelial 3D culture model reveals Cx43/hsa_circ_0077755/miR-182 as a biomarker axis for heightened risk of breast cancer initiation. Sci. Rep. 2021 , 11 , 2626. [ Google Scholar ] [ CrossRef ]
• Garber, J.E.; Goldstein, A.M.; Kantor, A.F.; Dreyfus, M.G.; Fraumeni, J.F.; Li, F.P. Follow-up study of twenty-four families with Li-Fraumeni syndrome. Cancer Res. 1991 , 51 , 6094–6097. [ Google Scholar ]
• Harris, C.C.; Hollstein, M. Clinical Implications of the p53 Tumor-Suppressor Gene. N. Engl. J. Med. 1993 , 329 , 1318–1327. [ Google Scholar ] [ CrossRef ]
• Williams, A.B.; Björn, S. P53 in the DNA-damage-repair process. Cold Spring Harb. Perspect. Med. 2016 , 6 , a026070. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dumay, A.; Feugeas, J.P.; Wittmer, E. Distinct tumor protein p53 mutants in breast cancer subgroups. Int. J. Cancer 2013 , 132 , 1227–1231. [ Google Scholar ] [ CrossRef ]
• Olivier, M.; Langerød, A.; Carrieri, P.; Bergh, J.; Klaar, S.; Eyfjord, J.; Theillet, C.; Rodriguez, C.; Lidereau, R.; Bièche, I.; et al. The clinical value of somatic TP53 gene mutations in 1794 patients with breast cancer. Clin. Cancer Res. 2006 , 12 , 1157–1167. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Petitjean, A.; Achatz, M.I.; Borresen-Dale, A.L.; Hainaut, P.; Olivier, M. TP53 mutations in human cancers: Functional selection and impact on cancer prognosis and outcomes. Oncogene 2007 , 26 , 2157–2165. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Liu, J.; Zhang, C.; Feng, Z. Tumor suppressor p53 and its gain-of-function mutants in cancer. Acta Biochim. Biophys. Sin. 2013 , 46 , 170–179. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chae, B.J.; Bae, J.S.; Lee, A.; Park, W.C.; Seo, Y.J.; Song, B.J.; Kim, J.S.; Jung, S.S. p53 as a Specific Prognostic Factor in Triple-Negative Breast Cancer. Jpn. J. Clin. Oncol. 2009 , 39 , 217–224. [ Google Scholar ] [ CrossRef ]
• Bae, S.Y.; Nam, S.J.; Jung, Y.; Lee, S.B.; Park, B.-W.; Lim, W.; Jung, S.H.; Yang, H.W.; Jung, S.P. Differences in prognosis and efficacy of chemotherapy by p53 expression in triple-negative breast cancer. Breast Cancer Res. Treat. 2018 , 172 , 437–444. [ Google Scholar ] [ CrossRef ]
• Biganzoli, E.; Coradini, D.; Ambrogi, F.; Garibaldi, J.; Lisboa, P.; Soria, D.; Green, A.; Pedriali, M.; Piantelli, M.; Querzoli, P.; et al. p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features. Jpn. J. Clin. Oncol. 2011 , 41 , 172–179. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Duffy, M.J.; Synnott, N.C.; Crown, J. Mutant p53 in breast cancer: Potential as a therapeutic target and biomarker. Breast Cancer Res. Treat. 2018 , 170 , 213–219. [ Google Scholar ] [ CrossRef ]
• Wiemer, E.A. The role of microRNAs in cancer: No small matter. Eur. J. Cancer 2007 , 43 , 1529–1544. [ Google Scholar ] [ CrossRef ]
• Iorio, M.; Croce, C.M. MicroRNA dysregulation in cancer: Diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol. Med. 2012 , 4 , 143–159. [ Google Scholar ] [ CrossRef ]
• Adhami, M.; Haghdoost, A.A.; Sadeghi, B.; Afshar, R.M. Candidate miRNAs in human breast cancer biomarkers: A systematic review. Breast Cancer 2017 , 25 , 198–205. [ Google Scholar ] [ CrossRef ]
• Fang, H.; Xie, J.; Zhang, M.; Zhao, Z.; Wan, Y.; Yao, Y. miRNA-21 promotes proliferation and invasion of triple-negative breast cancer cells through targeting PTEN. Am. J. Transl. Res. 2017 , 9 , 953–961. [ Google Scholar ] [ PubMed ]
• Rothé, F.; Ignatiadis, M.; Chaboteaux, C.; Haibe-Kains, B.; Kheddoumi, N.; Majjaj, S.; Badran, B.; Fayyad-Kazan, H.; Desmedt, C.; Harris, A.; et al. Global MicroRNA Expression Profiling Identifies MiR-210 Associated with Tumor Proliferation, Invasion and Poor Clinical Outcome in Breast Cancer. PLoS ONE 2011 , 6 , e20980. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Tang, Y.; Zhou, X.; Ji, J.; Chen, L.; Cao, J.; Luo, J.; Zhang, S. High Expression Levels of miR-21 and miR-210 Predict Unfavorable Survival in Breast Cancer: A Systemic Review and Meta-Analysis. Int. J. Biol. Markers 2015 , 30 , 347–358. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ding, Y.Z.C.; Zhang, J.; Zhang, N.; Li, T.; Fang, J.; Zhang, Y.; Zuo, F.; Tao, Z.; Tang, S.; Zhu, W.; et al. miR-145 inhibits proliferation and migration of breast cancer cells by directly or indirectly regulating TGF-β1 expression. Int. J. Oncol. 2017 , 50 , 1701–1710. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Enders, K.O.; Ng, R.L.; Vivian, S.Y.; Hong, C.J.; Candy, L.P.H.; Edmond, M.S.K.; Roberta, P.; Daniel, C.; Kent-Man, C.; Law, W.L.; et al. Circulating microRNAs as specific biomarkers for breast cancer detection. PLoS ONE 2013 , 8 , e53141. [ Google Scholar ]
• Cheng, C.; Sun, M.S.; Li, S.; Sun, X.; Yang, C.; Xi, Y.; Wang, L.; Zhang, F.; Bi, Y.; Fu, Y.; et al. Hsa-miR-139-5p inhibits proliferation and causes apoptosis associated with down-regulation of c-Met. Oncotarget 2015 , 6 , 39756–39792. [ Google Scholar ]
• Zhou, Q.; Han, L.R.; Zhou, Y.X.; Li, Y. MiR-195 Suppresses Cervical Cancer Migration and Invasion through Targeting Smad3. Int. J. Gynecol. Cancer 2016 , 26 , 817–824. [ Google Scholar ] [ CrossRef ]
• Gordon, S.; Martinez, F.O. Alternative Activation of Macrophages: Mechanism and Functions. Immunity 2010 , 32 , 593–604. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mantovani, A.; Sozzani, S.; Locati, M.; Allavena, P.; Sica, A. Macrophage polarization: Tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol. 2002 , 23 , 549–555. [ Google Scholar ] [ CrossRef ]
• Biswas, S.K.; Allavena, P.; Mantovani, A. Tumor-associated macrophages: Functional diversity, clinical significance, and open questions. Semin. Immunopathol. 2013 , 35 , 585–600. [ Google Scholar ] [ CrossRef ]
• Williams, C.B.; Yeh, E.S.; Soloff, A.C. Tumor-associated macrophages: Unwitting accomplices in breast cancer malignancy. NP J Breast Cancer 2016 , 2 , 15025. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yang, J.; Li, X.; Liu, X.; Liu, Y. The role of tumor-associated macrophages in breast carcinoma invasion and metastasis. Int. J. Clin. Exp. Pathol. 2015 , 8 , 6656–6664. [ Google Scholar ]
• Medrek, C.; Pontén, F.; Jirström, K.; Leandersson, K. The presence of tumor associated macrophages in tumor stroma as a prognostic marker for breast cancer patients. BMC Cancer 2012 , 12 , 306. [ Google Scholar ] [ CrossRef ]
• Gwak, J.M.; Jang, M.H.; Kim, D.I.; Na Seo, A.; Park, S.Y. Prognostic Value of Tumor-Associated Macrophages According to Histologic Locations and Hormone Receptor Status in Breast Cancer. PLoS ONE 2015 , 10 , e0125728. [ Google Scholar ] [ CrossRef ]
• Yuan, Z.Y.; Luo, R.Z.; Peng, R.J.; Wang, S.S.; Xue, C. High infiltration of tumor-associated macrophages in triple-negative breast cancer is associated with a higher risk of distant metastasis. Onco. Targets Ther. 2014 , 7 , 1475–1480. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhao, X.; Qu, J.; Sun, Y.; Wang, J.; Liu, X.; Wang, F.; Zhang, H.; Wang, W.; Ma, X.; Gao, X.; et al. Prognostic significance of tumor-associated macrophages in breast cancer: A meta-analysis of the literature. Oncotarget 2017 , 8 , 30576–30586. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, H.; Wang, X.; Shen, Z.; Xu, J.; Qin, J.; Sun, Y. Infiltration of diametrically polarized macrophages predicts overall survival of patients with gastric cancer after surgical resection. Gastric Cancer 2014 , 18 , 740–750. [ Google Scholar ] [ CrossRef ]
• Herrera, M.; Herrera, A.; Domínguez, G.; Silva, J.; García, V.; García, J.M.; Gómez, I.; Soldevilla, B.; Muñoz, C.; Provencio, M.; et al. Cancer-associated fibroblast and M2 macrophage markers together predict outcome in colorectal cancer patients. Cancer Sci. 2013 , 104 , 437–444. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, M. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. J. Ovarian Res. 2014 , 7 , 19. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Honkanen, T.J.; Tikkanen, A.; Karihtala, P.; Mäkinen, M.; Väyrynen, J.P.; Koivunen, J.P. Prognostic and predictive role of tumour-associated macrophages in HER2 positive breast cancer. Sci. Rep. 2019 , 9 , 10961. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Grivennikov, S.I.; Greten, F.R.; Karin, M. Immunity, inflammation, and cancer. Cell 2010 , 140 , 883–899. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guthrie, G.J.; Charles, K.A.; Roxburgh, C.S.; Horgan, P.G.; McMillan, D.C.; Clarke, S.J. The systemic inflammation-based neutro-phil-lymphocyte ratio: Experience in patients with cancer. Crit. Rev. Oncol. Hematol. 2013 , 88 , 218–230. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Huang, S.H.; Waldron, J.; Milosevic, M.; Shen, X.; Ringash, J.; Su, J.; Tong, L.; Perez-Ordonez, B.; Weinreb, I.; Bayley, A.J.; et al. Prognostic value of pretreatment circulating neutrophils, monocytes, and lymphocytes in oropharyngeal cancer stratified by human papillomavirus status. Cancer 2014 , 121 , 545–555. [ Google Scholar ] [ CrossRef ]
• Li, J.; Jiang, R.; Liu, W.-S.; Liu, Q.; Xu, M.; Feng, Q.-S.; Chen, L.-Z.; Bei, J.-X.; Chen, M.-Y.; Zeng, Y.-X. A Large Cohort Study Reveals the Association of Elevated Peripheral Blood Lymphocyte-to-Monocyte Ratio with Favorable Prognosis in Nasopharyngeal Carcinoma. PLoS ONE 2013 , 8 , e83069. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kilincalp, S.; Çoban, S.; Akinci, H.; Hamamcı, M.; Karaahmet, F.; Coşkun, Y.; Üstün, Y.; Şimşek, Z.; Erarslan, E.; Yuksel, I. Neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and mean platelet volume as potential biomarkers for early detection and monitoring of colorectal adenocarcinoma. Eur. J. Cancer Prev. 2015 , 24 , 328–333. [ Google Scholar ] [ CrossRef ]
• Proctor, M.; Morrison, D.; Talwar, D.; Balmer, S.M.; Fletcher, C.D.; O’Reilly, D.J. A comparison of inflammation-based prognostic scores in patients with cancer. A Glasgow inflammation outcome study. Eur J Cancer. 2011 , 47 , 2633–2641. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Luo, M.; Chen, Y.; Wang, Y.; Zhang, B.; Ren, Z.; Bao, L.; Wang, Y.; Wang, J.E.; Fu, Y.-X.; et al. ZMYND8 expression in breast cancer cells blocks T-lymphocyte surveillance to promote tumor growth. Cancer Res. 2020 . [ Google Scholar ] [ CrossRef ]
• Sobral-Leite, M. Cancer-immune interactions in ER-positive breast cancers: PI3K pathway alterations and tumor-infiltrating lymphocytes. Breast Cancer Res. 2019 , 21 , 90. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mouchemore, K.A.; Anderson, R.L.; Hamilton, J.A. Neutrophils, G-CSF and their contribution to breast cancer metastasis. FEBS J. 2018 , 285 , 665–679. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Azab, B.; Shah, N.; Radbel, J.; Tan, P.; Bhatt, V.; Vonfrolio, S. Pretreatment neutrophil/lymphocyte ratio is superior to plate-let/lymphocyte ratio as a predictor of long-term mortality in breast cancer patients. Med. Oncol. 2013 , 30 , 432. [ Google Scholar ] [ CrossRef ]
• Guo, W. Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for breast cancer patients: An updated meta-analysis of 17,079 individuals. Cancer Med. 2019 , 8 , 4135–4148. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Mandaliya, H.; Jones, M.; Oldmeadow, C.; Nordman, I.I.C. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): Neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl. Lung Cancer Res. 2019 , 8 , 886–894. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tan, D.; Fu, Y.; Tong, W.; Li, F. Prognostic significance of lymphocyte to monocyte ratio in colorectal cancer: A meta-analysis. Int. J. Surg. 2018 , 55 , 128–138. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mantovani, A.; Allavena, P.; Sica, A.; Balkwill, F. Cancer-related inflammation. Nature 2008 , 454 , 436–444. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Olingy, C.E.; Dinh, H.; Hedrick, C.C. Monocyte heterogeneity and functions in cancer. J. Leukoc. Biol. 2019 , 106 , 309–322. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hu, R.-J.; Liu, Q.; Ma, J.-Y.; Zhou, J.; Liu, G. Preoperative lymphocyte-to-monocyte ratio predicts breast cancer outcome: A meta-analysis. Clin. Chim. Acta 2018 , 484 , 1–6. [ Google Scholar ] [ CrossRef ]
• Goto, W.; Kashiwagi, S.; Asano, Y.; Takada, K.; Takahashi, K.; Hatano, T.; Takashima, T.; Tomita, S.; Motomura, H.; Hirakawa, K.; et al. Predictive value of lymphocyte-to-monocyte ratio in the preoperative setting for progression of patients with breast cancer. BMC Cancer 2018 , 18 , 1137. [ Google Scholar ] [ CrossRef ]
• Zou, Z.-Y.; Liu, H.-L.; Ning, N.; Li, S.-Y.; Du, X.-H.; Li, R. Clinical significance of pre-operative neutrophil lymphocyte ratio and platelet lymphocyte ratio as prognostic factors for patients with colorectal cancer. Oncol. Lett. 2016 , 11 , 2241–2248. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, X.; Zhao, W.; Yu, Y.; Qi, X.; Song, L.; Zhang, C.; Li, G.; Yang, L. Clinicopathological and prognostic significance of platelet-lymphocyte ratio (PLR) in gastric cancer: An updated meta-analysis. World J. Surg. Oncol. 2020 , 18 , 1–12. [ Google Scholar ] [ CrossRef ]
• Li, B.; Zhou, P.; Liu, Y.; Wei, H.; Yang, X.; Chen, T.; Xiao, J. Platelet-to-lymphocyte ratio in advanced Cancer: Review and meta-analysis. Clin. Chim. Acta 2018 , 483 , 48–56. [ Google Scholar ] [ CrossRef ]
• Schlesinger, M. Role of platelets and platelet receptors in cancer metastasis. J. Hematol. Oncol. 2018 , 11 , 125. [ Google Scholar ] [ CrossRef ]
• Jiang, L. Platelet releasate promotes breast cancer growth and angiogenesis via VEGF–integrin cooperative signal-ling. Br. J. Cancer 2017 , 117 , 695–703. [ Google Scholar ] [ CrossRef ]
• Kubota, S.I.; Takahashi, K.; Mano, T.; Matsumoto, K.; Katsumata, T.; Shi, S.; Tainaka, K.; Ueda, H.R.; Ehata, S.; Miyazono, K. Whole-organ analysis of TGF-β-mediated remodelling of the tumour microenvironment by tissue clearing. Commun. Biol. 2021 , 4 , 294. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Huang, X.; Song, Y.-X.; Gao, P.; Sun, J.-X.; Wang, Z.-N. High Platelet-to-Lymphocyte Ratio Predicts Poor Prognosis and Clinicopathological Characteristics in Patients with Breast Cancer: A Meta-Analysis. Bio. Med. Res. Int. 2017 , 2017 , 9503025. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cho, U.; Park, H.S.; Im, S.Y.; Yoo, C.Y.; Jung, J.H.; Suh, Y.J.; Choi, H.J. Prognostic value of systemic inflammatory markers and development of a nomogram in breast cancer. PLoS ONE 2018 , 13 , e0200936. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Koh, C.-H.; Bhoopathy, N.; Ng, K.-L.; Jabir, R.S.; Tan, G.-H.; See, M.H.; Jamaris, S.; Taib, N.A. Utility of pre-treatment neutrophil–lymphocyte ratio and platelet–lymphocyte ratio as prognostic factors in breast cancer. Br. J. Cancer 2015 , 113 , 150–158. [ Google Scholar ] [ CrossRef ]
• Morrow, M.; White, J.; Moughan, J.; Owen, J.; Pajack, T.; Sylvester, J.; Wilson, J.F.; Winchester, D. Factors Predicting the Use of Breast-Conserving Therapy in Stage I and II Breast Carcinoma. J. Clin. Oncol. 2001 , 19 , 2254–2262. [ Google Scholar ] [ CrossRef ]
• Rahman, G.A. Breast conserving therapy: A surgical technique where little can mean more. J. Surg. Tech. Case Rep. 2011 , 3 , 1–4. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cardoso, F.; Kyriakides, S.; Ohno, S.; Penault-Llorca, F.; Poortmans, P.; Rubio, I.; Zackrisson, S.; Senkus, E. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2019 , 30 , 1194–1220. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rouzier, R.; Perou, C.; Symmans, W.F.; Ibrahim, N.; Cristofanilli, M.; Anderson, K.; Hess, K.R.; Stec, J.; Ayers, M.; Wagner, P.; et al. Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy. Clin. Cancer Res. 2005 , 11 , 5678–5685. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Fisher, B.; Bryant, J.; Wolmark, N.; Mamounas, E.; Brown, A.; Fisher, E.R.; Wickerham, D.L.; Begovic, M.; DeCillis, A.; Robidoux, A.; et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J. Clin. Oncol. 1998 , 16 , 2672–2685. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, T.J.; Ho, A.Y. Radiation Therapy in the Management of Breast Cancer. Surg. Clin. N. Am. 2013 , 93 , 455–471. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Joshi, S.C.; Khan, F.A.; Pant, I.; Shukla, A. Role of Radiotherapy in Early Breast Cancer: An Overview. Int. J. Health Sci. 2007 , 1 , 259–264. [ Google Scholar ]
• Lumachi, F.; Luisetto, G.; Basso, S.M.M.; Basso, U.; Brunello, A.; Camozzi, V. Endocrine Therapy of Breast Cancer. Curr. Med. Chem. 2011 , 18 , 513–522. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tremont, A.; Lu, J.; Cole, J.T. Endocrine Therapy for Early Breast Cancer: Updated Review. Ochsner J. 2017 , 17 , 405–411. [ Google Scholar ] [ PubMed ]
• Jones, K.L.; Buzdar, A.U. A review of adjuvant hormonal therapy in breast cancer. Endocr.-Related Cancer 2004 , 11 , 391–406. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Drăgănescu, M.; Carmocan, C. Hormone Therapy in Breast Cancer. Chirurgia 2017 , 112 , 413–417. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Abe, O.; Abe, R.; Enomoto, K.; Kikuchi, K.; Koyama, H.; Masuda, H.; Nomura, Y.; Sakai, K.; Sugimachi, K.; Tominaga, T.; et al. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005 , 365 , 1687–1717. [ Google Scholar ] [ CrossRef ]
• Maximiano, S.; Magalhães, P.; Guerreiro, M.P.; Morgado, M. Trastuzumab in the Treatment of Breast Cancer. Bio. Drugs 2016 , 30 , 75–86. [ Google Scholar ] [ CrossRef ]
• Ishii, K.; Morii, N.; Yamashiro, H. Pertuzumab in the treatment of HER2-positive breast cancer: An evidence-based review of its safety, efficacy, and place in therapy. Core Évid. 2019 , 14 , 51–70. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Nguyen, X.; Hooper, M.; Borlagdan, J.P.; Palumbo, A. A Review of Fam-Trastuzumab Deruxtecan-nxki in HER2-Positive Breast Cancer. Ann. Pharmacother. 2021 . [ Google Scholar ] [ CrossRef ]
• Moreira, C.; Kaklamani, V. Lapatinib and breast cancer: Current indications and outlook for the future. Expert Rev. Anticancer. Ther. 2010 , 10 , 1171–1182. [ Google Scholar ] [ CrossRef ]
• Park, J.W.; Liu, M.C.; Yee, D.; Yau, C.; Veer, L.J.V.; Symmans, W.F.; Paoloni, M.; Perlmutter, J.; Hylton, N.M.; Hogarth, M.; et al. Adaptive Randomization of Neratinib in Early Breast Cancer. N. Engl. J. Med. 2016 , 375 , 11–22. [ Google Scholar ] [ CrossRef ]
• Pegram, M.D.; Reese, D.M. Combined biological therapy of breast cancer using monoclonal antibodies directed against HER2/protein and vascular endothelial growth factor. Semin. Oncol. 2002 , 29 , 29–37. [ Google Scholar ] [ CrossRef ]
• Riccardi, F.; Colantuoni, G.; Diana, A.; Mocerino, C.; Lauria, R.; Febbraro, A.; Nuzzo, F.; Addeo, R.; Marano, O.; Incoronato, P.; et al. Exemestane and Everolimus combination treatment of hormone receptor positive, HER2 negative metastatic breast cancer: A retrospective study of 9 cancer centers in the Campania Region (Southern Italy) focused on activity, efficacy and safety. Mol. Clin. Oncol. 2018 , 9 , 255–263. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Steger, G.G.; Gnant, M.; Bartsch, R. Palbociclib for the treatment of postmenopausal breast cancer—An update. Expert Opin. Pharmacother. 2016 , 17 , 255–263. [ Google Scholar ] [ CrossRef ]
• Shah, A.; Bloomquist, E.; Tang, S.; Fu, W.; Bi, Y.; Liu, Q.; Yu, J.; Zhao, P.; Palmby, T.R.; Goldberg, K.B.; et al. FDA Approval: Ribociclib for the Treatment of Postmenopausal Women with Hormone Receptor–Positive, HER2-Negative Advanced or Metastatic Breast Cancer. Clin. Cancer Res. 2018 , 24 , 2999–3004. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kwapisz, D. Cyclin-dependent kinase 4/6 inhibitors in breast cancer: Palbociclib, ribociclib, and abemaciclib. Breast Cancer Res. Treat. 2017 , 166 , 41–54. [ Google Scholar ] [ CrossRef ]
• Royce, M.E.; Osman, D. Everolimus in the Treatment of Metastatic Breast Cancer. Breast Cancer Basic Clin. Res. 2015 , 9 , 73–79. [ Google Scholar ] [ CrossRef ]
• Heimes, A.-S.; Schmidt, M. Atezolizumab for the treatment of triple-negative breast cancer. Expert Opin. Investig. Drugs 2018 , 28 , 1–5. [ Google Scholar ] [ CrossRef ]
• Steger, G.G.; Bartsch, R. Denosumab for the treatment of bone metastases in breast cancer: Evidence and opinion. Ther. Adv. Med. Oncol. 2011 , 3 , 233–243. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Tarantino, P.; Morganti, S.; Curigliano, G. Biologic therapy for advanced breast cancer: Recent advances and future directions. Expert Opin. Biol. Ther. 2020 , 20 , 1009–1024. [ Google Scholar ] [ CrossRef ] [ PubMed ]

Share and Cite

Łukasiewicz, S.; Czeczelewski, M.; Forma, A.; Baj, J.; Sitarz, R.; Stanisławek, A. Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review. Cancers 2021 , 13 , 4287. https://doi.org/10.3390/cancers13174287

Łukasiewicz S, Czeczelewski M, Forma A, Baj J, Sitarz R, Stanisławek A. Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review. Cancers . 2021; 13(17):4287. https://doi.org/10.3390/cancers13174287

Łukasiewicz, Sergiusz, Marcin Czeczelewski, Alicja Forma, Jacek Baj, Robert Sitarz, and Andrzej Stanisławek. 2021. "Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review" Cancers 13, no. 17: 4287. https://doi.org/10.3390/cancers13174287

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Research article
• Open access
• Published: 17 November 2020

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

• Maria Escala-Garcia 1 ,
• Anna Morra 1 ,
• Sander Canisius 1 , 2 ,
• Jenny Chang-Claude 3 , 4 ,
• Siddhartha Kar 5 , 6 ,
• Wei Zheng 7 ,
• Stig E. Bojesen 8 , 9 , 10 ,
• Doug Easton 11 , 12 ,
• Paul D. P. Pharoah 11 , 12 &
• Marjanka K. Schmidt   ORCID: orcid.org/0000-0002-2228-429X 1 , 13  

BMC Medicine volume  18 , Article number:  327 ( 2020 ) Cite this article

16k Accesses

43 Citations

2 Altmetric

Metrics details

Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM).

We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors.

Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [ P ] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors.

Conclusions

This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

Peer Review reports

Breast cancer is a heterogeneous disease with a broad variation in prognosis [ 1 ]. Providing a precise prognostication for breast cancer patients is important in order to inform them accurately about the course of the disease and to allocate them to the right treatment [ 2 ]. To date, most commonly used prognostic factors relate to tumour characteristics and the extent of the disease at the time of diagnosis [ 2 ]. Many observational studies have evaluated the association of breast cancer risk and survival with other patient characteristics and lifestyle-related risk factors [ 3 , 4 , 5 ]. However, due to their observational nature, it is difficult for these studies to establish causation. Understanding whether or not the association between breast cancer survival and risk factors is causal might influence strategies to improve survival in breast cancer patients. In theory, randomised control trials (RCTs) provide a reliable method to evaluate the causal relationship between risk factors and survival [ 6 , 7 ], but they are often not feasible as they can be prohibitively expensive, time-consuming, and even unethical. If an RCT cannot be performed to assess the causal effect between a risk factor and the outcome of interest, methods using instrumental variables may be an alternative.

Mendelian randomisation (MR) is a popular analytical method that uses genetic variants as instrumental variables (i.e. genetic instruments). This methodology uses a genetic predictor for the risk factor. Because of the natural randomisation of alleles during meiosis, this genetic predictor will be independently distributed across a population. Theoretically, therefore, this genetic instrument is not affected by potential environmental confounding factors or by disease status. MR rests on three basic assumptions: (1) genetic variants are associated with the risk factor (relevance assumption), (2) those genetic variants are not associated with any known or unknown confounders (independence assumption), and (3) the genetic variants affect the outcome only through the risk factor (exclusion restriction assumption) [ 8 ]. Using a genetic score that combines multiple variants explaining a large R -squared of the risk factor can help reducing the probability of violating the first MR assumption and providing more powerful MR analyses. The third assumption is also known as independence from horizontal pleiotropy, which occurs when the genetic variants influence the outcome by means of other pathways independently of the risk factor [ 8 ]. Several methods and sensitivity tests exist to assess these assumptions [ 9 ].

In this study, we used MR analysis to evaluate the causal relationships between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index (BMI), height, mammographic density, menarche (age at onset), menopause (age at onset), physical activity, smoking, and type 2 diabetes mellitus (T2DM). Observational studies have provided evidence for the potential association of these risk factors and breast cancer survival, sometimes with conflicting results.

A population-based prospective study found that smoking before or after breast cancer diagnosis is associated with worse breast cancer survival [ 10 ]. Another meta-analysis of cohort studies concluded that current smoking is associated with worse breast cancer-specific survival compared to never smoking in breast cancer patients [ 11 ]. Obesity (BMI of ≥ 30.0) has been associated with worse breast cancer survival in a meta-analysis and systematic review [ 12 ]. In another review, obesity was associated with worse breast cancer prognosis for women of all ages [ 13 ]. For T2DM, a retrospective study of breast cancer patients found that diabetes was independently associated with poorer breast cancer prognosis [ 14 ]. In a population-based study, breast cancer-specific mortality was higher among women with diabetes compared to non-diabetic patients [ 15 ]. In relation to menstrual risk factors, a population-based study showed that early age at menarche was significantly associated with poorer survival but age at menopause did not have a significant impact [ 16 ]. The relationship between mammographic density and breast cancer survival has been studied in several cohort studies, but results have been inconclusive [ 17 , 18 , 19 ]. For other factors such as physical activity, the evidence is also not clear: in an RCT with an 8-year follow-up, no significant difference in disease-free survival was found between an exercise group and a usual care group [ 20 ]. To date, there is no evidence for an association between height or post-diagnosis alcohol consumption and breast cancer survival [ 21 ].

Our hypothesis was that some of these risk factors, for which there is evidence of an association with breast cancer survival based on observational data, might have a causal association with breast cancer-specific survival. We also aimed to investigate whether we could observe—or refute—an effect for the risk factors for which the association is not clear. We therefore performed a two-sample MR analysis using genetic variants and risk factor association summary estimates from the GWAS Catalog [ 22 ] and breast cancer survival summary estimates from the Breast Cancer Association Consortium (BCAC) cohort [ 23 ].

Selection of risk factors

We first considered the full list of breast cancer risk factors provided on the Cancer Research UK site [ 33 ] as of January 2020 (Additional file  1 : Table S1). From this list of 25 factors, we identified nine factors for which genome-wide association study (GWAS) data were available. Only GWASs that could be directly downloaded from GWAS Catalog [ 22 ] into TwoSampleMR [ 34 ] R package were considered. If there were multiple GWAS for one risk factor, we selected the study with the largest sample size from those that were predominantly of European ancestry (Table  1 ). We considered only genome-wide significant variants ( P  < 5 × 10 −8 ) to ensure that the association with the risk factor was robust (first MR assumption). Only single-nucleotide polymorphisms (SNPs) were considered as the reference panel did not include other types of variants. Variants correlated with the most significant SNPs were removed so that only uncorrelated variants remained in the analysis ( r 2  < 0.001). We calculated a priori power to detect an association at a significant level of 0.05 for each risk factor using the tool ( https://sb452.shinyapps.io/power ) [ 35 ]. We used the number of events ( n  = 7054) as sample size.

Breast cancer survival and genetic data

The breast cancer survival data was obtained from the Breast Cancer Association Consortium (BCAC). We analysed clinic-pathological data (database version 12) and genotype data from the OncoArray [ 36 ] and iCOGS arrays [ 37 ]. The analysis included 86,627 female patients of European ancestry diagnosed at age > 18 years with invasive breast cancer of any stage. The dataset included 7054 breast cancer-specific deaths. A total of 59,378 patients (4246 deaths) had ER-positive disease, and 13,692 (1733 deaths) had ER-negative disease. Genotypes for variants not present on the arrays were imputed using the Haplotype Reference Consortium [ 38 ] as reference panel. Details about the genotyping, sample quality control, and imputation procedure have been described previously [ 36 , 39 ]. Our analyses were based on SNPs that were imputed with imputation r 2  > 0.7 and had minor allele frequency > 0.01 in at least one of the two datasets (iCOGS or OncoArray).

Breast cancer survival estimates

We took the SNPs referred to in Table  1 as genetic instruments for each of the nine risk factors. For every SNP, we performed survival analyses to obtain survival estimates as described previously [ 23 ]. The analyses included the full OncoArray and iCOGS datasets. Time at risk was calculated from the date of diagnosis with left truncation for prevalent cases. Follow-up was right censored on the date of death, last date known alive if death did not occur, or at 15 years after diagnosis, whichever came first [ 39 ]. We estimated the association between the genetic instruments and breast cancer-specific survival using Cox proportional hazards regression [ 40 ]. The models were stratified by study and included the first two ancestry informative principal components, based on the genotyping array data as previously described, to adjust for population structure [ 36 , 37 ]. We analysed the OncoArray and iCOGS datasets separately and then combined the estimates using fixed-effect meta-analyses [ 39 ]. Analyses were carried out for all invasive breast cancer and for estrogen receptor (ER)-positive and ER-negative disease separately. Additional file  2 : Tables S1-S9 provides the full list of SNPs used and the corresponding estimates for the per-allele risk factor effect sizes and the per-allele survival log (hazard ratios).

MR statistical analyses and sensitivity diagnostics

We used the TwoSampleMR [ 34 ] R package to perform the two-sample MR analyses. We obtained the genetic instruments for the risk factors (MR-Base NHGRI-EBI GWAS Catalog [ 22 ], 29 August 2019 update), harmonised the SNP effects so they corresponded to the same allele for the risk factor and survival associations, and performed the sensitivity tests. We estimated the causal relationships between each of the sets of SNPs for the nine risk factors and breast cancer-specific survival using the inverse-variance weighted (IVW) method. We performed the analyses for all invasive breast cancer, ER-positive, and ER-negative separately. The association of BMI with breast cancer-specific survival was previously evaluated in an earlier, smaller version of the BCAC dataset ( n  = 36,210) [ 41 ]. In this analysis, we included more patients, updated follow-up, and a larger BMI GWAS genetic instrument. It has been suggested that the potential negative effect of BMI on survival is especially relevant in postmenopausal women [ 12 ]. Therefore, we also tested whether the BMI associations differed between pre- (age at diagnosis under 50 years, n  = 27,009 with 2680 breast cancer-specific deaths) and postmenopausal women (age at diagnosis 50 years or older, n  = 59,617 with 4374 breast cancer-specific deaths). Inclusion of even a small percentage of a different ethnic group can affect the interpretation and validity of the causal estimates [ 42 ]. Because the genetic instrument that we used for BMI had 19% of non-European participants, we performed an additional analysis using the BMI European-specific summary estimates from the same GWAS available at the author’s supplementary material [ 25 ] (61 SNPs after filtering, Additional file 2 : Table S10).

IVW assumes that none of the variants exhibit horizontal pleiotropy, which may not be true in practice. Therefore, we also used the MR-Egger regression method that allows variants to demonstrate unbalanced pleiotropic associations. That is, MR-Egger regression relaxes the requirement of no horizontal pleiotropy provided that the pleiotropic effects are not proportional to the effects of the variants on the risk factors of interest [ 8 , 9 ]. In comparison to the IWM, the MR-Egger method’s intercept is not constrained to zero and provides a statistical test of the extent to which this intercept differs from zero as a measure of unbalanced pleiotropic effects.

For the risk factors with a significant association based on the IVW method (false discovery rate [FDR] < 0.05), we ran the following sensitivity analyses: heterogeneity tests, funnel plots, and leave-one-out tests. To assess the robustness of the results of the IVW method, we applied other MR methods (simple mode, weighted median, and weighted mode). We also tested all associations by performing the analysis using a multivariable model. In the multivariable model, we used imputed phenotypes [ 43 ] and adjusted for the following known prognostic factors: age of the patients at diagnosis; tumour size; node status; distant metastasis status; grade; ER-, progesterone receptor, and HER2-status; and (neo) adjuvant chemotherapy, adjuvant anti-hormone therapy, and adjuvant trastuzumab. Because breast cancer survival can differ on the short or longer term, we also assessed whether or not the associations would hold for the 5-year horizon, which is typically used in breast cancer prognostication [ 44 ]. For this analysis, we reduced the follow-up time from 15 to 10 years ( n  = 85,470 with 6147 breast cancer-specific deaths) and 5 years ( n  = 79,183 with 3573 breast cancer-specific deaths). Both in the multivariable model and the shorter follow-up analyses, we performed the MR analyses separately for OncoArray and iCOGS datasets and meta-analysed the results.

Relationships between BMI, T2DM, and breast cancer survival

To ensure that the effects of BMI and T2DM were independent, we identified SNPs that overlapped between the genetic instruments for these risk factors. Two SNPs, rs7144011 and rs7903146, were present in both the BMI and T2DM instrumental variables, and 12 (six pairs) SNPs were in linkage disequilibrium (LD): rs2972144, rs4072096, rs1801282, rs1899951, rs2112347, rs2307111, rs4715210, rs72892910, rs244415, rs889398, rs6059662, and rs6142096. We removed those 14 SNPs from the analyses to reduce the likelihood of horizontal pleiotropy. To further isolate the association of T2DM alone, we performed a multivariable MR model [ 45 ] by additionally including the genetically predicted BMI score as a covariate in the analyses of T2DM.

We found a significant association between genetic liability to T2DM and breast cancer-specific survival ( P  < 0.05, Table  2 ). For all breast cancers, T2DM was associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.04–1.18, P value [ P ] = 0.003, FDR = 0.023) (Fig.  1 and Table  2 ). T2DM was also associated with worse breast cancer-specific survival when restricting to ER-positive cases. The effect in the ER-positive subtype was consistent (HR = 1.09, CI = 1.01–1.18, P  = 0.036, FDR = 0.324) with the effect in all breast cancers. We did not observe associations at FDR < 0.05 (Table  2 ) between survival, for all breast cancer or by ER-subtype, and any of the other risk factors: alcohol consumption, BMI, height, mammographic density, menarche, menopause, physical activity, and smoking. The estimates we obtained from the models adjusted by other known prognostic factors (Additional file 1 : Table S2) were comparable to the initial unadjusted analyses for all risk factors. Under the current sample size of our study ( n  = 86,627 and 7054 events), the power to detect a causal association varied considerably between risk factors (Additional file 1 : Table S3). The estimated power was the largest for age at menopause and lowest for physical activity.

Effect of the nine breast cancer risk factors on breast cancer-specific survival in all breast cancers. The y -axis shows the −log 10 ( P value) effect for the association. The x -axis corresponds to log (hazard ratio) effect for each of the traits on breast cancer survival. The risk factors with false discovery rate (FDR) < 0.05 are coloured in red; the size of the circle is proportional to the −log 10 (FDR)

Genetic association between BMI by menopausal status and breast cancer-specific survival

We found no association between BMI and breast cancer-specific survival in any of the analysed subtypes, nor by menopausal status ( P  > 0.05): premenopausal (HR = 1.06, CI = 0.78–1.44, P  = 0.710) or postmenopausal women (HR = 1.02, CI = 0.80–1.30, P  = 0.899). The estimate using the European-specific BMI genetic instrument (HR = 1.14, CI = 0.94–1.38, P  = 0.174) was also not significant.

Genetic association between T2DM and breast cancer-specific survival

The HR estimate for T2DM and survival among all invasive breast cancers (HR = 1.10) was higher than that for either ER-subtype individually (ER-positive: HR = 1.09; ER-negative: HR = 1.09). This reflected the fact that the patients without ER-status information ( n  = 13,557) had a larger risk estimate (HR = 1.19, CI = 1.02–1.39, P  = 0.023).

To further validate the association between T2DM and breast cancer-specific survival, we performed the analysis using a shorter follow-up. The results were significant and similar to the main analysis both for 10-year (HR = 1.12, CI = 1.05–1.19, P  = 0.0006) and for 5-year follow-up (HR = 1.13, CI = 1.04–1.23, P  = 0.005). We also tested the association in a model adjusted by other known prognostic factors. The association of T2DM with breast cancer-specific survival in the adjusted model was still significant (HR = 1.10, CI = 1.02–1.18, P  = 0.013), and the effect size remained similar to the main T2DM analysis (HR = 1.10, CI = 1.04–1.18, P  = 0.003). Finally, we tried to replicate the result using another large and well-powered GWAS, i.e. the T2DM summary estimates from the DIAGRAM GWAS which is a large meta-analysis of 32 studies comprising data for 898,130 individuals (74,124 T2DM cases and 824,006 controls) of European ancestry [ 46 ]. The genetic instrument for this dataset included 152 SNPs (12 SNPs overlapping with the T2DM genetic instrument we initially used, Additional file 2 : Table S11). The association of T2DM with breast cancer-specific survival using the replication dataset was significant (HR = 1.18, CI = 1.04–1.33, P  = 0.009) and similar to the initial result (HR = 1.10).

Association between T2DM and breast cancer-specific survival with BMI adjustment

To explore the potential confounding effect of BMI with T2DM, we performed an analysis adjusting for genetically predicted BMI. The effect of BMI in this analysis was not significant (HR = 1.02, CI = 0.85–1.24, P  = 0.809), and the effect of T2DM on survival was similar (HR = 1.10, CI = 1.04–1.17, P  = 0.002) to the main T2DM analysis (HR = 1.10, CI = 1.04–1.18, P  = 0.003).

Causal association between T2DM and breast cancer-specific survival

We used different variations of the MR method to assess possible violations of the MR assumptions. Figure  2 shows that the range of MR methods used (simple mode, weighted median, and weighted mode) to assess the sensitivity of the findings all gave similar effect size estimates. Additionally, there was no evidence of pleiotropy based on the MR-Egger intercept test (MR-Egger intercept = 0.003, P  = 0.68, Fig.  2 ). In analyses using funnel plot (Additional file 1 : Figure S1) and a leave-one-out test (Additional file 1 : Figure S2), there was no indication for violation of the assumptions, nor that the association was driven by any particular SNP.

Plot showing the effect sizes of the SNP effects on breast cancer-specific survival for all breast cancers ( y -axes) and the SNP effects on T2DM ( x -axes) with 95% confidence intervals. Each dot represents one of the 95 SNPs used in the T2DM genetic instrument. The slopes indicate the estimate for each of the five different MR tests

We performed a Mendelian randomisation analysis to explore the potential causal effects on breast cancer-specific survival of nine established risk factors for breast cancer: alcohol consumption, BMI, height, mammographic density, menarche, menopause, physical activity, smoking, and T2DM. We used survival estimates from 86,627 European breast cancer patients with invasive breast cancer (by far the largest such dataset) and summary data from the GWAS Catalog for the nine risk factors. We used the IVW method to estimate causal effects and performed a wide range of sensitivity analyses to test the robustness of our findings.

Our analysis showed an association between genetic liability to T2DM and worse breast cancer-specific survival. The IVW method result was consistent with the results of other complementary MR-methods, and the performed sensitivity analyses did not give any statistical indication for violations of the MR assumptions. Additionally, the T2DM GWAS used was reasonably powered, with an estimated heritability of ~ 20% [ 32 ], supporting the relevance assumption. There was no evidence that the SNPs were associated with breast cancer survival (exclusion restriction). Finally, the association remained significant when adjusting for other known prognostic factors and when shortening the follow-up time to 10 and 5 years.

Because obesity and T2DM share some biological features such as elevated insulin levels, hypertension, and chronic inflammation [ 47 ] and since higher BMI has been associated with increased incidence of T2DM [ 48 ], we explored a possible interaction between the two risk factors. First, we ensured that there were no common SNPs between the T2DM and BMI genetic instruments or SNPs in LD that could be driving the association. Second, we performed BMI-adjusted analyses which also showed that the association was being driven by T2DM and not by BMI.

Earlier literature suggests an association between diabetes and worse breast cancer-specific survival [ 49 , 50 , 51 ]. There is no clear evidence linking diabetes to any particular ER-status specific breast cancer subtype [ 52 ] that could explain the poorer survival in women with T2DM. The increased mortality in patients with T2DM might be explained by the effect of insulin resistance or hyperinsulinemia, since breast cancer cells might have a selective growth advantage because of insulin receptor overexpression [ 53 , 54 ]. However, to our knowledge, no functional studies to evaluate this have yet been carried out. An important point to consider when interpreting the results is that, when using a binary risk factors such as T2DM, the genetic instrument estimate will only represent the average causal effect of the exposure in a fraction of the studied population (named “genetic compliers”). Additionally, the latter would only be true assuming that the monocity assumption is plausible, which means that increasing number of alleles for an individual would increase (or maintain constant) the risk of having T2DM [ 55 ].

All the other risk factors gave null results. Some of these may reflect the fact that there is no true association, but others may be underpowered since the fraction of variation of the risk factor explained by the genetic instrument was too small. The heritability explained by identified SNPs, and hence the power of the genetic instruments, varies substantially between risk factors, e.g. ~ 20% for T2DM [ 32 ] versus only 1% for the mammographic density GWAS [ 27 ]. In addition, we only kept genome-wide significant SNPs and dropped all SNPs in LD or with low imputation quality in the BCAC dataset, so the explained variation that we could utilise was smaller. As GWAS become larger and more powerful genetic instruments are available, it may be possible to find associations that could not be identified here. However, for those risk factors with a predicted small genetic component (e.g. physical activity), their association with breast cancer survival might not be assessable using an MR framework [ 8 ]. A potential limitation of our study is that some patients in the breast cancer survival dataset were also included in the GWASs for the risk factors, mammographic density (~ 2.5% overlap) and age at menarche (~ 27%) and menopause (~ 21%). However, because the genetic instruments of age at menarche and menopause were relatively strong and there was little overlap for mammographic density, we may expect the bias caused by patient overlap to be small [ 56 ]. Finally, another potential reason for which we did not observe association for some risk factors might be due to selection bias. This type of collider bias can lead to an under- or overidentification of genetic risk factors for breast cancer survival due to a relationship between the genetic risk factor concerned and breast cancer incidence [ 57 ]. This could be the case for BMI, age at menopause and menarche, or height, which have been causally associated with breast cancer risk [ 58 ]. For other risk factors such as T2DM or smoking, MR studies of incidence could not provide evidence for a causal association [ 59 , 60 ], which makes these genetic instruments less likely to be affected by selection bias.

To further explore the link between BMI and breast cancer survival, we also tested separately for pre- and postmenopausal status, but there was no indication for an association in any of the menopausal groups. Despite the evidence for an association between BMI and breast cancer survival from observational studies [ 12 , 13 ], our analysis on BMI and breast cancer-specific survival did not confirm this. A possible explanation is that obesity is associated with other comorbid conditions [ 48 ] that lead to poorer overall, but no breast cancer-specific survival. Additionally, it has been suggested that obese patients might receive suboptimal chemotherapy treatment compared to regular weight women [ 61 ] and tumours are usually detected at a later stage in obese patients [ 62 ]. This would, if insufficiently corrected for, lead to an association between high BMI and worse breast cancer-specific survival in observational, but not in MR, studies. The different observations of the relationship between BMI and survival from MR versus observational studies resemble those of genetic BMI and breast cancer risk [ 63 ], which were also deviant from epidemiological studies. To date, there is not a clear answer as to whether and how high BMI directly influences the biology of cancer [ 64 ].

From a clinical point of view, our analysis suggests that genetic liability to T2DM may contribute to variation in breast cancer outcomes in women of European ancestry. Such a genetic predictor might be included in prognostication models aimed at identifying women most likely to benefit from specific interventions. Furthermore, even though T2DM has a genetic component, it is also influenced by environmental and lifestyle factors and is potentially preventable [ 65 ]. Although our study does not address this directly, it seems sensible to recommend intensified management of T2DM, including lifestyle changes, in breast cancer patients.

The main strength of our study is the use of the biggest breast cancer dataset available so far and the use of SNPs as genetic instruments to reduce potential confounding. Despite including more than 7000 breast cancer-specific deaths in the analyses, our study was not well powered especially for the analysis within the subset of ER-negative tumours (as indicated by the broad confidence intervals). Additional findings might be possible when there are larger sample sizes available and a more complete follow-up. We also lacked power to detect associations for certain risk factors that had only a handful of SNPs in their genetic instruments such as mammographic density and physical activity. Finally, our results are applicable to women of European ancestry only. In order to be able to generalise these findings to other ancestry groups, larger breast cancer datasets are needed for the other ethnicities.

This two-sample MR analysis suggests that genetic liability to T2DM might be a cause of reduced breast cancer-specific survival. Our study provides further evidence for the importance of promoting a healthier lifestyle to improve survival in breast cancer patients.

Availability of data and materials

Not applicable.

Abbreviations

Body mass index

Breast Cancer Association Consortium

Confidence interval

Estrogen receptor

False discovery rate

Genome-wide association study

Hazard ratio

Inverse-variance weighted

Linkage disequilibrium

• Mendelian randomisation

Randomised control trial

Single-nucleotide polymorphism

Type 2 diabetes mellitus

Polyak K. Heterogeneity in breast cancer. J Clin Invest. 2011;121:3786–8.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Phung MT, Tin Tin S, Elwood JM. Prognostic models for breast cancer: a systematic review. BMC Cancer. 2019;19:230.

Article   PubMed   PubMed Central   Google Scholar  

Hellmann SS, Thygesen LC, Tolstrup JS, Grønbaek M. Modifiable risk factors and survival in women diagnosed with primary breast cancer: results from a prospective cohort study. Eur J Cancer Prev. 2010;19:366–73.

Article   PubMed   Google Scholar  

Barnett GC, Shah M, Redman K, Easton DF, Ponder BAJ, Pharoah PDP. Risk factors for the incidence of breast cancer: do they affect survival from the disease? J Clin Oncol. 2008;26:3310–6.

Reeves GK, Patterson J, Vessey MP, Yeates D, Jones L. Hormonal and other factors in relation to survival among breast cancer patients. Int J Cancer. 2000;89:293–9.

Article   CAS   PubMed   Google Scholar  

Hayes SC, Steele ML, Spence RR, Gordon L, Battistutta D, Bashford J, et al. Exercise following breast cancer: exploratory survival analyses of two randomised, controlled trials. Breast Cancer Res Treat. 2018;167:505–14.

Stagl JM, Lechner SC, Carver CS, Bouchard LC, Gudenkauf LM, Jutagir DR, et al. A randomized controlled trial of cognitive-behavioral stress management in breast cancer: survival and recurrence at 11-year follow-up. Breast Cancer Res Treat. 2015;154:319–28.

Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362.

Smith GD, Borges M-C, Bowden J, Evans DM, Haycock P, Hemani G, et al. Recent developments in Mendelian randomization studies. Curr Epidemiol Reports. 2017;4:330–45.

Article   Google Scholar  

Passarelli MN, Newcomb PA, Hampton JM, Trentham-Dietz A, Titus LJ, Egan KM, et al. Cigarette smoking before and after breast cancer diagnosis: mortality from breast cancer and smoking-related diseases. J Clin Oncol. 2016;34:1315–22.

Duan W, Li S, Meng X, Sun Y, Jia C. Smoking and survival of breast cancer patients: a meta-analysis of cohort studies. Breast. 2017;33:117–24.

Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat. 2010;123:627–35.

Picon-Ruiz M, Morata-Tarifa C, Valle-Goffin JJ, Friedman ER, Slingerland JM. Obesity and adverse breast cancer risk and outcome: mechanistic insights and strategies for intervention. CA Cancer J Clin. 2017;67:378–97.

Mu L, Zhu N, Zhang J, Xing F, Li D, Wang X. Type 2 diabetes, insulin treatment and prognosis of breast cancer. Diabetes Metab Res Rev. 2017;33:e2823.

Article   CAS   Google Scholar  

Lega IC, Austin PC, Fischer HD, Fung K, Krzyzanowska MK, Amir E, et al. The impact of diabetes on breast cancer treatments and outcomes: a population-based study. Diabetes Care. 2018;41:755–61.

Orgéas CC, Hall P, Rosenberg LU, Czene K. The influence of menstrual risk factors on tumor characteristics and survival in postmenopausal breast cancer. Breast Cancer Res. 2008;10:R107.

Olsson Å, Sartor H, Borgquist S, Zackrisson S, Manjer J. Breast density and mode of detection in relation to breast cancer specific survival: a cohort study. BMC Cancer. 2014;14:229.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Olsen AH, Bihrmann K, Jensen M-B, Vejborg I, Lynge E. Breast density and outcome of mammography screening: a cohort study. Br J Cancer. 2009;100:1205–8.

van der Waal D, Verbeek ALM, Broeders MJM. Breast density and breast cancer-specific survival by detection mode. BMC Cancer. 2018;18:386.

Courneya KS, Segal RJ, McKenzie DC, Dong H, Gelmon K, Friedenreich CM, et al. Effects of exercise during adjuvant chemotherapy on breast cancer outcomes. Med Sci Sports Exerc. 2014;46:1744–51.

Ali AMG, Schmidt MK, Bolla MK, Wang Q, Gago-Dominguez M, Castelao JE, et al. Alcohol consumption and survival after a breast cancer diagnosis: a literature-based meta-analysis and collaborative analysis of data for 29,239 cases. Cancer Epidemiol Biomark Prev. 2014;23:934–45.

Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–12.

Escala-Garcia M, Abraham J, Andrulis IL, Anton-Culver H, Arndt V, Ashworth A, et al. A network analysis to identify mediators of germline-driven differences in breast cancer prognosis. Nat Commun. 2020;11:312.

Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.

Hoffmann TJ, Choquet H, Yin J, Banda Y, Kvale MN, Glymour M, et al. A large multiethnic genome-wide association study of adult body mass index identifies novel loci. Genetics. 2018;210:499–515.

Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46:1173–86.

Lindström S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun. 2014;5:5303.

Article   PubMed   CAS   Google Scholar  

Perry JR, Day F, Elks CE, Sulem P, Thompson DJ, Ferreira T, et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature. 2014;514:92–7.

Day FR, Ruth KS, Thompson DJ, Lunetta KL, Pervjakova N, Chasman DI, et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat Genet. 2015;47:1294–303.

Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun. 2018;9:5257.

Karlsson Linnér R, Biroli P, Kong E, Meddens SFW, Wedow R, Fontana MA, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet. 2019;51:245–57.

Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9:2941.

Cancer Research UK. Risk factors for breast cancer. https://www.cancerresearchuk.org/about-cancer/breast-cancer/risks-causes/risk-factors .

Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:1–29.

Burgess S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol. 2014;43:922–9.

Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, et al. The OncoArray consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Prev. 2017;26:126–35.

Michailidou K, Lindström S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551:92–4.

Frye FL, Cucuel JP. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.

Escala-Garcia M, Guo Q, Dörk T, Canisius S, Keeman R, Dennis J, et al. Genome-wide association study of germline variants and breast cancer-specific mortality. Br J Cancer. 2019;120:647–57.

Cox DR, Hinkley DV. Theoretical Statistics. Boston: Springer US; 1974.

Guo Q, Burgess S, Turman C, Bolla MK, Wang Q, Lush M, et al. Body mass index and breast cancer survival: a Mendelian randomization analysis. Int J Epidemiol. 2017;46:1891–902.

Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2020;4:186.

van Buuren S. Flexible imputation of missing data, second edition. Chapman and Hall/CRC: Second edition. Boca Raton: CRC Press. 2019. 2018.

Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet. 2011;378:771–84.

Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251–60.

Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–13.

Jiralerspong S, Kim ES, Dong W, Feng L, Hortobagyi GN, Giordano SH. Obesity, diabetes, and survival outcomes in a large cohort of early-stage breast cancer patients. Ann Oncol. 2013;24:2506–14.

Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;9:88.

Goodwin PJ, Ennis M, Pritchard KI, Trudeau ME, Koo J, Madarnas Y, et al. Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol. 2002;20:42–51.

Lipscombe LL, Goodwin PJ, Zinman B, McLaughlin JR, Hux JE. The impact of diabetes on survival following breast cancer. Breast Cancer Res Treat. 2008;109:389–95.

Tsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. BMJ. 2015;350:g7607.

Bronsveld HK, Jensen V, Vahl P, De Bruin ML, Cornelissen S, Sanders J, et al. Diabetes and breast cancer subtypes. PLoS One. 2017;12:e0170084.

Papa V, Belfiore A. Insulin receptors in breast cancer: biological and clinical role. J Endocrinol Investig. 1996;19:324–33.

Coughlin SS, Calle EE, Teras LR, Petrelli J, Thun MJ. Diabetes mellitus as a predictor of cancer mortality in a large cohort of US adults. Am J Epidemiol. 2004;159:1160–7.

Burgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol. 2018;33:947–52.

Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40:597–608.

Paternoster L, Tilling K, Davey SG. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges. PLoS Genet. 2017;13:e1006944.

Pierce BL, Kraft P, Zhang C. Mendelian randomization studies of cancer risk: a literature review. Curr Epidemiol Reports. 2018;5:184–96.

Yuan S, Kar S, Carter P, Vithayathil M, Mason AM, Burgess S, et al. Is type 2 diabetes causally associated with cancer risk? Evidence from a two-sample Mendelian randomization study. Diabetes. 2020;69:1588–96.

Larsson SC, Carter P, Kar S, Vithayathil M, Mason AM, Michaëlsson K, et al. Smoking, alcohol consumption, and cancer: a mendelian randomisation study in UK Biobank and international genetic consortia participants. PLoS Med. 2020;17:e1003178.

Griggs JJ, Sorbero MES, Lyman GH. Undertreatment of obese women receiving breast cancer chemotherapy. Arch Intern Med. 2005;165:1267.

Cui Y, Whiteman MK, Flaws JA, Langenberg P, Tkaczuk KH, Bush TL. Body mass and stage of breast cancer at diagnosis. Int J Cancer. 2002;98:279–83.

Shu X, Wu L, Khankari NK, Shu X-O, Wang TJ, Michailidou K, et al. Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. Int J Epidemiol. 2019;48:795–806.

Azvolinsky A. Cancer prognosis: role of BMI and fat tissue. JNCI J Natl Cancer Inst. 2014;106.

Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289:76.

Download references

Acknowledgements

BCAC: We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians, and administrative staff who have enabled this work to be carried out. We acknowledge all contributors to the COGS and OncoArray study design, chip design, genotyping, and genotype analyses.

BCAC is funded by Cancer Research UK (C1287/A16563, C1287/A10118), by the European Union’s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344), and the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from the European Community’s Seventh Framework Programme under grant agreement no. 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. M.E.G was funded by the Dutch Cancer Society (grant 2015-7632).

Author information

Authors and affiliations.

Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands

Maria Escala-Garcia, Anna Morra, Sander Canisius & Marjanka K. Schmidt

Division of Molecular Carcinogenesis, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands

Sander Canisius

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

Jenny Chang-Claude

University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg (UCCH), Cancer Epidemiology Group, Hamburg, Germany

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Siddhartha Kar

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA

Copenhagen University Hospital, Copenhagen General Population Study, Herlev and Gentofte Hospital, Herlev, Denmark

Stig E. Bojesen

Copenhagen University Hospital, Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Herlev, Denmark

Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

Doug Easton & Paul D. P. Pharoah

Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands

Marjanka K. Schmidt

You can also search for this author in PubMed   Google Scholar

Contributions

M.K.S., S.C., and M.E.G. designed the study. M.E.G. performed the main data analyses and drafted the initial manuscript. A.M. performed the adjusted analysis. M.K.S., S.C., and M.E.G. interpreted the data. All authors were involved in the data collection, commented on the drafts, and approved the final version of the manuscript.

Corresponding author

Correspondence to Marjanka K. Schmidt .

Ethics declarations

Ethics approval and consent to participate.

The study was performed in accordance with the Declaration of Helsinki. Summary estimates were used from previously reported studies that followed the appropriate institutional review and patient consent procedures and followed the procedure the Data Access Coordination Committee (DACC) of BCAC ( http://bcac.ccge.medschl.cam.ac.uk/ ).

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: table s1..

List of breast cancer risk factors as indicated by Cancer Research UK. Information in this table was taken directly from: https://www.cancerresearchuk.org/about-cancer/breast-cancer/risks-causes/risk-factors (January 2020). Table S2. Comparison of the effect of nine breast cancer risk factors on breast cancer-specific survival for all breast cancers in the unadjusted model (left) and in the adjusted model (right). The model was adjusted for the known prognostic factors: age of the patients at diagnosis, tumour size, node status, distant metastasis status, grade, ER-, progesterone receptor and HER2-status and (neo) adjuvant chemotherapy, adjuvant anti-hormone therapy and adjuvant trastuzumab. HR = Hazard Ratio. CI = 95% Confidence Interval. Table S3. Power (%) estimation by a range of Hazard Ratios (HR) for the analysis of MR associations between nine breast cancer risk factors and breast cancer-specific survival in all breast cancers. Figure S1. Funnel plot for T2DM and breast cancer-specific survival. The plot shows the effect estimate (b) of a particular SNP against the SNP expected precision (1/Standard Error (SE)). Asymmetry in the funnel plot is an indication of horizontal pleiotropy. The dark and light blue lines represent the MR-Egger and Inverse variance weighted slopes respectively. Figure S2. Leave-one-out plot for T2DM and breast cancer specific-survival showing the estimate effect by sequentially dropping one SNP at a time. Each black dot in the forest plot represents the MR results (IVW method) excluding that particular SNP. The result including all SNPs is shown in red at the bottom of the plot.

Additional file 2:

SNPs used in the analyses for the nine risk factors. The risk factor estimates (beta and standard error (SE)) and breast cancer-specific survival estimates for each SNP are included. Table S1. Alcohol consumption. Table S2. Body mass index. Table S3. Height. Table S4. Mammographic density. Table S5. Menarche. Table S6. Menopause. Table S7. Physical activity. Table S8. Smoking behaviour. Table S9. Type 2 diabetes mellitus. Table S10. Body mass index European-specific. Table S11. Type 2 diabetes mellitus replicate.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Escala-Garcia, M., Morra, A., Canisius, S. et al. Breast cancer risk factors and their effects on survival: a Mendelian randomisation study. BMC Med 18 , 327 (2020). https://doi.org/10.1186/s12916-020-01797-2

Download citation

Received : 15 May 2020

Accepted : 25 September 2020

Published : 17 November 2020

DOI : https://doi.org/10.1186/s12916-020-01797-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Breast cancer survival
• Breast cancer risk factors
• GWAS Catalog

BMC Medicine

ISSN: 1741-7015

• General enquiries: [email protected]

• Research article
• Open access
• Published: 28 September 2014

Risk determination and prevention of breast cancer

• Anthony Howell 1 , 2 , 3 ,
• Annie S Anderson 4 ,
• Robert B Clarke 3 ,
• Stephen W Duffy 5 ,
• D Gareth Evans 1 , 2 , 6 ,
• Montserat Garcia-Closas 7 ,
• Andy J Gescher 8 ,
• Timothy J Key 9 ,
• John M Saxton 10 &
• Michelle N Harvie 1 , 2  

Breast Cancer Research volume  16 , Article number:  446 ( 2014 ) Cite this article

55k Accesses

225 Citations

50 Altmetric

Metrics details

Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.

Introduction

Breast cancer remains a major public health problem. The incidence is rising in most countries and is projected to rise further over the next 20 years despite current efforts to prevent the disease [ 1 ]-[ 4 ]. The increased incidence is not surprising since there has been, in most countries, an increase in numbers of women with major breast cancer risk factors, including lower age of menarche, late age of first pregnancy, fewer pregnancies, shorter or no periods of breastfeeding, and a later menopause. Other risk factors which add to the burden of breast cancer are the increase in obesity, alcohol consumption, inactivity, and hormone replacement therapy (HRT) [ 4 ]. The impact of hereditary breast cancer has also increased. For example, it is estimated that the penetrance of the breast cancer 2 ( BRCA2 ) founder mutation in Iceland increased fourfold over the last century, and the cumulative incidence of sporadic breast cancer by age 70 also increased fourfold, from 2.5% to 11% of the population, over the same period [ 5 ]. Birth cohort effects have also been seen for both BRCA1 and BRCA2 in other countries [ 6 ],[ 7 ]. These data suggest that both familial and non-familial risks have increased. The Collaborative Group on Hormonal Factors in Breast Cancer (2002) estimated that the cumulative incidence of breast cancer in developed countries would be reduced by more than half, from 6.3 to 2.7 per 100 women, by age 70 if women had on average more children and breastfed for longer periods as seen in some developing countries [ 8 ]. Given global increases in population growth and the strong evidence that a woman’s ability to control her fertility may improve her social, economic, and overall health, it is not considered desirable to increase the birth rate per woman or to encourage pregnancies at a very young age. However, breastfeeding can and should be encouraged for many reasons, including possibly for the reduction of breast cancer risk. Many of the risks of reproductive factors are related to the effects of estrogen as demonstrated by the reduction in breast cancer incidence after an early oophorectomy, by inhibition of the estrogen receptor (ER) by using selective estrogen receptor modulators (SERMs) such as a tamoxifen or raloxifene [ 9 ], or by blocking estrogen synthesis by using aromatase inhibitors (AIs) such as exemestane [ 10 ] and anastrozole [ 11 ],[ 12 ].

A paradigm for preventative therapy (chemoprevention) is cardiovascular disease (CVD). The introduction of drugs that suppress cholesterol synthesis, modify platelet aggregation, or lower blood pressure has led to a steady decline in CVD over the past three decades, such that deaths from CVD in women less than 85 years old fell below those for cancer in 1999 [ 13 ]. The cardiovascular community is helped by the reduction of a major risk factor (smoking) and having easy-to-measure, repeatable biomarkers (cholesterol and blood pressure). CVD deaths are also reduced by optimal treatment of disease once it arises; this is also true for breast cancer treatment, in which (as a result of the introduction of screening and optimizing treatments) deaths have decreased by approximately one third over the past 20 years. This is a major advance for breast cancer; however, primary prevention has not occurred at the population level in contradistinction to CVD.

The fraction of breast cancer cases attributable to lifestyle and environmental factors in the UK was estimated to be 26.8% in 2010 [ 14 ], and a recent review suggests that half of breast cancer cases may be prevented if chemoprevention is applied in appropriate at-risk populations and the major modifiable risk factors, including achieving and maintaining a healthy weight, regular physical activity (PA), and minimal alcohol intake, are instituted [ 4 ]. Thus, there are further possibilities of important reductions in breast cancer incidence. However, major gaps exist in our knowledge to determine the risk of breast cancer accurately in order to apply these approaches to appropriate populations of women.

This review is an expansion and update of a brief review published in the Gap Analysis in 2013 of breast cancer research overall [ 1 ]. Besides summarizing new data published over the past year, this review has enabled us to give more comprehensive summaries of risk factors, approaches to prevention, and how understanding the biology of the breast may lead to new approaches to risk and prevention and also to expand on the all-important area of how to implement current risk prediction and preventive measures in the population (Table 1 ).

Methods of risk assessment

Models and scoring systems have been developed either to predict the probability that a person carries a mutation in the BRCA1/2 genes, which is relevant to relatively small numbers of women with strong family histories, or to predict breast cancer risk over time [ 15 ],[ 16 ]. Computer models such as BOADICEA (The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and BRCAPRO (risk estimator for breast and ovarian cancer) [ 17 ] and scoring systems perform well for predicting BRCA1/2 mutation carrier probability, which is important in deciding whether to perform a genetic test [ 18 ],[ 19 ].

Of relevance to all women, several models have been developed to predict risk of breast cancer over time (for example, 5-year, 10-year, or lifetime risks). These predict the probability that a woman in the population with a particular combination of risk factors will develop breast cancer [ 14 ]-[ 16 ]. The tested models include the Tyrer-Cuzick [ 20 ] and Gail [ 21 ] models, both of which include family history and non-familial risk factors, BOADICEA [ 22 ], a modification of the Claus model to include non-familial risk factors [ 23 ], the Rosner-Colditz model [ 24 ], and several others, many of which require further validation [ 16 ].

The Gail model includes these risk factors: age at menarche, age at first live birth, number of previous breast biopsies, benign breast disease, and number of first-degree relatives with breast cancer. Studies indicate that the Gail model is well calibrated in regularly screened American women [ 25 ] and when using updated breast cancer incidence [ 26 ]. However, recent studies in the UK and US suggest that it may under-predict actual risk relative to the Tyrer-Cuzick model [ 27 ]-[ 29 ], possibly because of the limited family history and not including age of onset of cancer in the family whereas the Tyrer-Cuzick model also includes second-degree family history, age of onset of cancer, and use of HRT.

Although current models can give an accurate estimation of lifetime risk (for example, we can tell a woman, with some accuracy, that she has a 1 in 3 lifetime risk of breast cancer), we cannot tell her whether she is the one who will develop the disease or whether she is one of the two women who will not. To fill this gap in our knowledge, there is great interest in adding other risk factors to current models, such as mammographic density [ 30 ],[ 31 ], single-nucleotide polymorphisms (SNPs) [ 32 ],[ 33 ], estimation of hormone levels [ 34 ], and lifestyle factors in order to test whether they improve the accuracy of risk prediction in the female population. Here, we examine recent progress made in improving available breast cancer risk prediction models.

Improving risk estimation - mammographic density

The available data on mammographic density in relation to breast cancer risk have been reviewed recently [ 30 ],[ 31 ]. Dense tissue on the mammogram is white, whereas fat tissue is radio-lucent and appears black. An overview of 42 studies of visually assessed mammographic density (the proportion of the breast as a percentage which appears white) indicated that the relative risk of breast cancer for women with 70% or more density was 4.64-fold greater compared with women with less than 5% density [ 35 ]. In this report, the magnitude of the risk was greater using percentage density than for other visual methods of density estimation, such as Wolffe patterns or the Breast Imaging Reporting and Data System (BI-RADS) classification, which divides density into four visually assessed categories and is widely used in the US. The distribution of visually assessed mammographic density is shown in Figure 1 .

An example of the distribution of visually assessed percentage density of the breast. The sample consists of 50,831 women between 46 and 73 years of age. Density was estimated in two views of each breast on a visual analogue scale, and the four readings were combined to give a single value per woman [ 54 ].

Four studies have already assessed whether adding a measure of mammographic density improves risk estimation compared with the estimation using standard models alone. A standard measure of improvement of risk assessment is the C-statistic. This is the area under the receiver operating curve (AUC), which in turn is a reflection of the sensitivity and specificity of the model. The higher the C-statistic (AUC), the greater the discriminatory accuracy of the model. An AUC of 0.5 identifies a model whose discriminatory accuracy is no better than chance alone, whereas an AUC of 1.0 identifies a model with perfect discriminatory accuracy. In practice, AUCs of 0.7 or 0.8 are consistent with good discriminatory accuracy [ 15 ].

Tice and colleagues [ 36 ] estimated adding the BI-RADS assessed density to the Gail model. The C-statistic for the Gail model in this study was 0.67, but adding density to the model modestly increased the C-statistic to 0.68, although this small increase in discriminatory accuracy was significant ( P <0.01). Barlow and colleagues [ 37 ] reported an increase of the C-statistic from 0.605 (95% confidence interval (CI) 0.60 to 0.61) to 0.62 (95% C1 0.62 to 0.63) also by adding BI-RADS density to the Gail model. Chen and colleagues [ 38 ] demonstrated that adding percentage density to the Gail Model 2 significantly ( P = 0.015) increased the C-statistic, from 0.602 to 0.664. Tice and colleagues [ 39 ] performed a second study of adding BI-RADS to a modification of the Gail model and reported a C-statistic rise from 0.61 to 0.66. These studies are important in that there was an improvement, albeit modest, in discriminatory accuracy in all of them.

It should be borne in mind that owing to the correlations among breast cancer risk factors, the addition of a new risk factor, however powerful, to a model already containing several risk factors will invariably make a modest difference to prediction measures such as AUC. Whereas some studies have suggested that density adds little to risk prediction [ 40 ], some find AUCs for density or another breast composition measure alone of 0.6 to 0.8 [ 41 ]-[ 44 ], which is similar to those observed for the Gail and other models.

Although the improvement in the C-statistic shown in these studies is modest, a more relevant measure of the utility of adding density information to risk models is how much it improves the ability to identify women at different levels of absolute risk for breast cancer (for example, re-classification of women crossing threshold risk levels set for public health interventions such as enhanced screening or chemoprevention). Further validation of risk models, including BI-RADS or other density measures such as volumetric approaches in prospective cohort studies, is needed to assess potential value of density in risk-stratified prevention or screening programs.

One method of density estimation, the interactive thresholding technique known as CUMULUS developed in Toronto [ 45 ], determines the area of dense and non-dense tissue, unlike visual techniques outlined above, and is widely regarded as a gold standard method for estimation of density. A meta-analysis of 13 case-control studies using this technique indicated that the association of density with risk was strong. Perhaps surprisingly, the risk prediction was better for dense area as a percentage of the whole breast rather than absolute dense area [ 46 ]. There remains a need to assess whether some measure of CUMULUS density adds to the predictive accuracy of standard models. CUMULUS is time-consuming and requires specialized training, and the technique will require greater automation to be useful on a population basis (Nickson and colleagues [ 47 ]).

Methods are being developed to assess the volume of dense and non-dense tissue in the breast and may be more relevant not only because density is a volume but because they can be partially or fully automated with the potential for use in populations of women. The first reported estimation of the relationship of volumetric density to standard risk factors was by Shepherd and colleagues [ 48 ], who used a technique called single x-ray absorptiometry. In their study, the C-statistic for risk factors alone was 0.609, which significantly increased to 0.667 when log fibro-glandular volume was added to standard risk factors. The study was performed by using analogue mammograms. Newer automatic techniques - such as Quantra (Hologic, Inc., Bedford, MA, USA) and Volpara (Matakina International, Wellington, New Zealand) - are designed for use with modern digital mammograms and are fully automatic. How they add to standard models is being tested, but studies already demonstrate that they are consistent with magnetic resonance imaging measures of volumetric density [ 49 ],[ 50 ].

Improving risk estimation - single-nucleotide polymorphisms

Mutations in high-risk breast cancer genes such as BRCA1/2 affect only small numbers of women, whereas variation in lower-impact, common susceptibly loci or SNPs can be responsible for a larger percentage of cancers in the population. Although it has been predicted for some time that risk would be related to polygenic inheritance of common low-penetrance loci [ 51 ], these have only recently been identified. SNPs are, by definition, common alterations in the DNA code that are mostly thought to be non-functional variants that frequently occur outside functional genes. Relative risks from SNPs are small (maximum risk is around 1.43-fold) and many have effects of less than 1.1-fold. Recent reports of ‘risk’ SNPs are a result of large-scale multinational collaborations involving tens of thousands of breast cancer cases and appropriate controls. Such large-scale studies are required since each SNP is associated with a small increase or decrease in risk. However, in combination (for example, through polygenic risk scores based on the average of the number of risk alleles weighted by the relative risk associated with each allele), combined SNPs can be associated with substantial increases or decreases in risk. The number of validated SNPs associated with breast cancer risk is currently over 70, but it is thought that there may be hundreds more that affect breast cancer risk [ 32 ].

Based on the first few SNPs identified, studies were performed to determine how they might add to the Gail model. All studies showed some improvement in the C-statistic when SNP scores and the Gail model were combined. Mealiffe and colleagues [ 52 ] using seven SNPs reported an increase in AUC from 0.58 to 0.61 ( P = 0.001), Wacholder and colleagues [ 53 ] using 10 SNPs reported an increase in the AUC from 0.58 to 0.62 ( P <0.001), and Gail [ 54 ] predicted an increase in the C-statistic from 0.61 to 0.63. More recently, Dite and colleagues [ 55 ] included seven SNPs and reported an increase in AUC from 0.58 to 0.61 ( P <0.001).

An additional way to determine the value of adding SNPs to risk models is to assess changes in risk group stratification before and after adding SNPs. For instance, increasing the numbers of women estimated to be truly at high or low risk would be of value clinically. All the studies outlined above resulted in changes in classification to higher and lower risk categories resulting in a ‘widening’ of the risk distribution curves. For example, in the study by Comen and colleagues [ 56 ], a combination of 10 risk SNPs and the Gail model resulted in 20% of women being re-classified into a lower and 20% into a higher risk group as defined by quintiles. More recently, Brentnall and colleagues [ 57 ] and Evans and colleagues [ 58 ] estimated the effect on risk of combining 18 or 67 SNPs and the Tyrer-Cuzick model (Figure 2 ). Adding more SNPs changed the risk distribution so that more women were in the high- and low-risk groups, respectively (Figure 2 ).

Estimation of the effect on the distribution of Tyrer-Cuzick scores by adding the results of 18 or 67 single-nucleotide polymorphisms (SNPs) in 10,000 women [ [ 53 ] ]. Adding SNPs increases the number of women in high- and low-risk groups. ER, estrogen receptor; SNP 18 and SNP 67, distribution using SNPs alone; TC, the Tyrer-Cuzick score alone; TC + SNP67, distribution of the combined score.

The studies outlined above highlight the prospects of using SNPs for improved risk prediction in high-risk clinics and in the general population. Further improvements may come from introducing more SNPs and the prospects of being able to predict the risk of specific breast cancer subtypes, such as ER + [ 59 ], ER − [ 60 ], grade III [ 61 ], and triple-negative [ 62 ] tumors, separately, knowledge of which could direct preventative approaches [ 63 ].

Improving risk estimation - hormone measurements

Large studies with long-term follow-up indicate that many hormones and growth factors are associated with an increased risk of breast cancer. The important question is whether any of them could be incorporated into models of breast cancer risk prediction. The Endogenous Hormones and Breast Cancer Collaborative Group reported that risk of breast cancer was related to steroid hormones such as estradiol, testosterone, and sex hormone-binding globulin in pre- and post-menopausal women and was recently confirmed in the European Prospective Investigation into Cancer study [ 64 ]-[ 67 ]. The relation of body mass index (BMI) with risk is attenuated by adjusting for estrogen, but the relation of estrogen with risk is not attenuated by adjusting for BMI. This is what would be expected if estrogen mediates the effect of BMI [ 64 ]. Thus, estrogens may explain the increased risk of breast cancer in obese post-menopausal women, although this does not preclude other hormones and cytokines from mediating the effects of estrogen (which may be more readily measurable) or other mechanisms by which overweight and obesity might affect risk [ 64 ],[ 68 ].

The use of hormone measurements in breast cancer to incorporate into risk models is attractive. However, measurement, particularly in post-menopausal women, is problematic because of assay variation related to low hormone levels and other unknown causes of variation in hormone levels over time [ 69 ]. Nevertheless, Jones and colleagues [ 70 ] demonstrated that change in estradiol and testosterone may be good biomarkers of the effectiveness of weight loss and this is supported by recent data from the Nurses’ Health Study [ 71 ]. Other growth factors/hormones such as insulin-like growth factor-1 (IGF-1) and prolactin are associated with breast cancer risk, particularly in post-menopausal women, and may possibly be useful in models, although the risk increases between high and lower risk groups of hormone concentrations are relatively small [ 72 ]-[ 75 ].

Improving risk estimation - other methods

New biomarkers for risk prediction are likely to come from measures in blood or tissues by a variety of techniques. At present, it appears that none of these is ready for incorporation into the standard models, but given the pace of advance they are likely to be in the near future. Examples of some current approaches include the development of assays for serum antibodies against epithelial antigens [ 76 ], gene expression in peripheral blood white cells [ 77 ], blood epigenetic markers [ 78 ], and developments in high-throughput proteomics [ 79 ] and adductomics [ 80 ]. Incorporating new risk markers into risk models may not be straightforward since extensive validation will be required and potential interactions with known existing factors will need to be carefully evaluated.

Breast cancer prevention

What can we advise women to do with respect to prevention? Recent reviews focus on various aspects of prevention, including SERMs and AIs for the chemoprevention of ER + cancers [ 81 ],[ 82 ], chemoprevention for ER – cancers [ 83 ],[ 84 ], and lifestyle changes [ 4 ],[ 85 ],[ 86 ]. These reviews are helpful in pointing out some areas that are potentially clinically useful and others where far more investigational work is required.

There is probably sufficient evidence from the randomized trials for the use of SERMs and AIs for use in women at high and moderate breast cancer risk [ 9 ],[ 11 ] and sufficient observational data to advise weight control, exercise, and moderation of alcohol intake [ 4 ],[ 86 ]. In this section, we review the data which support these suppositions for each of the approaches to prevention; in the next section, we review possible new investigational avenues.

Preventative therapy (chemoprevention)

There have been nine randomized trials of SERMs [ 9 ] and two trials of AIs [ 10 ],[ 11 ] mainly in women at increased risk of breast cancer but also in women with osteoporosis or heart disease (raloxifene). In the SERM trials, 83,399 participants were included with 306,617 years of follow-up over an average period of 65 months. The overall reduction in all breast cancer (including ductal carcinoma in situ ) using tamoxifen 20 mg per day was 38% ( P <0.0001) [ 9 ] with an estimated 10-year reduction in cumulative incidence from 6.3% in the control group to 4.2% in the SERM groups. This overview included the SERMs lasofoxifene and arzoxifene, which are not undergoing further development by their respective drug companies. This leaves tamoxifen and raloxifene as the two SERMs in clinical practice. These were compared in a randomized trial (the Study of Tamoxifen and Raloxifene, or STAR, trial) [ 87 ]. Tamoxifen was significantly superior to raloxifene in longer-term follow-up for preventing invasive breast cancer (relative risk raloxifene/tamoxifen 1.24, 95% CI 1.05 to 1.47). Nonetheless, raloxifene was associated with fewer side effects than tamoxifen, particularly with respect to the uterus, and may be preferable in post-menopausal women.

When given after surgery to prevent relapse of breast cancer, AIs are generally superior to tamoxifen. This led to the initiation of two placebo-controlled trials in post-menopausal women at increased breast cancer risk. One tested the AI exemestane and reported a reduction of breast cancer risk of 65% after 5 years of treatment [ 10 ]. In the other trial (International Breast Cancer Intervention Study II, or IBIS II), anastrozole was compared with placebo [ 11 ]. In that study, 3,864 post-menopausal women between 40 and 70 years of age at increased risk of breast cancer were randomly assigned to anastrozole 1 mg per day or placebo for 5 years. A recent report indicates that the incidence of breast cancer was reduced by 53% (hazard ratio 0.47, 95% CI 0.32 to 0.68) by use of anastrozole. Compared with SERMs, AIs are not associated with an increased risk of thromboembolic disease and uterine problems, including cancer, but are associated with increased mild to moderate bone/muscle pain and reduced bone density.

Additional hormonal approaches to prevention surround the use of HRT. Results from the Women’s Health Initiative (WHI) randomized controlled trial of premarin and medroxyprogesterone acetate indicate that the combination given after menopause increases breast cancer risk [ 88 ], a result supported by many observational studies. After the publication of the WHI study, many women stopped HRT and it has been suggested by some to have been associated with a reduction in the incidence of breast cancer, CVD, and venous thrombosis as well as potential considerable savings in health resources [ 89 ]. However, the magnitude of these associations, as well as the question of whether a cause-and-effect relationship exists, remains controversial. In contrast, estrogen-only HRT using premarin resulted in a reduction of the incidence and deaths from breast cancer in the second WHI trial performed in women with a previous hysterectomy [ 90 ]. This result is supported by some, but not all, observational studies and indicates that premarin may be regarded as a breast cancer preventive agent [ 91 ].

The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) has estimated that over 40% of post-menopausal breast cancer could be prevented by reductions in alcohol, excess body weight, and inactivity [ 92 ]. These estimates differ from those suggested by others as outlined above [ 4 ],[ 14 ], but all of the estimates point in the same direction and indicate the importance of lifestyle throughout the lifespan and the challenge of finding ways to support women to achieve healthy ways of life.

Energy restriction/weight control

Strong observational data indicate that weight gain in the premenopausal period and being overweight or obese after menopause increase breast cancer risk [ 4 ],[ 93 ]. In a meta-analysis, Renehan and colleagues [ 93 ] estimated that for each 5 kg/m 2 increase in BMI the risk of breast cancer was increased by 12%. Evidence from two large observational studies indicates that pre- or post-menopausal weight loss reduces the risk of post-menopausal breast cancer. In the Iowa Women’s Health Study, sustained weight reduction of 5% of body weight reduced post-menopausal breast cancer risk by 25% to 40% compared with women who continued to gain weight [ 94 ]. In the Nurses’ Health Study, post-menopausal women who did not take HRT and maintained a body weight reduction of 10 kg or more had a 50% reduction in the risk of breast cancer [ 95 ]. There is some evidence from the National Surgical Adjuvant Breast Project P-I and STAR SERM trials that weight reduction after the age of 35 is also effective [ 96 ]. It is important to emphasize the other well-known beneficial effects of weight control, including the reduction of diabetes [ 97 ],[ 98 ] and CVD [ 99 ],[ 100 ]. Modest weight loss of 5% to 10% will reduce the risk of diabetes by up to 60% and can reduce low-density lipoprotein cholesterol by 15% and triglycerides by 20% to 30%, increase high-density lipoprotein cholesterol by 8% to 10%, and reduce blood pressure by around 5%. These changes in CVD risk markers suggest a 30% or greater reduction in risk of CVD.

Dietary components and prevention

There is great interest in determining whether components of diets such as saturated fat content or the amount of fruit and vegetables is related to the risk of breast cancer. A randomized trial performed by the WHI of reduction of the proportion of fat in the diet resulted in a non-significant 8% reduction in the risk of breast cancer, but there was some confounding with weight loss [ 101 ]. After surgery for breast cancer, where dietary interventions were performed in addition to standard adjuvant therapy, reduction of fat was associated with a 23% reduction in recurrence. This study was also confounded by weight loss in the intervention arm and thus in both studies the reason for the effects on risks is not clear [ 102 ]. There was no advantage to an increase of fruit and vegetable intake in another large randomized adjuvant trial [ 103 ]. Recent large pooled analyses have suggested that both dietary intake of vegetables and circulating concentrations of some carotenoids may be inversely associated with the risk for ER – breast cancer but not with the risk for ER + disease. This topic requires further investigation [ 104 ],[ 105 ]. Whereas intervention studies give little support for the preventive efficacy of specific dietary components, prospective cohort studies provide indications that adherence to dietary guidelines and certain types of diet may impact on breast cancer risk. Adherence to dietary and lifestyle guidelines appears to be beneficial. In a study from Canada [ 106 ], adherence to the American Cancer Society (ACS) and WCRF/AICR dietary/lifestyle guidelines appeared to be beneficial: 49,613 women completed dietary and lifestyle questionnaires, and adherence was associated with a 31% reduction of breast cancer estimated over 16 years compared with women who did not follow the guidelines. The guidelines include advice on weight control, PA, alcohol intake, and intake of red meat, vegetables, fruit, and sodium. In another study, the WHI reported the effects of adherence to ACS guidelines in 65,838 post-menopausal women and indicated that adherence to guidelines reduced breast cancer risk by 22% after 12.6 years of follow-up [ 107 ].

Adherence to dietary types may also affect risk. For example, in the California Teachers Study, data from 91,779 women were analyzed according to predominant dietary pattern by using principal component factor analysis [ 108 ]. A greater consumption of plant-based foods was associated with a 15% reduction in breast cancer risk (85% CI 0.76 to 0.95). A systematic review of dietary patterns and breast cancer was performed by Albuquerque and colleagues [ 109 ], who concluded that a Mediterranean dietary pattern and diets composed largely of vegetables, fruit, fish, and soy are associated with a decreased risk of breast cancer. Risk reduction may also be helped by appropriate intakes of dietary fiber, fruit, and vegetables [ 110 ]-[ 114 ].

Physical activity

More than half of the US population does not meet the recommended PA guidelines. In addition, the most recent Health Survey for England [ 115 ] showed that over 40% of adult women (at least 19 years old) are not meeting current guidelines of 150 minutes of moderate or 75 minutes of vigorous PA per week [ 116 ]. The WCRF/AICR Expert Report [ 117 ] described the evidence for an inverse association between PA and breast cancer risk as ‘probable’ and ‘limited - suggestive’ for post- and pre-menopausal women, respectively. A more recent review of 73 observational studies indicated that moderate to vigorous PA reduces breast cancer risk by an average of 25% in pre- and post-menopausal women compared with inactive women [ 118 ]. The strongest inverse associations with breast cancer risk were observed for recreational PA, lifetime PA, post-menopausal PA, and participation in moderate to vigorous PA. There was also evidence of dose-response relationships, with higher volumes of PA associated with greater risk reduction, but with the most pronounced reductions in risk being observed in lean versus obese women. The optimal level of PA for breast cancer risk reduction is unclear, however, and may be greater than current recommendations [ 118 ]. A major limitation of observational studies is the heterogeneity of self-report questionnaires that have been used to measure PA. The use of more objective measures, such as 7-day accelerometry, would provide more robust PA data. There is a clear need for randomized controlled trials which include clinical end-points or biomarkers on the causal pathway, but designing such trials is challenging because of the large sample size required and the expense of collecting long-term follow-up data.

It is estimated that breast cancer risk is increased by 7% to 10% for each one-unit increase in intake of alcohol per day (a unit is half a pint of 4% strength beer or cider or 25 mL of 40% strength spirits, and a small 125-mL glass of 12% strength wine is 1.5 units). In the Nurses’ Health Study, women who consumed 4 to 9 units per week were 15% more likely to develop breast cancer compared with never drinkers [ 119 ]. Women with the highest alcohol intake (of at least 27 units per week) were 51% more likely to develop breast cancer compared with non-drinkers. These studies suggest that women who want to minimize their breast cancer risk should not be drinking more than one unit daily and probably have at least two alcohol-free days weekly. Studies show that the negative effect of alcohol may be abrogated by adequate dietary folate intake (rather than supplements) and should be pointed out as a preventive measure for women who find reduction in alcohol intake difficult [ 120 ]. Better life expectancy associated with moderate alcohol intake compared with none in a large meta-analysis should be balanced against recommending zero intake [ 121 ].

It is important to be aware that lifestyle prevention includes not only middle- and late-age women but younger women after menarche. Animal experiments and modeling of the reproductive events in women indicate that the most susceptible period for carcinogenesis is during the period between menarche and first pregnancy [ 122 ],[ 123 ]. In women, this susceptibility is highlighted by the increase in premalignant lesions in the breast of women who drank alcohol or smoked (or both) during this period of early life [ 124 ].

The biology of risk and prevention as an indicator of potential new approaches

One way to develop new approaches to prevention is to assume that understanding the biological basis of breast development will give indications of potential targets for therapeutic interventions. Great insights into the mechanisms of breast development in utero and at puberty, particularly in the rodent mammary gland, have been discovered and are summarized in recent reviews [ 125 ],[ 126 ]. They highlight the crucial importance of epithelial-stromal interactions for normal breast development and of the individual cell types within the stroma, including immune cells, fibroblasts, or adipocytes. Importantly, it has been shown that experimental inhibition of any one of these interactions results in lack of breast development and this has implications for our thinking about approaches to prevention (Figure 3 ).

Features of the normal breast. (a) Electron micrograph of a ductule of the breast. (b) Section of lobules of the breast showing a relationship with collagenous and fatty stroma. Reprinted with permission from the American Association for Cancer Research [ 166 ]. (c) A simplified cartoon of reported potential interactions between three cell types in the stroma and the epithelium of the breast. CSF, colony-stimulating factor; ER, estrogen receptor; IGF1, insulin-like growth factor 1; PR, progesterone receptor; PTH, parathyroid hormone; TDLU, terminal duct lobular unit.

The experiments outlined above cannot be performed in humans. However, another approach to the development of prevention is understanding the biological mechanisms of risk factors for breast cancer. Here, we discuss some examples which support this view with respect to estrogen and the breast, early and late first pregnancy, menopausal involution of epithelial cells, mammographic density, and mechanism of the effects of energy restriction and exercise.

Estrogen and the breast

The most successful preventative approach to breast cancer to date, reducing the effects of estrogen on the breast, has come from an understanding of the biology of the ER and the knowledge that estrogen is synthesized in the breast and elsewhere after ovarian function decreases at menopause. These data have led to the introduction of the SERMs (tamoxifen and raloxifene) and the potential introduction of AIs (exemestane and anastrozole) for breast cancer prevention. Tamoxifen acts by blocking the ER but under certain circumstances can change to being a partial agonist via the ER and this may limit its preventive utility since in some women at increased risk it appears to increase mammographic density [ 127 ]. The development of orally active ER downregulators similar to fulvestrant (which has to be given intramuscularly, thus limiting its preventive utility) may be superior to tamoxifen (for example, ARN-810, NCTO1823835) [ 128 ]. Another potential way to enhance the therapeutic ratio of tamoxifen is to use low doses or to combine tamoxifen with retinoids such as fenretinide; studies of these approaches are under way in prevention trials in Italy [ 129 ]. Another approach may be a combination with low-dose aspirin, which has some minor preventive effects on breast cancer risk but would help combat the increased risks of thromboembolic disease with tamoxifen.

Mimicking the protective effects of an early first pregnancy

Recent insights into the effects of early first pregnancy of the normal breast in young women give clues to how we might mimic this effect therapeutically. Since the demonstration that ER + and progesterone receptor-positive (PR + ) cells in the normal breast rarely proliferate [ 130 ], it has been shown, for example, that progesterone binds to its receptor on the PR of the epithelial cell and stimulates the synthesis and release of paracrine mediators such as Rank (receptor activator of nuclear factor-kappa-B), Wnt (wingless related integration site), and growth hormone, which in turn stimulate adjacent stem and progenitor cell expansion [ 131 ],[ 132 ]. Recently, it was shown that early first pregnancy in women reduces the number of PR + cells and downregulation of paracrine mediators, resulting in a reduction of the stem/progenitor cell compartment [ 133 ]. These data suggest that modulating the effect of progesterone by the use of antiprogestins should be explored for breast cancer prevention [ 134 ].

Establishing the cause of the inverse association between childhood/adolescent obesity and lower risk of breast cancer

Observational data have linked diet and growth in height in childhood and dietary exposures during early adulthood (that is, between menarche and first full-term pregnancy to later risk of breast cancer). These studies have either used retrospective recall of early life exposures from adults or prospectively assessed short-term effects on surrogate risk markers like benign breast disease [ 135 ]. Studying lifestyle exposures in this period is a challenge which has understandably received less research attention than exposures later in life. The period between menarche and first full-term pregnancy is a priority for research since risk can accumulate rapidly in this period until terminal differentiation that accompanies first pregnancy.

Key observations which deserve further study are the reduced breast cancer risk with a higher BMI in early adulthood (that is, at the age of 18 to 21), reported from numerous prospective studies among Caucasian [ 136 ],[ 137 ], black [ 138 ], and Asian [ 139 ] populations. This observation is partly explained by smaller adult weight gains, which are consistently reported among heavier young women [ 140 ]-[ 143 ]. Other possible mechanisms which may put heavier women at lower risk than their lean counterparts include higher estrogen levels, which may upregulate the BRCA1 tumor-suppressor gene, earlier differentiation of breast tissue [ 9 ], subsequent lower IGF-1 levels in adulthood [ 144 ], and a slower pubertal growth and sexual maturation despite their early menarche [ 135 ]. Increased irregular cycles are often cited as a likely protective mechanism but are not supported by available data [ 145 ]. Likewise, height velocity has been linked to risk of breast cancer [ 146 ] and benign breast disease [ 147 ], which in turn may be linked to dietary patterns which are high in animal versus vegetable protein and lower in fiber and isoflavones [ 148 ].

Reversing the promotional effects of late pregnancy

Late pregnancy is a major driver of the worldwide increase in breast cancer incidence. Over half of women in the UK have their first pregnancy over the age of 30, and thus understanding the mechanism of its effect on risk is of great importance. It seems likely that the breasts of older fertile women harbor early pre-cancerous lesions. One mechanism in which these may be stimulated is as a result of immunological processes that occur during post-partum breast involution. Lyons and colleagues [ 149 ] demonstrated an increase in cyclooxygenase 2 during involutional macrophage infiltration and showed that ibuprofen reduces post-partum breast cancer in these models. Ibuprofen might be tested in women at high risk because of late pregnancy and a positive family history [ 148 ],[ 149 ]. Premalignant lesions in the breast have indeed been detected by review of serial sections of the breasts at post-mortem of older premenopausal women and found to be present in up to one third of women [ 150 ],[ 151 ]. It is clear that most do not progress to breast cancer since the incidence of the disease is not that high. Recently, Haricharan and colleagues [ 152 ] demonstrated that the signal transduction molecule pSTAT5 (phospho-signal transducer and activator of transcription 5) is activated by inhibiting apoptosis in premalignant lesions that progress to forming cancer. Inhibitors of this pathway are in the clinic and ultimately could be used for prevention [ 153 ].

Failure of menopausal breast involution

The lobules of the breast undergo involution after menopause. However, Wellings and colleagues [ 154 ] reported atypical premalignant lobules which persisted after menopause where menopausal regression might be expected. Investigators at the Mayo Clinic noted, by careful histological examination of biopsies of the breast of post-menopausal women, that the breast lobules in some women did not undergo post-menopausal involution and that these women were at high risk of subsequent breast cancer [ 155 ]. As a measure of the importance of this observation, the authors investigated how the lack of involution compared with risk prediction of the Gail model in this group of women. The C-statistic for the Gail model of the patients studied was 0.60. For lobular involution (or not), the C-statistic was 0.66. Combining Gail risk and involution did not change the latter figure [ 156 ]. There are, as far as we are aware, no published data on the mechanism of lack of post-menopausal involution but this may be similar to the lack of involution after a pregnancy [ 152 ]. The reduction of apoptosis reported in animal models of pregnancy involution was reported in women [ 157 ]. In the clinic, there are agents to enhance apoptosis, such as ABT-263, with potential for transfer to prevention if toxicity could be reduced [ 158 ].

Mechanism of mammographic density

Some studies show that the rate of the well-known decline of mammographic density with age is slower in some women and indicates higher breast cancer risk [ 159 ],[ 160 ]. Methods to reduce density may prevent breast cancer. As proof of principle of this hypothesis, Cuzick and colleagues [ 127 ] demonstrated in the IBIS-I prevention trial that women who had a more than 10% reduction in density with tamoxifen had a 70% reduction in risk of breast cancer risk but that for women with less or no reduction in density there was no reduction in risk. Investigation of the reasons for the lack of effect of age and of tamoxifen on some breasts is clearly important [ 161 ].

Gene expression profiles of fibroblasts derived from dense and non-dense areas of the breast indicate marked differences in expression. Expression of genes associated with inflammation (such as c-Jun N-terminal kinases, or JNK) and several signaling pathways is upregulated and suggests the use of, for example, JNK inhibitors, already in the clinic for treatment of overt disease [ 162 ],[ 163 ]. Some fibroblasts in dense areas resemble cancer-associated fibroblasts in their signaling pathways and production of extracellular aligned collagen, all potential targets for prevention [ 164 ].

Energy restriction mimetics

Energy restriction is well known to increase longevity in several types of organisms, in part by reducing the incidence of cancer. It acts predominantly by reversing the effects of obesity on inflammation, certain signal transduction pathways, and insulin/IGF-1 [ 165 ]. Obesity is associated with macrophage infiltration and activation in fat, which in turn results in cytokine production and increased aromatase activity and estrogen production [ 166 ],[ 167 ]. Obesity also results in reduced insulin sensitivity and altered signal transduction pathways, such as P13Kinase and mammalian target of rapamycin (mTOR), and in mitochondrial metabolism [ 168 ],[ 169 ]. Some agents which beneficially reduce activity of these pathways such as mTOR inhibitors are already in the clinic, and others such as metformin and SIRT 1 activators such as resveratol and other activators of sirtuins are under investigation [ 170 ]. Doubt has been cast on the value of metformin [ 171 ], giving added importance to the randomized trial of adjuvant metformin instigated by Goodwin and colleagues [ 172 ].

Several biological mechanisms have been proposed to explain the inverse association between PA and breast cancer risk. Although regular exercise may delay the onset of menarche, increase the length of the menstrual cycle, or increase the number of anovulatory cycles, hence reducing exposure to sex hormones, prospective intervention studies suggest that high levels of exercise may be needed to induce menstrual cycle changes [ 173 ],[ 174 ]. Other possible mechanisms include improvements in insulin sensitivity, immune function/surveillance, and antioxidant defense capacity as well as alterations in gene function or apoptosis [ 175 ],[ 176 ]. Studies have also highlighted a potential role for epigenetic mechanisms which could reduce breast cancer risk in physically active women, including an increase in LINE-1 (long interspersed nucleotide elements-1) methylation (index of global DNA methylation) and an increase in the methylation of tumor-suppressor genes [ 176 ],[ 177 ]. Moderate levels of PA may also increase the expression of telomere-stabilizing proteins, thereby attenuating the effects of aging on telomere length and potentially reducing the risk of age-related diseases such as breast cancer [ 178 ],[ 179 ].

PA could also influence breast cancer risk through its effect on weight loss and reduced levels of body fat. This means that distinguishing the independent effects of PA on breast cancer risk is difficult because body fat reduction impacts a range of putative breast cancer risk markers, including circulating levels of sex hormones, insulin-like growth factors, adipokines, and inflammatory mediators [ 173 ]. Elevated circulating levels of adipokines such as leptin, interleukin-6, and tumor necrosis factor-alpha and the acute phase protein C-reactive protein as well as reduced levels of adiponectin are associated with high levels of body fat [ 173 ],[ 180 ], whereas weight loss interventions involving PA evoke reductions in circulating levels of inflammatory markers and leptin while increasing circulating levels of adiponectin [ 181 ],[ 182 ]. Despite this, evidence from both human [ 173 ],[ 174 ] and animal [ 175 ],[ 183 ] studies suggests that regular aerobic exercise can induce changes in biological risk factors (for example, sex hormones, insulin sensitivity, antioxidant defense capacity, and intracellular signaling pathways) that are independent of PA-induced changes in body weight and body composition.

The studies outlined above indicate the interactions which occur between epithelial cells and between them and stromal cells such as macrophages, fibroblasts, and adipocytes (Figure 3 ). They indicate the potential for new approaches to prevention, although translation to the clinic will be difficult. An excellent discussion of the problems is given by Strasser-Weippl and Goss [ 184 ].

Clinical application

Preventive therapy.

Several guidelines advise how we might apply the knowledge that we have gained concerning hormonal prevention (tamoxifen, raloxifene, exemestane, and anastrozole) and lifestyle factors (weight control, exercise, and limitation of alcohol) to populations of women. Hormonal chemoprevention is suggested for women at increased risk, whereas lifestyle factors can be applied to all women since all are at some risk of breast cancer, and even at low risk, lifestyle factors are similar to those which help prevent other conditions such as CVDs and diabetes.

Three major sets of clinical guidelines were published concerning the selection of women for chemoprevention in 2013. The US Preventive Service Task Force gives guidelines for prescription of medication for risk reduction of breast cancer [ 185 ]. The recommendation applies to asymptomatic women 35 years or older without a prior diagnosis of breast cancer, ductal carcinoma in situ , or lobular carcinoma in situ . They advise use of the Gail model to assess risks and a cutoff of 1.66% 5-year risk. However, taking toxicity into account, they suggest that a threshold for advising treatment of 3% 5-year risk may be more appropriate and advise use of the tables published by Freedman and colleagues [ 186 ] and, as in the tables, that the balance for use/no use depends on age, race/ethnicity, the medication used, and whether the woman has a uterus.

The American Society of Clinical Oncology published their clinical practice guideline in August 2013 [ 187 ]. The report included a systematic review of randomized controlled trials and meta-analyses published between 2007 and 2013 which identified 19 trials and six chemoprevention agents. In women who are at increased risk of breast cancer and who are more than 35 years old, they suggest that tamoxifen (20 mg per day for 5 years) be discussed as an option to reduce the risk of ER + breast cancer. In post-menopausal women, raloxifene (60 mg per day for 5 years) and exemestane (25 mg per day for 5 years) should also be discussed as options for breast cancer risk reduction. Those at increased breast cancer risk are defined as individuals with a 5-year projected absolute risk of breast cancer of more than 1.66% (based on the National Cancer Institute Breast Cancer Risk Assessment Tool or an equivalent measure) or women diagnosed with lobular carcinoma in situ . SERMs are not recommended for use in women with a history of deep vein thrombosis, pulmonary embolus, stroke, or transient ischemic attack or during prolonged immobilization or in combination with HRT. In this update of the guideline published in 2009, the phrase ‘may be offered’ was replaced by ‘should be discussed as an option’ in women at increased risk of breast cancer [ 187 ]. The American Society of Clinical Oncology reviewers concluded that ‘research is needed to address the many unresolved issues related to the poor uptake of breast cancer chemoprevention agents in women who are at increased risk. These include (1) the design of effective tools and approaches to educate providers on the option of chemoprevention, (2) efficacious interventions that communicate to eligible women the risks and benefits of specific chemoprevention agents, (3) the development of tools that more accurately identify women at increased risk, and (4) a greater understanding of what disparities and barriers exist with regard to chemoprevention use among women at higher risk for breast cancer’ [ 187 ]. The document provides in-depth reviews of all of the important trials.

The UK National Institute of Health and Care Excellence published guidelines for women at increased risk of breast cancer by virtue of a family history of the disease [ 188 ]. For the first time in the UK, their recommendation was that women at greater than 30% (1 in 3-4+) lifetime risk of breast cancer be ‘offered’ tamoxifen or raloxifene and that in those at greater than 17% (1 in 6+) lifetime risk preventive therapy be ‘considered’ for treatment. They did not endorse use of AIs, since the IBIS-2 study had not been published at the time, but did suggest that a lifestyle advice leaflet be given.

Lifestyle change

The ACS published guidelines on nutrition and PA measures for cancer prevention in 2012 [ 189 ]. The guidelines were based on published data. Randomized controlled trials were given greatest credence and cohort studies over case-control studies. Four lifestyle choices were recommended to reduce cancer risk: (a) achieve and maintain a healthy weight throughout life, (b) adopt a physically active lifestyle, (c) consume a healthy diet, with an emphasis on plant foods, and (d) limit consumption of alcoholic beverages.

Importantly, recommendations were also made for introduction of the guidelines into the community: ‘Public, private, and community organizations should work collaboratively at national, state, and local levels to implement policy and environmental changes…’ [ 190 ].

The Second WCRF/AICR Expert Report (Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective) was published in 2007 [ 117 ] and is continually updated [ 190 ]. The recommendations are similar to the ACS guidelines and are relevant to the prevention of other conditions such as CVD.

Implementation

The guidelines outlined above are based on the best available knowledge and seem eminently sensible. It is widely appreciated that their implementation is a major and long-term problem. Although several models give reasonable indicators of risk of breast cancer, detecting women at risk in the population is problematic. For example, women with only a minor family history but with endocrine risk factors are very often not aware of their breast cancer risks. One solution is to use mammographic screening programs as a time to communicate risk information (including lifestyle parameters) and to highlight/signpost access to preventive therapy lifestyle programs [ 58 ],[ 191 ]. In a program in Manchester, UK, collecting risk information at screening was shown to be feasible, and 95% of women indicated that they wished to know their risks of breast cancer [ 58 ]. Women at high risk can be offered preventive therapy in the context of specialist clinics, but on a population basis it may be optimal to implement risk assessment and treatment in general practitioner practices as is the case for the prevention of CVD in clinical practice.

For lifestyle change, the goals for breast cancer prevention are the same as those required to solve the obesity epidemic. These are very well highlighted in the goals set by the US Institute of Medicine report on ‘Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation’ [ 192 ]. The goals of the program encompassed integrating PA as a routine into everyday life, making healthy foods and beverages available everywhere, marketing messages pertaining to healthy nutrition and PA and expansion of the role of health-care providers, insurers, employers, and schools as national focal points for obesity prevention. The national (US) progress of this very broad and crucial program was summarized in a recent workshop [ 193 ]. The US Institute of Medicine believes that the obesity problem will be solved only by mobilizing the population of all ages for there to be an accelerated transformation to the obesity problem (Figure 4 ). The documents suggest groundbreaking approaches; similar ones could be adapted to other developed and developing countries.

US Institute of Medicine blueprint for lifestyle change. Reprinted with permission from the US Institute of Medicine [ 192 ].

Colditz and colleagues [ 194 ] recently summarized the critical barriers to change for the prevention of cancer in general. These included (a) skepticism that cancer can be prevented, (b) the short-term focus of cancer research, (c) interventions deployed too late in life, (d) research focus on treatment not prevention, (e) debates among scientists, (f) societal factors which affect health outcomes, (g) lack of transdiciplinary approaches, and (h) the complexity of successful implementation. These are barriers to be overcome.

Conclusions

One conclusion of this review is that the application of measures that are already available, such as chemoprevention and lifestyle prevention, would result in appreciable reductions in breast cancer risk. A second conclusion is that the pace of advance of our understanding of the biology of breast cancer risk and development is highly likely to give rise to new avenues for prevention over the next 10 years. A major problem is applying what we already know concerning the efficacy of prevention to appropriate populations of women. To apply chemoprevention, we need to have measures in place to assess risk and to explain the pros and cons of treatment and for prescription of appropriate therapies. Lifestyle change is a population problem which involves publicity concerning its risks and benefits of change and providing mechanisms to support women in their choices throughout society as highlighted in the US Institute of Medicine documents.

Abbreviations

American cancer society

Aromatase inhibitor

American institute for cancer research

Area under the receiver operating curve

Breast imaging reporting and data system

Body mass index

The breast and ovarian analysis of disease incidence and carrier estimation algorithm

Breast cancer 1/2

Confidence interval

Cardiovascular disease

Estrogen receptor

Hormone replacement therapy

International breast cancer intervention study

Insulin-like growth factor-1

c-Jun N-terminal kinases

mammalian target of rapamycin

Progesterone receptor

Selective estrogen receptor modulator

Single-nucleotide polymorphism

Study of tamoxifen and raloxifene

World cancer research fund

Women’s health initiative

Eccles SA, Aboagye EO, Ali S, Anderson AS, Armes J, Berditchevski F, Blaydes JP, Brennan K, Brown NJ, Bryant HE, Bundred NJ, Burchell JM, Campbell AM, Carroll JS, Clarke RB, Coles CE, Cook GJ, Cox A, Curtin NJ, Dekker LV, Silva Idos S, Duffy SW, Easton DF, Eccles DM, Edwards DR, Edwards J, Evans D, Fenlon DF, Flanagan JM: Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res. 2013, 15: R92-

Article   PubMed   PubMed Central   Google Scholar  

Arnold M, Karim-Kos HE, Coebergh JW, Byrnes G, Antilla A, Ferlay J, Renehan AG, Forman D, Soerjomataram I: Recent trends in incidence of five common cancers in 26 European countries since 1988: Analysis of the European Cancer Observatory. Eur J Cancer. 2013,

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM: Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014, 74: 2913-2921.

Article   CAS   PubMed   Google Scholar  

Colditz GA, Bohlke K: Priorities for the primary prevention of breast cancer. CA Cancer J Clin. 2014, 64: 186-194.

Article   PubMed   Google Scholar  

Tryggvadottir L, Sigvaldason H, Olafsdottir GH, Jonasson JG, Jonsson T, Tulinius H, Eyfjörd JE: Population-based study of changing breast cancer risk in Icelandic BRCA2 mutation carriers, 1920-2000. J Natl Cancer Inst. 2006, 98: 116-122.

King MC, Motulsky AG: Human genetics. Mapping human history. Science. 2002, 298: 2342-2343.

Evans DG, Shenton A, Woodward E, Lalloo F, Howell A, Maher ER: Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a Clinical Cancer Genetics service setting: risks of breast/ovarian cancer quoted should reflect the cancer burden in the family. BMC Cancer. 2008, 8: 155-

Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002, 360: 187-195.

Cuzick J, Sestak I, Bonanni B, Costantino JP, Cummings S, DeCensi A, Dowsett M, Forbes JF, Ford L, LaCroix AZ, Mershon J, Mitlak BH, Powles T, Veronesi U, Vogel V, Wickerham DL: Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data. Lancet. 2013, 381: 1827-1834.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Goss PE, Ingle JN, Ales-Martinez JE, Cheung AM, Chlebowski RT, Wactawski-Wende J, McTiernan A, Robbins J, Johnson KC, Martin LW, Winquist E, Sarto GE, Garber JE, Fabian CJ, Pujol P, Maunsell E, Farmer P, Gelmon KA, Tu D, Richardson H: Exemestane for breast-cancer prevention in post-menopausal women. N Engl J Med. 2011, 64: 2381-2391.

Article   Google Scholar  

Cuzick J, Sestak I, Forbes JF, Dowsett M, Knox J, Cawthorn S, Saunders C, Roche N, Mansel RE, von Minckwitz G, Bonanni B, Palva T, Howell A: IBIS-II investigators: Anastrozole for prevention of breast cancer in high-risk post-menopausal women (IBIS-II): an international, double-blind, randomised placebo-controlled trial. Lancet. 2014, 383: 1041-1048.

Brown P: Prevention: targeted therapy-anastrozole prevents breast cancer. Nat Rev Clin Oncol. 2014, 11: 127-128.

Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ: Cancer statistics, 2007. CA Cancer J Clin. 2007, 57: 43-66.

Parkin DM, Boyd L, Walker LC: The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer. 2011, 105: S77-S81.

Amir E, Freedman OC, Seruga B, Evans DG: Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010, 102: 680-691.

Meads C, Ahmed I, Riley RD: A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat. 2012, 132: 365-377.

Fischer C, Kuchenbäcker K, Engel C, Zachariae S, Rhiem K, Meindl A, Rahner N, Dikow N, Plendl H, Debatin I, Grimm T, Gadzicki D, Flöttmann R, Horvath J, Schröck E, Stock F, Schäfer D, Schwaab I, Kartsonaki C, Mavaddat N, Schlegelberger B, Antoniou AC, Schmutzler R: German Consortium for Hereditary Breast and Ovarian Cancer: Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium. J Med Genet. 2013, 50: 360-367.

Evans DG, Lalloo F, Cramer A, Jones EA, Knox F, Amir E, Howell A: Addition of pathology and biomarker information significantly improves the performance of the Manchester scoring system for BRCA1 and BRCA2 testing. J Med Genet. 2009, 46: 811-817.

Kast K, Schmutzler RK, Rhiem K, Kiechle M, Fischer C, Niederacher D, Arnold N, Grimm T, Speiser D, Schlegelberger B, Varga D, Horvath J, Beer M, Briest S, Meindl A, Engel C: Validation of the Manchester scoring system for predicting BRCA1/2 mutations in 9,390 families suspected of having hereditary breast and ovarian cancer. Int J Cancer. 2014 [Epub ahead of print]

Tyrer J, Duffy SW, Cuzick J: A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004, 23: 1111-1130.

Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989, 81: 1879-1886.

MacInnis RJ, Bickerstaffe A, Apicella C, Dite GS, Dowty JG, Aujard K, Phillips KA, Weideman P, Lee A, Terry MB, Giles GG, Southey MC, Antoniou AC, Hopper JL: Prospective validation of the breast cancer risk prediction model BOADICEA and a batch-mode version BOADICEACentre. Br J Cancer. 2013, 109: 1296-1301.

Evans DG, Ingham S, Dawe S, Roberts L, Lalloo F, Brentnall AR, Stavrinos P, Howell A: Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme. Fam Cancer. 2014, 13: 189-196.

Rosner BA, Colditz GA, Hankinson SE, Sullivan-Halley J, Lacey JV, Bernstein L: Validation of Rosner-Colditz breast cancer incidence model using an independent data set, the California Teachers Study. Breast Cancer Res Treat. 2013, 142: 187-202.

Costantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J, Wieand HS: Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst. 1999, 91: 1541-1548.

Schonfeld SJ, Pee D, Greenlee RT, Hartge P, Lacey JV, Park Y, Schatzkin A, Visvanathan K, Pfeiffer RM: Effect of changing breast cancer incidence rates on the calibration of the Gail model. J Clin Oncol. 2010, 28: 2411-2417.

Amir E, Evans DG, Shenton A, Lalloo F, Moran A, Boggis C, Wilson M, Howell A: Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J Med Genet. 2003, 40: 807-814.

Quante AS, Whittemore AS, Shriver T, Strauch K, Terry MB: Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance. Breast Cancer Res. 2012, 14: R144-

Powell M, Jamshidian F, Cheyne K, Nititham J, Prebil LA, Ereman R: Assessing breast cancer risk models in Marin County, a population with high rates of delayed childbirth. Clin Breast Cancer. 2014, 14: 212-220.

Huo CW, Chew GL, Britt KL, Ingman WV, Henderson MA, Hopper JL, Thompson EW: Mammographic density-a review on the current understanding of its association with breast cancer. Breast Cancer Res Treat. 2014, 144: 479-502.

Cummings SR, Tice JA, Bauer S, Browner WS, Cuzick J, Ziv E, Vogel V, Shepherd J, Vachon C, Smith-Bindman R, Kerlikowske K: Prevention of breast cancer in post-menopausal women: approaches to estimating and reducing risk. J Natl Cancer Inst. 2009, 101: 384-398.

Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, Bolla MK, Wang Q, Dicks E, Lee A, Turnbull C, Rahman N, Fletcher O, Peto J, Gibson L, Dos Santos Silva I, Nevanlinna H, Muranen TA, Aittomäki K, Blomqvist C, Czene K, Irwanto A, Liu J, Waisfisz Q, Meijers-Heijboer H, Adank M: Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013, 45: 353-361.

Burton H, Chowdhury S, Dent T, Hall A, Pashayan N, Pharoah P: Public health implications from COGS and potential for risk stratification and screening. Nat Genet. 2013, 45: 349-351.

Hormones E, Group BCC, Key TJ, Appleby PN, Reeves GK, Roddam AW, Helzlsouer KJ, Alberg AJ, Rollison DE, Dorgan JF, Brinton LA, Overvad K, Kaaks R, Trichopoulou A, Clavel-Chapelon F, Panico S, Duell EJ, Peeters PH, Rinaldi S, Fentiman IS, Dowsett M, Manjer J, Lenner P, Hallmans G, Baglietto L, English DR, Giles GG, Hopper JL, Severi G, Morris HA, et al: Circulating sex hormones and breast cancer risk factors in post-menopausal women: reanalysis of 13 studies. Br J Cancer. 2011, 105: 709-722.

Article   CAS   Google Scholar  

McCormack VA, dos Santos SI: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006, 15: 1159-1169.

Tice JA, Cummings SR, Ziv E, Kerlikowske K: Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005, 94: 115-122.

Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DS, Geller BM, Rosenberg R, Yankaskas BC, Kerlikowske K: Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006, 98: 1204-1214.

Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail MH: Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006, 98: 1215-1226.

Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K: Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008, 148: 337-347.

Cecchini RS, Costantino JP, Cauley JA, Cronin WM, Wickerham DL, Bandos H, Weissfeld JL, Wolmark N: Baseline mammographic breast density and the risk of invasive breast cancer in post-menopausal women participating in the NSABP study of tamoxifen and raloxifene (STAR). Cancer Prev Res. 2012, 5: 1321-1329.

Vachon CM, Fowler EE, Tiffenberg G, Scott CG, Pankratz VS, Sellers TA, Heine JJ: Comparison of percent density from raw and processed full-field digital mammography data. Breast Cancer Res. 2013, 15: R1-

Keller BM, Chen J, Conant EF, Kontos D: Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer. Proc SPIE. 2014, 9035: 90351D-

PubMed Central   Google Scholar  

Cheddad A, Czene K, Shepherd JA, Li J, Hall P, Humphreys K: Enhancement of mammographic density measures in breast cancer risk prediction. Cancer Epidemiol Biomarkers Prev. 2014, 23: 1314-1323.

Zheng B, Sumkin JH, Zuley ML, Wang X, Klym AH, Gur D: Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol. 2012, 81: 3222-3228.

Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ: The quantitative analysis of mammographic densities. Phys Med Biol. 1994, 39: 1629-1638.

Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baglietto L, Vachon C, Bakker MF, Giles GG, Chia KS, Czene K, Eriksson L, Hall P, Hartman M, Warren RM, Hislop G, Chiarelli AM, Hopper JL, Krishnan K, Li J, Li Q, Pagano I, Rosner BA, Wong CS, Scott C, Stone J, Maskarinec G, Boyd NF, van Gils CH, Tamimi RM: Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. 2014, 106: dju078-

Nickson C, Arzhaeva Y, Aitken Z, Elgindy T, Buckley M, Li M, English DR, Kavanagh AM: AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes. Breast Cancer Res. 2013, 15: R80-

Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, Malkov S, Vittinghoff E, Cummings SR: Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2011, 20: 1473-1482.

Wang J, Azziz A, Fan B, Malkov S, Klifa C, Newitt D, Yitta S, Hylton N, Kerlikowske K, Shepherd JA: Agreement of mammographic measures of volumetric breast density to MRI. PLoS One. 2013, 8: e81653-

Article   PubMed   PubMed Central   CAS   Google Scholar  

Gubern-Mérida A, Kallenberg M, Platel B, Mann RM, Martí R, Karssemeijer N: Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One. 2014, 9: e85952-

Pharoah PD, Antoniou AC, Easton DF, Ponder BA: Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008, 358: 2796-2803.

Mealiffe ME, Stokowski RP, Rhees BK, Prentice RL, Pettinger M, Hinds DA: Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010, 102: 1618-1627.

Wacholder S, Hartge P, Prentice R, Garcia-Closas M, Feigelson HS, Diver WR, Thun MJ, Cox DG, Hankinson SE, Kraft P, Rosner B, Berg CD, Brinton LA, Lissowska J, Sherman ME, Chlebowski R, Kooperberg C, Jackson RD, Buckman DW, Hui P, Pfeiffer R, Jacobs KB, Thomas GD, Hoover RN, Gail MH, Chanock SJ, Hunter DJ: Performance of common genetic variants in breast-cancer risk models. N Engl J Med. 2010, 362: 986-993.

Gail MH: Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst. 2009, 101: 959-963.

Dite GS, Mahmoodi M, Bickerstaffe A, Hammet F, Macinnis RJ, Tsimiklis H, Dowty JG, Apicella C, Phillips KA, Giles GG, Southey MC, Hopper JL: Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013, 139: 887-896.

Comen E, Balistreri L, Gönen M, Dutra-Clarke A, Fazio M, Vijai J, Stadler Z, Kauff N, Kirchhoff T, Hudis C, Offit K, Robson M: Discriminatory accuracy and potential clinical utility of genomic profiling for breast cancer risk in BRCA-negative women. Breast Cancer Res Treat. 2011, 127: 479-487.

Brentnall AR, Evans DG, Cuzick J: Distribution of breast cancer risk from SNPs and classical risk factors in women of routine screening age in the UK. Br J Cancer. 2014, 110: 827-828.

Evans DG, Warwick J, Astley SM, Stavrinos P, Sahin S, Ingham S, McBurney H, Eckersley B, Harvie M, Wilson M, Beetles U, Warren R, Hufton A, Sergeant JC, Newman WG, Buchan I, Cuzick J, Howell A: Assessing individual breast cancer risk within the U.K. National Health Service Breast Screening Program: a new paradigm for cancer prevention. Cancer Prev Res (Phila). 2012, 5: 943-951.

Stacey SN, Manolescu A, Sulem P, Thorlacius S, Gudjonsson SA, Jonsson GF, Jakobsdottir M, Bergthorsson JT, Gudmundsson J, Aben KK, Strobbe LJ, Swinkels DW, van Engelenburg KC, Henderson BE, Kolonel LN, Le Marchand L, Millastre E, Andres R, Saez B, Lambea J, Godino J, Polo E, Tres A, Picelli S, Rantala J, Margolin S, Jonsson T, Sigurdsson H, Jonsdottir T, Hrafnkelsson J, et al: Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2008, 40: 703-706.

Garcia-Closas M, Couch FJ, Lindstrom S, Michailidou K, Schmidt MK, Brook MN, Orr N, Rhie SK, Riboli E, Feigelson HS, Le Marchand L, Buring JE, Eccles D, Miron P, Fasching PA, Brauch H, Chang-Claude J, Carpenter J, Godwin AK, Nevanlinna H, Giles GG, Cox A, Hopper JL, Bolla MK, Wang Q, Dennis J, Dicks E, Howat WJ, Schoof N, Bojesen SE, et al: Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet. 2013, 45: 392-398.

Purrington KS, Slettedahl S, Bolla MK, Michailidou K, Czene K, Nevanlinna H, Bojesen SE, Andrulis IL, Cox A, Hall P, Carpenter J, Yannoukakos D, Haiman CA, Fasching PA, Mannermaa A, Winqvist R, Brenner H, Lindblom A, Chenevix-Trench G, Benitez J, Swerdlow A, Kristensen V, Guénel P, Meindl A, Darabi H, Eriksson M, Fagerholm R, Aittomäki K, Blomqvist C, Nordestgaard BG, et al: Genetic variation in mitotic regulatory pathway genes is associated with breast tumor grade. Hum Mol Genet. 2014, [Epub ahead of print]

Purrington KS, Slager S, Eccles D, Yannoukakos D, Fasching PA, Miron P, Carpenter J, Chang-Claude J, Martin NG, Montgomery GW, Kristensen V, Anton-Culver H, Goodfellow P, Tapper WJ, Rafiq S, Gerty SM, Durcan L, Konstantopoulou I, Fostira F, Vratimos A, Apostolou P, Konstanta I, Kotoula V, Lakis S, Dimopoulos MA, Skarlos D, Pectasides D, Fountzilas G, Beckmann MW, Hein A, et al: Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer. Carcinogenesis. 2014, 35: 1012-1019.

Garcia-Closas M, Burak Gunsoy N, Chatterjee N: Combined effects of genetic and environmental risk factors: implications for prevention of breast cancer. J Natl Cancer Inst. -in press

Key TJ, Appleby PN, Reeves GK, Roddam A, Dorgan JF, Longcope C, Stanczyk FZ, Stephenson HE, Falk RT, Miller R, Schatzkin A, Allen DS, Fentiman IS, Key TJ, Wang DY, Dowsett M, Thomas HV, Hankinson SE, Toniolo P, Akhmedkhanov A, Koenig K, Shore RE, Zeleniuch-Jacquotte A, Berrino F, Muti P, Micheli A, Krogh V, Sieri S, Pala V, et al: Body mass index, serum sex hormones, and breast cancer risk in post-menopausal women. J Natl Cancer Inst. 2003, 95: 1218-1226.

Hormones E, Group BCC, Key TJ, Appleby PN, Reeves GK, Travis RC, Alberg AJ, Barricarte A, Berrino F, Krogh V, Sieri S, Brinton LA, Dorgan JF, Dossus L, Dowsett M, Eliassen AH, Fortner RT, Hankinson SE, Helzlsouer KJ, Hoffman-Bolton J, Comstock GW, Kaaks R, Kahle LL, Muti P, Overvad K, Peeters PH, Riboli E, Rinaldi S, Rollison DE, Stanczyk FZ, et al: Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol. 2013, 14: 1009-1019.

James RE, Lukanova A, Dossus L, Becker S, Rinaldi S, Tjønneland A, Olsen A, Overvad K, Mesrine S, Engel P, Clavel-Chapelon F, Chang-Claude J, Vrieling A, Boeing H, Schütze M, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Krogh V, Panico S, Tumino R, Sacerdote C, Rodríguez L, Buckland G, Sánchez MJ, Amiano P, Ardanaz E, Bueno-de-Mesquita B, Ros MM, et al: Post-menopausal serum sex steroids and risk of hormone receptor-positive and -negative breast cancer: a nested case-control study. Cancer Prev Res (Phila). 2011, 4: 1626-1635.

Kaaks R, Tikk K, Sookthai D, Schock H, Johnson T, Tjønneland A, Olsen A, Overvad K, Clavel-Chapelon F, Dossus L, Baglietto L, Rinaldi S, Chajes V, Romieu I, Boeing H, Schütze M, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Sieri S, Tumino R, Ricceri F, Mattiello A, Buckland G, Ramón Quirós J, Sánchez MJ, Amiano P, Chirlaque MD, Barricarte A, et al: Premenopausal serum sex hormone levels in relation to breast cancer risk, overall and by hormone receptor status - results from the EPIC cohort. Int J Cancer. 2014, 134: 1947-1957.

Ritte R, Lukanova A, Berrino F, Dossus L, Tjønneland A, Olsen A, Overvad TF, Overvad K, Clavel-Chapelon F, Fournier A, Fagherazzi G, Rohrmann S, Teucher B, Boeing H, Aleksandrova K, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Sieri S, Panico S, Tumino R, Vineis P, Quir’s JR, Buckland G, Sánchez MJ, Amiano P, Chirlaque MD, Ardanaz E, Sund M, et al: Adiposity, hormone replacement therapy use and breast cancer risk by age and hormone receptor status: a large prospective cohort study. Breast Cancer Res. 2012, 14: R76-

Jones ME, Schoemaker MJ, Rae M, Folkerd EJ, Dowsett M, Ashworth A, Swerdlow AJ: Reproducibility of estradiol and testosterone levels in post-menopausal women over 5 years: results from the Breakthrough Generations Study. Am J Epidemiol. 2014, 179: 1128-1133.

Jones ME, Schoemaker M, Rae M, Folkerd EJ, Dowsett M, Ashworth A, Swerdlow AJ: Changes in estradiol and testosterone levels in post-menopausal women after changes in body mass index. J Clin Endocrinol Metab. 2013, 98: 2967-2974.

Tworoger SS, Zhang X, Eliassen AH, Qian J, Colditz GA, Willett WC, Rosner BA, Kraft P, Hankinson SE: Inclusion of endogenous hormone levels in risk prediction models of post-menopausal breast cancer. J Clin Oncol 2014. [Epub ahead of print].,

Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies. Lancet Oncol. 2010, 11: 530-542.

Tworoger SS, Eliassen AH, Zhang X, Qian J, Sluss PM, Rosner BA, Hankinson SE: A 20-year prospective study of plasma prolactin as a risk marker of breast cancer development. Cancer Res. 2013, 73: 4810-4819.

Tikk K, Sookthai D, Johnson T, Rinaldi S, Romieu I, Tjønneland A, Olsen A, Overvad K, Clavel-Chapelon F, Baglietto L, Boeing H, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Pala V, Tumino R, Rosso S, Panico S, Agudo A, Menéndez V, Sánchez MJ, Amiano P, Huerta Castaño JM, Ardanaz E, Bueno-de-Mesquita HB, Monninkhof E, Onland-Moret C, Andersson A, Sund M, et al: Circulating prolactin and breast cancer risk among pre- and post-menopausal women in the EPIC cohort. Ann Oncol. 2014, 25: 1422-1428.

Kaaks R, Johnson T, Tikk K, Sookthai D, Tjønneland A, Roswall N, Overvad K, Clavel-Chapelon F, Boutron-Ruault MC, Dossus L, Rinaldi S, Romieu I, Boeing H, Schütze M, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Grioni S, Tumino R, Sacerdote C, Panico S, Buckland G, Argüelles M, Sánchez MJ, Amiano P, Chirlaque MD, Ardanaz E, Bueno-de-Mesquita HB, van Gils CH, et al: Insulin-like growth factor I and risk of breast cancer by age and hormone receptor status - a prospective study within the EPIC cohort. Int J Cancer. 2014, 134: 2683-2690.

Macdonald IK, Allen J, Murray A, Parsy-Kowalska CB, Healey GF, Chapman CJ, Sewell HF, Robertson JF: Development and validation of a high throughput system for discovery of antigens for autoantibody detection. PLoS One. 2012, 7: e40759-

Sharma P, Sahni NS, Tibshirani R, Skaane P, Urdal P, Berghagen H, Jensen M, Kristiansen L, Moen C, Sharma P, Zaka A, Arnes J, Sauer T, Akslen LA, Schlichting E, Børresen-Dale AL, Lönneborg A: Early detection of breast cancer based on gene-expression patterns in peripheral blood cells. Breast Cancer Res. 2005, 7: R634-R644.

Almouzni G, Altucci L, Amati B, Ashley N, Baulcombe D, Beaujean N, Bock C, Bongcam-Rudloff E, Bousquet J, Braun S, Paillerets BB, Bussemakers M, Clarke L, Conesa A, Estivill X, Fazeli A, Grgurević N, Gut I, Heijmans BT, Hermouet S, Houwing-Duistermaat J, Iacobucci I, Ilaš J, Kandimalla R, Krauss-Etschmann S, Lasko P, Lehmann S, Lindroth A, Majdič G, Marcotte E, et al: Relationship between genome and epigenome - challenges and requirements for future research. BMC Genomics. 2014, 15: 487-

Anderson KS, Sibani S, Wallstrom G, Qiu J, Mendoza EA, Raphael J, Hainsworth E, Montor WR, Wong J, Park JG, Lokko N, Logvinenko T, Ramachandran N, Godwin AK, Marks J, Engstrom P, Labaer J: Protein microarray signature of autoantibody biomarkers for the early detection of breast cancer. J Proteome Res. 2011, 10: 85-96.

Balbo S, Turesky RJ, Villalta PW: DNA adductomics. Chem Res Toxicol. 2014, 27: 356-366.

Advani P, Moreno-Aspitia A: Current strategies for the prevention of breast cancer. Breast Cancer (Dove Med Press). 2014, 6: 59-71.

CAS   Google Scholar  

Chlebowski RT: Current concepts in breast cancer chemoprevention. Pol Arch Med Wewn. 2014, 124: 191-199.

PubMed   Google Scholar  

den Hollander P, Savage MI, Brown PH: Targeted therapy for breast cancer prevention. Front Oncol. 2013, 3: 250-

Steward WP, Brown K: Cancer chemoprevention: a rapidly evolving field. Br J Cancer. 2013, 109: 1-7.

Colditz GA, Bohlke K, Berkey CS: Breast cancer risk accumulation starts early: prevention must also. Breast Cancer Res Treat. 2014, 145: 567-579.

Harvie M, Howell A: Energy restriction and the prevention of breast cancer. Proc Nutr Soc. 2012, 71: 263-275.

Vogel VG, Costantino JP, Wickerham DL, Cronin WM, Cecchini RS, Atkins JN, Bevers TB, Fehrenbacher L, Pajon ER, Wade JL 3rd, Robidoux A, Margolese RG, James J, Runowicz CD, Ganz PA, Reis SE, McCaskill-Stevens W, Ford LG, Jordan VC, Wolmark N: National Surgical Adjuvant Breast and Bowel Project: Update of the National Surgical Adjuvant Breast and Bowel Project Study of Tamoxifen and Raloxifene (STAR) P-2 Trial: preventing breast cancer. Cancer Prev Res (Phila) 2010, 3:696-706.,

Manson JE, Chlebowski RT, Stefanick ML, Aragaki AK, Rossouw JE, Prentice RL, Anderson G, Howard BV, Thomson CA, LaCroix AZ, Wactawski-Wende J, Jackson RD, Limacher M, Margolis KL, Wassertheil-Smoller S, Beresford SA, Cauley JA, Eaton CB, Gass M, Hsia J, Johnson KC, Kooperberg C, Kuller LH, Lewis CE, Liu S, Martin LW, Ockene JK, O'Sullivan MJ, Powell LH, Simon MS, et al: Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women’s Health Initiative randomized trials. JAMA. 2013, 310: 1353-1368.

Roth JA, Etzioni R, Waters TM, Pettinger M, Rossouw JE, Anderson GL, Chlebowski RT, Manson JE, Hlatky M, Johnson KC, Ramsey SD: Economic return from the Women’s Health Initiative estrogen plus progestin clinical trial: a modeling study. Ann Intern Med. 2014, 160: 594-602.

Anderson GL, Chlebowski RT, Aragaki AK, Kuller LH, Manson JE, Gass M, Bluhm E, Connelly S, Hubbell FA, Lane D, Martin L, Ockene J, Rohan T, Schenken R, Wactawski-Wende J: Conjugated equine oestrogen and breast cancer incidence and mortality in post-menopausal women with hysterectomy: extended follow-up of the Women’s Health Initiative randomised placebo-controlled trial. Lancet Oncol. 2012, 13: 476-486.

Howell A, Cuzick J: Oestrogen and breast cancer: results from the WHI trial. Lancet Oncol. 2012, 13: 437-438.

World Cancer Research Fund International: Cancer preventability estimates for food, nutrition, body fatness, and physical activity.., [ http://www.wcrf.org/cancer_statistics/preventability_estimates/preventability_estimates_food.php ]

Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M: Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008, 371: 569-578.

Harvie M, Howell A, Vierkant RA, Kumar N, Cerhan JR, Kelemen LE, Folsom AR, Sellers TA: Association of gain and loss of weight before and after menopause with risk of post-menopausal breast cancer in the Iowa women’s health study. Cancer Epidemiol Biomarkers Prev. 2005, 14: 656-661.

Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE: Adult weight change and risk of post-menopausal breast cancer. JAMA. 2006, 296: 193-201.

Cecchini RS, Costantino JP, Cauley JA, Cronin WM, Wickerham DL, Land SR, Weissfeld JL, Wolmark N: Body mass index and the risk for developing invasive breast cancer among high-risk women in NSABP P-1 and STAR breast cancer prevention trials. Cancer Prev Res (Phila). 2012, 5: 583-592.

Lindström J, Ilanne-Parikka P, Peltonen M, Aunola S, Eriksson JG, Hemiö K, Hämäläinen H, Härkönen P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Mannelin M, Paturi M, Sundvall J, Valle TT, Uusitupa M, Tuomilehto J: Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet. 2006, 368: 1673-1679.

Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ, Brenneman AT, Brown-Friday JO, Goldberg R, Venditti E, Nathan DM: 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009, 374: 1677-1686.

Ebrahim S, Taylor F, Ward K, Beswick A, Burke M, Davey Smith G: Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev. 2011, 1: CD001561-

Google Scholar  

Van Gaal LF, Mertens IL, Ballaux D: What is the relationship between risk factor reduction and degree of weight loss?. Eur Heart J Suppl. 2005, 7: L21-L26.

Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, Margolis KL, Limacher MC, Manson JE, Parker LM, Paskett E, Phillips L, Robbins J, Rossouw JE, Sarto GE, Shikany JM, Stefanick ML, Thomson CA, Van Horn L, Vitolins MZ, Wactawski-Wende J, Wallace RB, Wassertheil-Smoller S, Whitlock E, Yano K, Adams-Campbell L, Anderson GL, Assaf AR, Beresford SA, Black HR, et al: Low-fat dietary pattern and risk of invasive breast cancer: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006, 295: 629-642.

Chlebowski RT, Blackburn GL, Thomson CA, Nixon DW, Shapiro A, Hoy MK, Goodman MT, Giuliano AE, Karanja N, McAndrew P, Hudis C, Butler J, Merkel D, Kristal A, Caan B, Michaelson R, Vinciguerra V, Del Prete S, Winkler M, Hall R, Simon M, Winters BL, Elashoff RM: Dietary fat reduction and breast cancer outcome: interim efficacy results from the Women’s Intervention Nutrition Study. J Natl Cancer Inst. 2006, 98: 1767-1776.

Pierce JP, Natarajan L, Caan BJ, Parker BA, Greenberg ER, Flatt SW, Rock CL, Kealey S, Al-Delaimy WK, Bardwell WA, Carlson RW, Emond JA, Faerber S, Gold EB, Hajek RA, Hollenbach K, Jones LA, Karanja N, Madlensky L, Marshall J, Newman VA, Ritenbaugh C, Thomson CA, Wasserman L, Stefanick ML: Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: the Women’s Healthy Eating and Living (WHEL) randomized trial. JAMA. 2007, 298: 289-298.

Jung S, Spiegelman D, Baglietto L, Bernstein L, Boggs DA, van den Brandt PA, Buring JE, Cerhan JR, Gaudet MM, Giles GG, Goodman G, Hakansson N, Hankinson SE, Helzlsouer K, Horn-Ross PL, Inoue M, Krogh V, Lof M, McCullough ML, Miller AB, Neuhouser ML, Palmer JR, Park Y, Robien K, Rohan TE, Scarmo S, Schairer C, Schouten LJ, Shikany JM, Sieri S, et al: Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J Natl Cancer Inst. 2013, 105: 219-236.

Eliassen AH, Hendrickson SJ, Brinton LA, Buring JE, Campos H, Dai Q, Dorgan JF, Franke AA, Gao YT, Goodman MT, Hallmans G, Helzlsouer KJ, Hoffman-Bolton J, Hultén K, Sesso HD, Sowell AL, Tamimi RM, Toniolo P, Wilkens LR, Winkvist A, Zeleniuch-Jacquotte A, Zheng W, Hankinson SE: Circulating carotenoids and risk of breast cancer: pooled analysis of eight prospective studies. J Natl Cancer Inst. 2012, 104: 1905-1916.

Catsburg C, Miller AB, Rohan TE: Adherence to cancer prevention guidelines and risk of breast cancer. Int J Cancer. 2014, [Epub ahead of print]

Thomson CA, McCullough ML, Wertheim BC, Chlebowski RT, Martinez ME, Stefanick ML, Rohan TE, Manson JE, Tindle HA, Ockene J, Vitolins MZ, Wactawski-Wende J, Sarto GE, Lane DS, Neuhouser ML: Nutrition and physical activity cancer prevention guidelines, cancer risk, and mortality in the women’s health initiative. Cancer Prev Res (Phila). 2014, 7: 42-53.

Link LB, Canchola AJ, Bernstein L, Clarke CA, Stram DO, Ursin G, Horn-Ross PL: Dietary patterns and breast cancer risk in the California Teachers Study cohort. Am J Clin Nutr. 2013, 98: 1524-1532.

Albuquerque RC, Baltar VT, Marchioni DM: Breast cancer and dietary patterns: a systematic review. Nutr Rev. 2014, 72: 1-17.

Ferrari P, Rinaldi S, Jenab M, Lukanova A, Olsen A, Tjønneland A, Overvad K, Clavel-Chapelon F, Fagherazzi G, Touillaud M, Kaaks R, von Rüsten A, Boeing H, Trichopoulou A, Lagiou P, Benetou V, Grioni S, Panico S, Masala G, Tumino R, Polidoro S, Bakker MF, van Gils CH, Ros MM, Bueno-de-Mesquita HB, Krum-Hansen S, Engeset D, Skeie G, Pilar A, Sánchez MJ, et al: Dietary fiber intake and risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition study. Am J Clin Nutr. 2013, 97: 344-353.

Deschasaux M, Zelek L, Pouchieu C, His M, Hercberg S, Galan P, Latino-Martel P, Touvier M: Prospective association between dietary fiber intake and breast cancer risk. PLoS One. 2013, 8: e79718-

Aune D, Chan DS, Vieira AR, Rosenblatt DA, Vieira R, Greenwood DC, Norat T: Fruits, vegetables and breast cancer risk: a systematic review and meta-analysis of prospective studies. Breast Cancer Res Treat. 2012, 134: 479-493.

Aune D, Chan DS, Vieira AR, Navarro Rosenblatt DA, Vieira R, Greenwood DC, Norat T: Dietary compared with blood concentrations of carotenoids and breast cancer risk: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr. 2012, 96: 356-373.

Fung TT, Chiuve SE, Willett WC, Hankinson SE, Hu FB, Holmes MD: Intake of specific fruits and vegetables in relation to risk of estrogen receptor-negative breast cancer among post-menopausal women. Breast Cancer Res Treat. 2013, 138: 925-930.

Health and Social Care Information Centre: Health Survey for England (2102).., [ http://www.hscic.gov.uk/catalogue/PUB13218 ]

Hastert TA, Beresford SA, Patterson RE, Kristal AR, White E: Adherence to WCRF/AICR cancer prevention recommendations and risk of post-menopausal breast cancer. Cancer Epidemiol Biomarkers Prev. 2013, 22: 1498-1508.

Wiseman M: The second World Cancer Research Fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008, 67: 253-256.

Lynch BM, Neilson HK, Friedenreich CM: Physical activity and breast cancer prevention. Recent Results Cancer Res. 2011, 186: 13-42.

Chen WY, Rosner B, Hankinson SE, Colditz GA, Willett WC: Moderate alcohol consumption during adult life, drinking patterns, and breast cancer risk. JAMA. 2011, 306: 1884-1890.

Zhang SM, Hankinson SE, Hunter DJ, Giovannucci EL, Colditz GA, Willett WC: Folate intake and risk of breast cancer characterized by hormone receptor status. Cancer Epidemiol Biomarkers Prev. 2005, 14: 2004-2008.

Ferrari P, Licaj I, Muller DC, Kragh Andersen P, Johansson M, Boeing H, Weiderpass E, Dossus L, Dartois L, Fagherazzi G, Bradbury KE, Khaw KT, Wareham N, Duell EJ, Barricarte A, Molina-Montes E, Sanchez CN, Arriola L, Wallström P, Tjønneland A, Olsen A, Trichopoulou A, Benetou V, Trichopoulos D, Tumino R, Agnoli C, Sacerdote C, Palli D, Li K, Kaaks R, et al: Lifetime alcohol use and overall and cause-specific mortality in the European Prospective Investigation into Cancer and nutrition (EPIC) study. BMJ Open. 2014, 4: e005245-

Pike MC, Krailo MD, Henderson BE, Casagrande JT, Hoel DG: ‘Hormonal’ risk factors, ‘breast tissue age’ and the age-incidence of breast cancer. Nature. 1983, 303: 767-770.

Colditz GA, Frazier AL: Models of breast cancer show that risk is set by events of early life: prevention efforts must shift focus. Cancer Epidemiol Biomarkers Prev. 1995, 4: 567-571.

CAS   PubMed   Google Scholar  

Liu Y, Colditz GA, Rosner B, Berkey CS, Collins LC, Schnitt SJ, Connolly JL, Chen WY, Willett WC, Tamimi RM: Alcohol intake between menarche and first pregnancy: a prospective study of breast cancer risk. J Natl Cancer Inst. 2013, 105: 1571-1578.

McNally S, Martin F: Molecular regulators of pubertal mammary gland development. Ann Med. 2011, 43: 212-234.

Gjorevski N, Nelson CM: Integrated morphodynamic signalling of the mammary gland. Nat Rev Mol Cell Biol. 2011, 12: 581-593.

Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, Forbes JF, Warren RM: Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. J Natl Cancer Inst. 2011, 103: 744-752.

Komm BS, Mirkin S: An overview of current and emerging SERMs. J Steroid Biochem Mol Biol. 2014, 143: 207-222.

Johansson H, Bonanni B, Gandini S, Guerrieri-Gonzaga A, Cazzaniga M, Serrano D, Macis D, Puccio A, Sandri MT, Gulisano M, Formelli F, Decensi A: Circulating hormones and breast cancer risk in premenopausal women: a randomized trial of low-dose tamoxifen and fenretinide. Breast Cancer Res Treat. 2013, 142: 569-578.

Clarke RB, Howell A, Potten CS, Anderson E: Dissociation between steroid receptor expression and cell proliferation in the human breast. Cancer Res. 1997, 57: 4987-4991.

Brisken C, O’Malley B: Hormone action in the mammary gland. Cold Spring Harb Perspect Biol. 2010, 2: a003178-

Shinomiya I, Marlow R, Buchupalli B, Gazinska P, Brown J, Catchpole S, Liu S, Barkan A, Wicha M, Purushotham A, Burchell J, Pinder S, Dontu G: Growth hormone is secreted by normal breast epithelium upon progesterone stimulation and increases proliferation of stem/progenitor cells. Stem Cell Reports. 2014, 2: 780-793.

Meier-Abt F, Bentires-Alj M: How pregnancy at early age protects against breast cancer. Trends Mol Med. 2014, 20: 143-153.

Chabbert-Buffet N, Meduri G, Bouchard P, Spitz IM: Selective progesterone receptor modulators and progesterone antagonists: mechanisms of action and clinical applications. Hum Reprod Update. 2005, 11: 293-307.

Harvard Medical School: Growing up today study. In., [ http://www.gutsweb.org ]

Baer HJ, Tworoger SS, Hankinson SE, Willett WC: Body fatness at young ages and risk of breast cancer throughout life. Am J Epidemiol. 2010, 171: 1183-1194.

Hilakivi-Clarke L, Forsén T, Eriksson JG, Luoto R, Tuomilehto J, Osmond C, Barker DJ: Tallness and overweight during childhood have opposing effects on breast cancer risk. Br J Cancer. 2001, 85: 1680-1684.

Robinson WR, Tse CK, Olshan AF, Troester MA: Body size across the life course and risk of premenopausal and post-menopausal breast cancer in Black women, the Carolina Breast Cancer Study, 1993-2001. Cancer Causes Control. 2014, 25: 1101-1117.

Suzuki R, Iwasaki M, Inoue M, Sasazuki S, Sawada N, Yamaji T, Shimazu T, Tsugane S: Body weight at age 20 years, subsequent weight change and breast cancer risk defined by estrogen and progesterone receptor status - the Japan public health center-based prospective study. Int J Cancer. 2011, 129: 1214-1224.

Barnes-Josiah D, Potter JD, Sellers TA, Himes JH: Early body size and subsequent weight gain as predictors of breast cancer incidence (Iowa, United States). Cancer Causes Control. 1995, 6: 112-118.

Feigelson HS, Jonas CR, Teras LR, Thun MJ, Calle EE: Weight gain, body mass index, hormone replacement therapy, and post-menopausal breast cancer in a large prospective study. Cancer Epidemiol Biomarkers Prev. 2004, 13: 220-224.

Krishnan K, Bassett JK, MacInnis RJ, English DR, Hopper JL, McLean C, Giles GG, Baglietto L: Associations between weight in early adulthood, change in weight, and breast cancer risk in post-menopausal women. Cancer Epidemiol Biomarkers Prev. 2013, 22: 1409-1416.

Magnusson CM, Roddam AW: Breast cancer and childhood anthropometry: emerging hypotheses?. Breast Cancer Res. 2005, 7: 83-

Poole EM, Tworoger SS, Hankinson SE, Schernhammer ES, Pollak MN, Baer HJ: Body size in early life and adult levels of insulin-like growth factor 1 and insulin-like growth factor binding protein 3. Am J Epidemiol. 2011, 174: 642-651.

Michels KB, Terry KL, Eliassen AH, Hankinson SE, Willett WC: Adult weight change and incidence of premenopausal breast cancer. Int J Cancer. 2012, 130: 902-909.

De Stavola BL, dos Santos SI, McCormack V, Hardy RJ, Kuh DJ, Wadsworth ME: Childhood growth and breast cancer. Am J Epidemiol. 2004, 159: 671-682.

Berkey CS, Willett WC, Frazier AL, Rosner B, Tamimi RM, Rockett HR, Colditz GA: Prospective study of growth and development in older girls and risk of benign breast disease in young women. Cancer. 2011, 117: 1612-1620.

Cheng G, Buyken AE, Shi L, Karaolis-Danckert N, Kroke A, Wudy SA, Degen GH, Remer T: Beyond overweight: nutrition as an important lifestyle factor influencing timing of puberty. Nutr Rev. 2012, 70: 133-152.

Lyons TR, O’Brien J, Borges VF, Conklin MW, Keely PJ, Eliceiri KW, Marusyk A, Tan AC, Schedin P: Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2. Nat Med. 2011, 17: 1109-1115.

Bartow SA, Pathak DR, Black WC, Key CR, Teaf SR: Prevalence of benign, atypical, and malignant breast lesions in populations at different risk for breast cancer. A forensic autopsy study. Cancer. 1987, 60: 2751-2760.

Nielsen M, Thomsen JL, Primdahl S, Dyreborg U, Andersen JA: Breast cancer and atypia among young and middle-aged women: a study of 110 medicolegal autopsies. Br J Cancer. 1987, 56: 814-819.

Haricharan S, Dong J, Hein S, Reddy JP, Du Z, Toneff M, Holloway K, Hilsenbeck SG, Huang S, Atkinson R, Woodward W, Jindal S, Borges VF, Gutierrez C, Zhang H, Schedin PJ, Osborne CK, Tweardy DJ, Li Y: Mechanism and preclinical prevention of increased breast cancer risk caused by pregnancy. Elife. 2013, 2: e00996-

Britschgi A, Andraos R, Brinkhaus H, Klebba I, Romanet V, Müller U, Murakami M, Radimerski T, Bentires-Alj M: AK2/STAT5 inhibition circumvents resistance to PI3K/mTOR blockade: a rationale for cotargeting these pathways in metastatic breast cancer. Cancer Cell. 2012, 22: 796-811.

Wellings SR, Jensen HM, Marcum RG: An atlas of subgross pathology of the human breast with special reference to possible precancerous lesions. J Natl Cancer Inst. 1975, 55: 231-273.

Milanese TR, Hartmann LC, Sellers TA, Frost MH, Vierkant RA, Maloney SD, Pankratz VS, Degnim AC, Vachon CM, Reynolds CA, Thompson RA, Melton LJ, Goode EL, Visscher DW: Age-related lobular involution and risk of breast cancer. J Natl Cancer Inst. 2006, 98: 1600-1607.

McKian KP, Reynolds CA, Visscher DW, Nassar A, Radisky DC, Vierkant RA, Degnim AC, Boughey JC, Ghosh K, Anderson SS, Minot D, Caudill JL, Vachon CM, Frost MH, Pankratz VS, Hartmann LC: Novel breast tissue feature strongly associated with risk of breast cancer. J Clin Oncol. 2009, 27: 5893-5898.

Allan DJ, Howell A, Roberts SA, Williams GT, Watson RJ, Coyne JD, Clarke RB, Laidlaw IJ, Potten CS: Reduction in apoptosis relative to mitosis in histologically normal epithelium accompanies fibrocystic change and carcinoma of the premenopausal human breast. J Pathol. 1992, 167: 25-32.

Deng J, Letai A: Priming BCL-2 to kill: the combination therapy of tamoxifen and ABT-199 in ER + breast cancer. Breast Cancer Res. 2013, 15: 317-

Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DS, Vacek PM, Smith-Bindman R, Yankaskas B, Carney PA, Ballard-Barbash R: National Institutes of Health Breast Cancer Surveillance Consortium: Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst. 2007, 99: 386-395.

Work ME, Reimers LL, Quante AS, Crew KD, Whiffen A, Terry MB: Changes in mammographic density over time in breast cancer cases and women at high risk for breast cancer. Int J Cancer. 2014, 135: 1740-1744.

Hattar R, Maller O, McDaniel S, Hansen KC, Hedman KJ, Lyons TR, Lucia S, Wilson RS, Schedin P: Tamoxifen induces pleiotrophic changes in mammary stroma resulting in extracellular matrix that suppresses transformed phenotypes. Breast Cancer Res. 2009, 11: R5-

Lisanti MP, Tsirigos A, Pavlides S, Reeves KJ, Peiris-Pag’s M, Chadwick AL, Sanchez-Alvarez R, Lamb R, Howell A, Martinez-Outschoorn UE, Sotgia F: JNK1 stress signaling is hyper-activated in high breast density and the tumor stroma: connecting fibrosis, inflammation, and stemness for cancer prevention. Cell Cycle. 2014, 13: 580-599.

Gross ND, Boyle JO, Du B, Kekatpure VD, Lantowski A, Thaler HT, Weksler BB, Subbaramaiah K, Dannenberg AJ: Inhibition of Jun NH2-terminal kinases suppresses the growth of experimental head and neck squamous cell carcinoma. Clin Cancer Res. 2007, 13: 5910-5917.

Dumont N, Liu B, Defilippis RA, Chang H, Rabban JT, Karnezis AN, Tjoe JA, Marx J, Parvin B, Tlsty TD: Breast fibroblasts modulate early dissemination, tumorigenesis, and metastasis through alteration of extracellular matrix characteristics. Neoplasia. 2013, 15: 249-262.

Mattson MP, Allison DB, Fontana L, Harvie M, Longo VD, Malaisse W, Mosley M, Notterpek L, Ravussin E, Scheer F, Seyfried T, Varady K, Panda S: Meal frequency, timing and intermittent energy restriction in health and disease. J Natl Cancer Inst. in press

Arendt LM, McCready J, Keller PJ, Baker DD, Naber SP, Seewaldt V, Kuperwasser C: Obesity promotes breast cancer by CCL2-mediated macrophage recruitment and angiogenesis. Cancer Res. 2013, 73: 6080-6093.

Howe LR, Subbaramaiah K, Hudis CA, Dannenberg AJ: Molecular pathways: adipose inflammation as a mediator of obesity-associated cancer. Clin Cancer Res. 2013, 19: 6074-6083.

Lashinger LM, Ford NA, Hursting SD: Interacting inflammatory and growth factor signals underlie the obesity-cancer link. J Nutr. 2014, 144: 109-113.

de Cabo R, Carmona-Gutierrez D, Bernier M, Hall MN, Madeo F: The search for antiaging interventions: from elixirs to fasting regimens. Cell. 2014, 157: 1515-1526.

Mercken EM, Mitchell SJ, Martin-Montalvo A, Minor RK, Almeida M, Gomes AP, Scheibye-Knudsen M, Palacios HH, Licata JJ, Zhang Y, Becker KG, Khraiwesh H, González-Reyes JA, Villalba JM, Baur JA, Elliott P, Westphal C, Vlasuk GP, Ellis JL, Sinclair DA, Bernier M, de Cabo R: SRT2104 extends survival of male mice on a standard diet and preserves bone and muscle mass. Aging Cell. 2014, ?: ?-[Epub ahead of print]

Badrick E, Renehan AG: Diabetes and cancer: 5 years into the recent controversy. Eur J Cancer. 2014, 50: 2119-2125.

Goodwin PJ, Thompson AM, Stambolic V: Diabetes, metformin, and breast cancer: lilac time?. J Clin Oncol. 2012, 30: 2812-2814.

Campbell KL, McTiernan A: Exercise and biomarkers for cancer prevention studies. J Nutr. 2007, 137 (1 Suppl): 161S-169S.

Bernstein L: Exercise and breast cancer prevention. Curr Oncol Rep. 2009, 11: 490-496.

Wheatley KE, Nogueira LM, Perkins SN, Hursting SD: Differential effects of calorie restriction and exercise on the adipose transcriptome in diet-induced obese mice. J Obes. 2011, 2011: 265417-

Neilson HK, Conroy SM, Friedenreich CM: The influence of energetic factors on biomarkers of post-menopausal breast cancer risk. Curr Nutr Rep. 2014, 3: 22-34.

Zeng H, Irwin ML, Lu L, Risch H, Mayne S, Mu L, Deng Q, Scarampi L, Mitidiera M, Katsaros D, Yu H: Physical activity and breast cancer survival: an epigenetic link through reduced methylation of a tumor suppressor gene L3MBTL1. Breast Cancer Res Treat. 2012, 133: 127-135.

Shammas MA: Telomeres, lifestyle, cancer, and aging. Curr Opin Clin Nutr Metab Care. 2011, 14: 28-34.

Ludlow AT, Roth SM: Physical activity and telomere biology: exploring the link with aging-related disease prevention. J Aging Res. 2011, 21: 2011-

Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB: Elevated C-reactive protein levels in overweight and obese adults. JAMA. 1999, 282: 2131-2135.

Esposito K, Pontillo A, Di Palo C, Giugliano G, Masella M, Marfella R, Giugliano D: Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA. 2003, 289: 1799-1804.

Jung SH, Park HS, Kim KS, Choi WH, Ahn CW, Kim BT, Kim SM, Lee SY, Ahn SM, Kim YK, Kim HJ, Kim DJ, Lee KW: Effect of weight loss on some serum cytokines in human obesity: increase in IL-10 after weight loss. J Nutr Biochem. 2008, 19: 371-375.

Thompson HJ, Jiang W, Zhu Z: Candidate mechanisms accounting for effects of physical activity on breast carcinogenesis. IUBMB Life. 2009, 61: 895-901.

Strasser-Weippl K, Goss PE: Suitable trial designs and cohorts for preventive breast cancer agents. Nat Rev Clin Oncol. 2013, 10: 677-687.

Moyer VA: Medications to decrease the risk for breast cancer in women: recommendations from the U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2013, 159: 698-708.

Freedman AN, Yu B, Gail MH, Costantino JP, Graubard BI, Vogel VG, Anderson GL, McCaskill-Stevens W: Benefit/risk assessment for breast cancer chemoprevention with raloxifene or tamoxifen for women age 50 years or older. J Clin Oncol. 2011, 29: 2327-2333.

Visvanathan K, Hurley P, Bantug E, Brown P, Col NF, Cuzick J, Davidson NE, Decensi A, Fabian C, Ford L, Garber J, Katapodi M, Kramer B, Morrow M, Parker B, Runowicz C, Vogel VG, Wade JL, Lippman SM: Use of pharmacologic interventions for breast cancer risk reduction: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol. 2013, 31: 2942-2962.

Evans DG, Graham J, O’Connell S, Arnold S, Fitzsimmons D: Familial breast cancer: summary of updated NICE guidance. BMJ. 2013, 346: f3829-

Kushi LH, Doyle C, McCullough M, Rock CL, Demark-Wahnefried W, Bandera EV, Gapstur S, Patel AV, Andrews K, Gansler T, Society AC: Nutrition and Physical Activity Guidelines Advisory Committee: American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity. CA Cancer J Clin. 2010, 2012 (62): 30-67.

Norat T, Aune D, Chan D, Romaguera D: Fruits and vegetables: updating the epidemiologic evidence for the WCRF/AICR lifestyle recommendations for cancer prevention. Cancer Treat Res. 2014, 159: 35-50.

Anderson AS, Mackison D, Boath C, Steele R: Promoting changes in diet and physical activity in breast and colorectal cancer screening settings: an unexplored opportunity for endorsing healthy behaviors. Cancer Prev Res (Phila). 2013, 6: 165-172.

US Institute of Medicine: Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation.., [ http://www.iom.edu/~/media/Files/Report%20Files/2012/APOP/APOP_insert.pdf ]

US Institute of Medicine: The Current State of Obesity Solutions in the United States - Workshop Summary.., [ http://www.iom.edu/Reports/2014/The-Current-State-of-Obesity-Solutions-in-the-United-States.aspx ]

Colditz GA, Wolin KY, Gehlert S: Applying what we know to accelerate cancer prevention. Sci Transl Med. 2012, 4: 127rv4-

Download references

Acknowledgments

The authors would like to acknowledge the help given by Breast Cancer Campaign staff during the production of this review.

Author information

Authors and affiliations.

Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT, Manchester, UK

Anthony Howell, D Gareth Evans & Michelle N Harvie

The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ, UK

Breakthrough Breast Cancer Research Unit, Institute of Cancer Sciences, University of Manchester, Wilmslow Road, Manchester, M20 2QJ, UK

Anthony Howell & Robert B Clarke

Centre for Public Health Nutrition Research, Division of Cancer Research, Level 7, University of Dundee, Ninewells Hospital & Medical School, Mailbox 7, George Pirie Way, Dundee, DD1 9SY, UK

Annie S Anderson

Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK

Stephen W Duffy

Manchester Centre for Genomic Medicine, The University of Manchester, Manchester Academic Health Science Centre, Central Manchester Foundation Trust, St. Mary’s Hospital, Oxford Road, Manchester, M13 9WL, UK

D Gareth Evans

Division of Genetics and Epidemiology, Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, London, UK

Montserat Garcia-Closas

Department of Cancer Studies and Molecular Medicine, University of Leicester, University Road, Leicester, LE2 7LX, UK

Andy J Gescher

Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford, OX3 7LF, UK

Timothy J Key

School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, University Drive, Norwich, NR4 7TJ, UK

John M Saxton

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anthony Howell .

Additional information

Competing interests.

The authors declare that they have no competing interests.

Authors’ contributions

AH wrote the first draft of the review, and all authors commented on and amended the draft. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2, authors’ original file for figure 3, authors’ original file for figure 4, rights and permissions.

Reprints and permissions

About this article

Cite this article.

Howell, A., Anderson, A.S., Clarke, R.B. et al. Risk determination and prevention of breast cancer. Breast Cancer Res 16 , 446 (2014). https://doi.org/10.1186/s13058-014-0446-2

Download citation

Published : 28 September 2014

DOI : https://doi.org/10.1186/s13058-014-0446-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Breast Cancer
• Breast Cancer Risk
• Mammographic Density

Breast Cancer Research

ISSN: 1465-542X

• Submission enquiries: [email protected]

Risk Stratification of Breast Cancer Patients: Integrating Epidemiology, Risk Factors, and Prognostic Markers for Sustainable Development

• Conference paper
• First Online: 19 November 2023
• Cite this conference paper

• Rajan Prasad Tripathi   ORCID: orcid.org/0000-0002-1192-4773 10 ,
• Sunil Kumar Khatri   ORCID: orcid.org/0000-0003-4373-9000 11 ,
• Darelle Van Greunen   ORCID: orcid.org/0000-0002-0761-713X 12 &
• Danish Ather   ORCID: orcid.org/0000-0003-1596-5553 10  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1939))

Included in the following conference series:

• International Conference on Sustainable Development through Machine Learning, AI and IoT

177 Accesses

1 Citations

The classification of breast cancer risk into high and low categories is essential for individualized treatment planning and enhanced patient outcomes. This research paper provides a comprehensive analysis of the classification process utilizing machine learning algorithms, with a particular emphasis on cancerous growth rate, hormone receptor status, and lymph node involvement as key factors. Classification models were created using a variety of machine learning procedures, including logistic regression, support vector machines, random forest, and k-nearest neighbours. The evaluation of performance was based on precision, recall, Accuracy and F1-score. The random forest model outperformed all other algorithms with an accuracy of 95.9%. Analysing the significance of characteristics revealed important factors that influence the classification process. The top ten characteristics, including hormone receptor status, lymph node involvement, and tumour size, exhibited strong predictive power. This study demonstrates the ability of machine learning algorithms, specifically the random forest model, to accurately classify breast cancer patients into risk categories based on cell nuclei images. The implications of these findings for personalized treatment planning and improved patient outcomes are discussed. Accurate risk classification enables healthcare professionals to tailor interventions, ensuring that high-risk patients receive the appropriate treatment and averting superfluous interventions for low-risk patients.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Breast Cancer Survival Prediction Using Machine Learning

Exploring the Machine Learning Algorithms to Find the Best Features for Predicting the Breast Cancer and Its Recurrence

Predictive Modeling of Breast Cancer Subtypes Using Machine Learning Algorithms

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 (6), 394–424 (2018). https://doi.org/10.3322/caac.21492

Article   Google Scholar  

National Cancer Institute.  Breast Cancer Risk in American Women (2019).   https://www.cancer.gov/types/breast/risk-fact-sheet

Islami, F., et al.: Breastfeeding and breast cancer risk by receptor status—a systematic review and meta-analysis. Ann. Oncol. 28 (3), 512–523 (2017)

Google Scholar  

Dua, D.,  Karra Taniskidou, E.:  UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science (2017).   http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic )

Henderson, B.E., Feigelson, H.S., Barrington, W.: Hormonal carcinogenesis. Carcinogenesis 31 (1), 27–33 (2010)

Ghosh, K., Brandt-Rauf, P.: Breast cancer and the environment: a life course perspective. Curr. Environ. Health Rep. 4 (1), 39–47 (2017)

Antoniou, A.C., et al.: The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br. J. Cancer 98 (8), 1457–1466 (2008)

Collaborative Group on Hormonal Factors in Breast Cancer: Type and timing of menopausal hormone therapy and breast cancer risk: individual participant meta-analysis of the worldwide epidemiological evidence. The Lancet 394 (10204), 1159–1168 (2019)

Boyd, N.F., et al.: Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 356 (3), 227–236 (2007)

Yager, J.D., Davidson, N.E.: Estrogen carcinogenesis in breast cancer. N. Engl. J. Med. 354 (3), 270–282 (2006)

Colditz, G.A., Rosner, B.A., Chen, W.Y.: Risk factors for breast cancer according to estrogen and progesterone receptor status. J. Natl. Cancer Inst. 96 (3), 218–228 (2004)

Terry, M.B., et al.: Lifetime alcohol intake and breast cancer risk. Ann. Epidemiol. 16 (3), 230–240 (2006)

Key, T.J., Appleby, P.N., Reeves, G.K., Roddam, A.W.: Endogenous hormones and breast cancer collaborative group: circulating sex hormones and breast cancer risk factors in postmenopausal women: reanalysis of 13 studies. Br. J. Cancer 105 (5), 709–722 (2011)

Li, C.I., Malone, K.E., Daling, J.R., Potter, J.D.: Timing of menarche and first full-term birth in relation to breast cancer risk. Am. J. Epidemiol. 158 (8), 748–754 (2003)

National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer (2021).   https://www.nccn.org/guidelines/category_1

Chen, L., Li, C.I., Tang, M.T., Porter, P., Hill, D.A., Wiggins, C.L.: Reproductive factors and risk of luminal, HER2-overexpressing, and triple-negative breast cancer among multiethnic women. Cancer Epidemiol. Biomark. Prev. 20 (10), 2470–2478 (2011)

Michels, K.B., Mohllajee, A.P., Roset-Bahmanyar, E., Beehler, G.P.: Diet and breast cancer: a review of the prospective observational studies. Cancer 109 (S7), 2712–2749 (2007)

Hartmann, L.C., et al.: Benign breast disease and the risk of breast cancer. N. Engl. J. Med. 353 (3), 229–237 (2005)

Ewertz, M., et al.: Age at first birth, parity, and risk of breast cancer: a meta-analysis of 8 studies from the Nordic countries. Int. J. Cancer 117 (3), 643–648 (2005)

Anothaisintawee, T., et al.: Risk factors of breast cancer: A systematic review and meta-analysis. Asia Pac. J. Public Health 25 (5), 368–387 (2013). https://doi.org/10.1177/1010539513488795

Phipps, A.I., et al.: Body size, physical activity, and risk of triple-negative and estrogen receptor-positive breast cancer. Cancer Epidemiol. Biomark. Prev. 20 (3), 454–463 (2011)

Li, C.I., Malone, K.E., Porter, P.L.: Relationship between long durations and different regimens of hormone therapy and risk of breast cancer. JAMA 289 (24), 3254–3263 (2003)

Xu, Y., et al.: Comprehensive analysis of necroptosis-related genes as prognostic factors, and immunological biomarkers in breast cancer. J. Pers. Med. 13 , 44 (2023). https://doi.org/10.3390/jpm13010044

Noman, & Noman. (n.d.). Educational intervention in breast cancer screening update, knowledge and beliefs among Yemeni female school teachers in the King Valley, Malaysia. http://psasir.upm.edu.my/id/eprint/97810/1/FPSK%28p%29%202021%2038%20IR.pdf

Download references

Acknowledgements

The authors would like to acknowledge the American Cancer Society, the National Cancer Institute, and the UCI Machine Learning Repository for providing valuable data and resources for this research. Their contributions to cancer research and data sharing are instrumental in advancing our understanding of breast cancer and improving patient care.

Conflict of Interest

In performing this study and writing this report, the authors have found no conflicts of interest. Scientific rigor and objectivity were adhered to throughout the research process

No government, private, or non-profit organization provided particular support for this study.

Data Availability Statement

Supporting data for this work may be found at https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 in the UCI Machine Learning Repository.

Compliance with Ethical Standards

This research paper complied with all ethical standards in conducting the study and analysing the data. The use of the Breast Cancer Wisconsin (Diagnostic) Data Set ensured the privacy and confidentiality of patient information, as the dataset has been anonymized and made publicly available for research purposes. The study adhered to ethical guidelines and regulations governing the use of human subject data and followed proper data protection and privacy protocols.

Author information

Authors and affiliations.

Department of IT and Engineering, Amity University in Tashkent, Tashkent, Uzbekistan

Rajan Prasad Tripathi & Danish Ather

Amity University Uttar Pradesh, Noida, India

Sunil Kumar Khatri

Centre for Community Technologies, Nelson Mandela University, Port Elizabeth, South Africa

Darelle Van Greunen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Rajan Prasad Tripathi .

Editor information

Editors and affiliations.

Vivekananda Institute of Professional Studies - TC, New Delhi, Delhi, India

UnifAI Technology, London, UK

Head of Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef City, Egypt

Ahmed A. Elngar

Universiti Brunei Darussalam, Gadong, Brunei Darussalam

Nagender Aneja

Parshuram Institute of Technology, New Delhi, Delhi, India

Pavika Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Tripathi, R.P., Khatri, S.K., Van Greunen, D., Ather, D. (2023). Risk Stratification of Breast Cancer Patients: Integrating Epidemiology, Risk Factors, and Prognostic Markers for Sustainable Development. In: Whig, P., Silva, N., Elngar, A.A., Aneja, N., Sharma, P. (eds) Sustainable Development through Machine Learning, AI and IoT. ICSD 2023. Communications in Computer and Information Science, vol 1939. Springer, Cham. https://doi.org/10.1007/978-3-031-47055-4_9

Download citation

DOI : https://doi.org/10.1007/978-3-031-47055-4_9

Published : 19 November 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-47054-7

Online ISBN : 978-3-031-47055-4

eBook Packages : Computer Science Computer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

National Institute of Environmental Health Sciences   —   National Institutes of Health   —   U.S. Department of Health and Human Services

The sister study.

• Home 20th Anniversary
• for researchers

Your browser does not support JavaScript! For a better website experience, please enable the JavaScript on your browser.

Health Updates are on the Way! Learn More

Dietary Patterns and Chronic Disease Risk Learn More

Prediction Model for Iron Status Learn More

Green Spaces and Recreational Activity Learn More

en español

Please complete your health update for a chance to win $500.

Sisters, starting in October 2023, we are contacting you to complete either your Annual Health Update or your more in-depth Detailed Health Update (asked every three years). Please check your emails or mail to complete your questions. To show our appreciation, when you complete the questions, you will be entered into a $500 gift card drawing. Thank you!

The Sister Study is Still Going Strong After 20 Years!

October is breast cancer awareness month and we are commemorating the 20th Anniversary of the NIEHS Sister Study —the nation's longest and largest study to find environmental and genetic causes of breast cancer. Be sure to see the new Sister Study video and other products.

A Study of the Environmental and Genetic Risk Factors for Breast Cancer.

  the sister study is on facebook.

Read archived Sister Study Facebook Posts

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• For authors
• Browse by collection
• BMJ Journals

You are here

• Volume 14, Issue 8
• Qualitative exploration of patients’ experiences with Intrabeam TARGeted Intraoperative radioTherapy (TARGIT-IORT) and External-Beam RadioTherapy Treatment (EBRT) for breast cancer
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0002-7573-6712 Sandeep Kumar Bagga 1 ,
• Natalie Swiderska 2 ,
• Charlotte Hooker 1 ,
• Jennifer Royle 3 ,
• Marie Ennis-O'Connor 4 ,
• Siobhan Freeney 5 ,
• Dympna Watson 4 ,
• Robin Woolcock 6 ,
• George Lodge 7 ,
• Siobhan Laws 7 ,
• http://orcid.org/0000-0003-1760-1278 Jayant S Vaidya 8
• 1 Research , MediPaCe , London , UK
• 2 Patient Engagement , MediPaCe , London , UK
• 3 Strategy , MediPaCe , London , UK
• 4 Independent Patient Advocate , Dublin , Ireland
• 5 Lobular (Breast Cancer) Ireland , Dublin , Ireland
• 6 Triple Negative Breast Cancer Foundation Inc , London , UK
• 7 Royal Hampshire County Hospital , Winchester , UK
• 8 Division of Surgery and Interventional Science , University College London , London , UK
• Correspondence to Dr Sandeep Kumar Bagga; sandeep{at}medipace.com

Objective To gather a deep qualitative understanding of the perceived benefits and impacts of External-Beam RadioTherapy (EBRT) and TARGeted Intraoperative radioTherapy (TARGIT-IORT) using Intrabeam to assess how the treatments affected patient/care partner experiences during their cancer treatment and beyond.

Design and participants A patient-led working group was established to guide study design and to help validate findings. Patients with experience of receiving EBRT or TARGIT-IORT were purposively sampled by Hampshire Hospitals NHS Foundation Trust. These patients had been offered both regimens as per their clinical features and eligibility. Semistructured interviews were conducted with 29 patients and care partners with lived experience of either EBRT (n=12, 5-day FAST-Forward regimen and n=3, 3-week regimen) or TARGIT-IORT (n=14). Thematic analysis was then carried out by two coders generating 11 themes related to EBRT or TARGIT-IORT.

Setting Semistructured interviews were conducted virtually via Zoom during February and March 2023.

Results A number of procedural grievances were noted among EBRT patients. EBRT was perceived as being disruptive to normal routines (work, home and travel) and caused discomfort from side effects. TARGIT-IORT was perceived by patients and care partners as the safer option and efficient with minimal if any disruptions to quality of life. The need for timely accessible information to reduce anxieties was noted in both cohorts.

Conclusions This qualitative study found that patients perceived EBRT as being greatly disruptive to their lives. In contrast, the one-off feature of TARGIT-IORT given while they are asleep during surgery gives them the feeling of stamping out the cancer without conscious awareness. These insights can help healthcare staff and policy-makers further justify the incorporation of the treatment favoured by these patient perceptions (TARGIT-IORT) more widely in routine practice. Further research is planned to explore TARGIT-IORT in more diverse populations and in the 35 countries where it is an established treatment option.

• breast cancer
• radiotherapy
• qualitative research
• breast surgery
• quality of life

Data availability statement

Data are available upon reasonable request. Raw data such as interview transcripts are not publicly available due to participant confidentiality and risk of compromising privacy but can be made available to researchers if appropriate confidentiality, ethics, regulatory and consent processes can be put in place.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-081222

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

STRENGTHS AND LIMITATIONS OF THIS STUDY

This qualitative study included the two routinely offered radiotherapy treatment options (External-Beam RadioTherapy and TARgeted Intraoperative radioTherapy using Intrabeam) allowing for assessment of patients’ perceptions and experiences in each.

Methodological strengths include measures to prevent researcher bias, such as producing reflexive accounts, independent coding, and exploring both patient and care partner perspectives .

Extensive involvement of a patient-led working group ensures the study design and delivery is robust yet sensitive and respectful.

A limitation is the lack of diversity in the study population, being predominantly white and of higher socioeconomic status from a single English location because of which we are planning to explore these concepts in more diverse populations and in the 35 other countries where TARGIT-IORT is already a well-established treatment option.

The COVID-19 pandemic during which the patients were treated may have introduced some confounding factors, but they did provide useful insights into patient isolation issues.

Introduction

Conventionally, radiotherapy treatment for breast cancer has involved patients undergoing External-Beam Radiotherapy (EBRT) several weeks or months after their surgical removal (lumpectomy). EBRT is usually delivered postoperatively to the whole breast. For external beam radiotherapy, patients are required to attend 15 treatment sessions, each lasting about 15 min, 5 days a week over 3–6 weeks. 1 , 2 In 2020, the FAST-Forward protocol, administering radiotherapy over five sessions, was adopted in some parts of UK, partly as a response to the COVID-19 pandemic and even before the results of the FAST-Forward trial were published. 3 An additional 5–8 days of tumour bed boost is given in about a quarter of cases who are found to have higher-risk disease. 3

Targeted Intraoperative Radiotherapy (TARGIT-IORT) using Intrabeam offers an alternative to women with early breast cancer that is currently being used in a small number of hospitals across England. This approach, first used in 1998, delivers a single dose of radiotherapy directly to the breast tissue surrounding the tumour immediately after the tumour has been removed and the patient is still under the same anaesthetic in the operating theatre. The long-term results of the international randomised TARGIT-A trial (n=2298) in which TARGIT-IORT was compared with EBRT found TARGIT-IORT to be effective as whole breast radiotherapy, reduced non-breast cancer deaths and improved overall survival in those with grade 1 and grade 2 cancers. 4–12

To date, several studies have investigated patients’ experiences with TARGIT-IORT quantitatively. 13–18 These studies gathered information about patients’ quality of life (QoL) during and after treatment via questionnaires and have concluded that patients receiving TARGIT-IORT report high QoL scores 13 and better emotional well-being, less pain, fewer breast and arm symptoms compared with patients receiving EBRT. 14 19 The social impact of reducing the repeated journeys to the radiotherapy centre for both the patient and their care partner has been established. 20 Patient preferences have also been explored in studies based in the USA and Australia. 21–24 However, qualitative insights can give researchers and practitioners an in-depth understanding of patient perceptions that can help explain, with confidence, the reasoning behind the difference in QoL experienced by patients having these treatments.

Much of the literature on patients’ experiences of receiving radiotherapy has focused on EBRT alone where qualitative studies have used various methods such as workshops, interviews and diary entry analysis. Recurring themes include the need for adequate information provision, healthcare professionals’ knowledge of breast or arm lymphoedema (sluggish drainage of lymph fluid), perceived lack of choice, experiences of being naked and feelings of disempowerment, 25 psychological burdens of impact (and the resources required to support patients), 26 impact of side effects such as skin toxicity on patients’ QoL, life and health after radiotherapy and feeling mystified by radiotherapy and how it works. 27 28 While there are other studies investigating breast cancer patients’ lived experiences of receiving the diagnosis, treatment perceptions, experiences of survivorship and symptoms from radiotherapy, 29–34 they do not focus on lived experiences of receiving EBRT specifically.

In addition, the literature review has highlighted that no qualitative comparison of patients’ experiences of TARGIT-IORT and EBRT has been conducted although one qualitative study, exploring overdiagnosis of breast cancer, did briefly describe the experiences of patients having TARGIT-IORT and EBRT. 35 Rich descriptions of authentic experience can help to place the treatment pathway in the context of patients’ everyday world and to truly understand the perceived barriers, benefits and personal consequences of treatment. Therefore, this study is designed to gather a deep understanding of how patients define the benefits and impacts of each therapeutic regimen and how this qualitatively affects patients’ and/or care partners’ experiences. As a secondary aim, the study will also identify where there have been unnecessary treatment-related impacts on QoL and areas of potential improvement.

Methodology

Study design.

This study used a qualitative research design with semistructured interviews as the primary research instrument. Researchers adopted a phenomenological approach which encourages a bracketing off of researchers’ own preconceptions and opinions to help mitigate bias and promotes a special importance to individual human experience where multiple realities exist (based on participants’ own subjective experiences).

At the inception of the study, a patient-led working group was established to ensure the research was designed and conducted in a respectful and sensitive manner. Figure 1 outlines the research process. The authors have used the Consolidated criteria for Reporting Qualitative research to report the study 36 (see online supplemental file 1 ).

Supplemental material

• Download figure
• Open in new tab
• Download powerpoint

Overview of research process. EBRT, External-Beam RadioTherapy; TARGIT-IORT, TARGeted intraoperative radiotherapy; REC, Research Ethics Committee.

Working group

Four patient advocates (three patients who had been treated for breast cancer and one care partner) with lived experiences of radiotherapy were invited to participate in a working group with the researchers. An initial meeting was held on 19 August 2022. In this meeting, the research design was discussed which included reviewing the study aims, the need for a comparison group, data collection method and participant recruitment channels. The second meeting, on 30 May 2023, focused on validating the emerging themes from the analysis. Between these meetings, the researchers shared early drafts of the core research material (eg, participant information sheet and consent form) to obtain members’ feedback and suggestions for amendments. The working group has also coauthored this paper.

Recruitment of participants and consent

A key outcome of the first working group meeting was to ensure the study design had a comparison group. This meant recruiting patients or care partners with lived experience of receiving EBRT to enable a comparison to those who had received TARGIT-IORT. The eligibility criteria are based on criteria used previously in TARGIT-IORT clinical trials ( table 1 ). All patients were, at the time of their cancer diagnosis, eligible for both TARGIT-IORT and EBRT.

• View inline

Eligibility criteria for interview participants

Participants were first identified by SL and GL at Hampshire Hospitals NHS Foundation Trust in accordance with the eligibility criteria, using purposive sampling (NHS stands for National Health Service, which is provided free for cost to patients in the United Kingdom). This Trust recruited patients to the randomised TARGIT-A trial between 2000 and 2012. Since the National Institute of Health and Care Excellence (NICE) recommendation to offer TARGIT-IORT to suitable patients, they have been offering the procedure to their patients. For this study, GL compiled a list of all patients who received either TARGIT-IORT or EBRT. Prospective participants were stratified first into rural and urban subgroups and then by age (50–60 and 60–70). A randomiser was then applied to these subgroups to ensure the final selection process was free from bias from clinicians who had treated patients. Cover letters and recruitment advertisements (approved by the ethics committee) were posted by GL to 58 eligible patients. Those interested in participating in the study contacted researchers voluntarily. Subsequently, the researchers shared a participant information sheet, consent form and provided further information during a short introductory call where participants also had the opportunity to ask further questions about the purpose and conduct of the research. In total 29 participants responded, and all were successfully recruited to the study. Participant characteristics can be found in table 2 .

Sample characteristics of interview participants

Semistructured interviews

Working group members agreed semistructured interviews should be used to gain rich descriptive accounts of experiences with EBRT and TARGIT-IORT. Members felt discussing sensitive and privileged information, namely people’s experiences of receiving the cancer diagnosis and treatment, would be more uncomfortable in, for example, a focus group environment. Therefore, two discussion guides (one for each type of radiotherapy) were developed and refined with the help of working group members (see online supplemental files 2 and 3 ). Interviews were conducted between 9 February 2023 and 2 March 2023 by researchers SKB, NS and JR who are experienced in using qualitative research methods as part of their professional roles. Each interview lasted approximately 60 min and was conducted virtually through Zoom and either digitally video recorded or audio recorded depending on participants’ wishes. The identity of the interviewer can positively or negatively affect the interviewer–interviewee relationship. Participants were, therefore, asked whether they would prefer an interviewer of the same sex, with the default position being that a female interviewer (NS or JR) will conduct the interview with patients and similarly, a male interviewer (SKB) for male care partners. During the interviews, informal member checking took place by interviewers routinely summarising what participants had said to check for accuracy and understanding.

Recordings were transcribed verbatim, with potentially identifying details anonymised and assigned a unique identifier.

In keeping with a phenomenological approach to analysis, researchers began by writing reflexive accounts. This involved reflecting on their own experiences, preconceptions and assumptions that have the potential to influence interpretations of participants’ accounts. This process helps to create the self-awareness required when attempting to consciously bracket out thoughts and opinions that could lead to bias.

Reflexive thematic analysis based on Braun and Clarke’s 37–39 six-step approach was used to analyse the qualitative data and to identify recurring themes related to patient and care partner experience ( figure 2 ). The process of data familiarisation took place during data collection, postinterview reflections, transcribing and re-reading the transcriptions and interview field notes. Initial transcripts were individually coded (identifying units of meaning) by two researchers (SKB and NS) who then reviewed the other’s codes. Through subsequent discussion and reflection and agreement that theoretical saturation was sufficiently achieved, codes were finalised and applied throughout the remaining transcripts. Through an iterative process, descriptive and interpretative codes were categorised to form 28 subthemes and 11 major themes. Microsoft Word and Excel were used to facilitate coding, grouping and text retrieval to identify illustrative quotes.

Reflexive thematic analysis (adapted from Braun and Clarke, 2020). 37

Patient and public involvement

Patients were involved in refining the research question, study design and outcome measures. Their contributions during these discussions were informed by their lived experiences, priorities and preferences during the first working group meeting (described above in the ‘Methodology, Working group’ section). Participant identification and recruitment channels were also discussed with the patient-led working group though they were not directly involved in recruitment into the study nor in the conduct of the study. The results of the study will be disseminated to the study participants once the peer-reviewed paper is published. The burden of the intervention was assessed by the patients themselves for this qualitative study.

The following section presents findings from the thematic analysis which looked at EBRT and TARGIT-IORT separately and the outputs from discussions of the second working group meeting ( table 3 ). Participants’ quotes have been labelled with identifiers (eg, P1, P2) not known to anyone other than the researchers (ie, not the hospital staff, participants themselves or anyone else).

Themes and subthemes arising from the interviews

Themes coming out of interviews with patients who had EBRT

Dissatisfaction with unalterable elements of ebrt procedure.

The majority of EBRT participants expressed discontent with many of the standard elements of the EBRT procedure. Some participants felt intimidated by the size of the room being ‘disturbing’ (P22) and the radiotherapy machine being ‘scary’ (P19):

…the room that you go into where the machine is, is cold…it could be a bit warmer. Now, some of that could be psychological because you're in a big white room with a big, huge machine… (P3) …the 2 nurses go into another side room, so, you feel so alone, and you know, and this machine sort of moving around you. It’s, it is quite scary to deal with. (P19)

Four participants also described the challenge associated with needing to hold one’s breath during sessions. This is done with the hope that the heart may receive less radiation by pushing the chest wall and the breast away from it. Participants described it as saying, ‘ that was the worst bit’ (P12), ‘ it’s going to be difficult’ (P15), ‘ I don’t want to be zapped on my heart’ (P21) and another felt it was ‘ really claustrophobic’ (P19) or causing ‘ panic’ (P19). The planning appointment required for EBRT was met with similar dissatisfaction. While there is a clear appreciation for healthcare staff and their workload, participants were unhappy with the dehumanising nature of these appointments:

You become another face… you do feel like a slab of meat while they're trying to get you in the right position and it’s not a pleasant experience. (P19)

These experiences resonated with working group members’ recollections: ‘silent’ and ‘cold, dark room’ and finding it difficult, a ‘physical challenge’ to maintain position after surgery. Another member felt that while healthcare staff were pleasant, the experience of receiving radiotherapy itself is ‘quite traumatic’ and emotional, ‘I remember lying there and tears came from nowhere…’.

As with study participants, working group members acknowledged that while healthcare staff themselves are not at fault, the ‘system’ causes the dehumanising elements described by participants referring to poor staffing levels and a high-pressure work environment within healthcare.

In contrast, one participant positively describes the EBRT sessions, ‘ there’s music on, and I didn’t find any cause to worry at all’ (P15). The relaxing effect of music was echoed by a working group member who also recalled how music helped in which she felt was ‘ brilliant’ and stated ‘ it helped my head’ .

Finally, one-third of participants expressed a strong objection to being tattooed which was required to ensure radiation is delivered to the right location.

What really did wind me up actually, I had to have the 3 dots tattooed on me and I didn’t want tattoos. (P3)

Participants were frustrated by the fact that it is permanent, the colour used and two participants felt it affected their confidence in wearing certain clothes, ‘ I can still see that now, if I wear a swimsuit or something’ (19 ). One working group member felt that patients are often uninformed about radiotherapy and that patients’ preferences are not listened to. She concluded that this was a good example of an area that required adequate information sharing at the right time.

Unanticipated disruptions cause helplessness

One-third of EBRT participants experienced either delays on the day of the EBRT session or extensions to their course owing to either machine failure or staff absences. The impacts on patients and care partners include stress, aggravation and disappointment with knock-on consequences proving to be burdensome:

…the machine broke down…but I couldn't find [the new hospital], and I got really tired and upset. I was trying to find where I was supposed to go, and nobody seemed to know, and I just managed to grab the team before they went home. I was like, ‘Give me my last radiotherapy now!’ It was down in some basement I mean. Location S is a maze. So, that was a bit stressy. (P1)

Patients who received their EBRT during the COVID-19 pandemic were unable to have their care partners (in most cases husbands) with them at key treatment stages. This isolation caused additional anxieties in the EBRT cohort as one care partner stated, ‘ it’s disappointing and it would have been nicer for me to be able to support her more…’ (P23). Similarly, a patient participant states:

…it’s anxiety level of just having that, that security blanket of having somebody there with you. (P19)

The working group discussed isolation and the emotional impact during EBRT sessions. Since EBRT continues for days and indeed weeks in some cases (5–6 weeks for all members), patients truly feel alone during this phase. They recalled overwhelming feelings of sadness during the sessions with thoughts such as, ‘ how did I get here’ . One member expressed empathy with study participants who would have had to endure further isolation during the COVID-19 pandemic period.

Straightforward sessions were met with surprise but travelling for EBRT was still a concern

Four out of the 15 participants interviewed from the EBRT cohort, stated that no side effects or other complications were experienced with one participant saying she felt like ‘ one of the lucky ones’ (P20). In the absence of side effects such as pain, burning or tiredness, patients experience a sense of surprise and relief after having received prior warnings from either clinical staff or hearing stories from friends and family.

…they said to put a couple of tubes of aloe vera, and keep in the fridge, and put it on religiously. But I was fine. I was fine. I literally had no burning. No rash, no nothing. I’d heard about friends having burns, but I just didn’t, I didn’t. (P12)

Similarly, three participants were appreciative of the fact that individual sessions of EBRT were in fact ‘ very straightforward’ (P1) and quick:

I did find the time, actually, went quite quick. It wasn’t very long; it might have been an hour. Yeah, it wasn’t too long. (P1)

Although these four participants did not experience radiation-related side effects, it should be noted that two of the four participants did, express frustrations about the burden of travelling during EBRT sessions and that the overall radiotherapy course was ‘ time-consuming’ (P2).

Disruption to normal life routines

A few participants were either employed or self-employed and described how EBRT impacted their own work performance (eg, tiredness, weakness in arm) with one person concluding, ‘ I’m an office worker but if I’d be doing a manual job, I think it might have impacted more.’ (P3). There is also the realisation that patients would likely need to adjust work sessions to accommodate for the EBRT sessions and the related side effects: ‘ I’ve worked out a part time basis to get back into work.’ (P19).

Participants shared concerns about the impact on work colleagues. One participant states: ‘ I’m the only person that does my job. So, I was acutely aware that when I’m not at work other people are picking up my job’ (P19). Similarly:

…we were a short-staffed team, I was aware that when I wasn’t there, it was putting work onto other people, and I felt I should have been there…. (P3)

The EBRT cohort also revealed the emotional toll of work-related worries and impacts, and the work guilt associated with the impact on colleagues. Similarly, the potential impact on the care partner’s work is also, clearly, a significant consideration for patients:

The first night after my first session I was in so much pain, I mean I didn’t sleep a wink the first night. It was absolute agony…it was my self-confidence, and everything was destroyed…and I didn’t dare, I didn’t want to wake my husband up. He had to go to work early, so then he could take me to radiotherapy in the afternoon. (P19)

These impacts on employment were corroborated during working group discussions. One member, commenting in particular on self-employed people, described the impact on financial standing and home life as ‘catastrophic’.

In contrast, those who had flexible hours of working were less affected, as one patient participant describes her care partner: ‘ luckily he was doing a job where could sort of pick and choose what he did and his hours, so it was all right.’ (P3).

The repetitive nature of the EBRT sessions, the travel involved and the side effects experienced all also impacted on participants’ normal daily routines. For instance, home activities such as shopping, gardening and caring responsibilities were impacted:

I think we might have cut down [caring for elderly parents] to once a week instead of two or three times… (P3)

One participant mentioned a close family member taking a week off from work to support looking after her, her husband, family pets and ‘ doing a few household chores like pushing the Hoover around’ (P22).

Severe pain brought on by EBRT was described as ‘ agony’ (P19) both during sessions and after sessions (P7). Ongoing pain months and years after the radiotherapy means patients need to settle into a new norm, now constantly having to be aware of their own ‘ limitations’ (P6):

I’m still aching like mad from [radiotherapy], that’s two years later and I’m still achy and in pain. (P6)

For working group members, these experiences were familiar. One member clarified that although there were no specific side effects from the radiotherapy the disruption to life was ‘ hugely problematic’ .

Many of the patients who received the FAST-Forward protocol included sessions on either side of a weekend resulting in a total of 7 days to complete the course. No participants complained specifically about the weekend being involved in this way however many described dedicated activities for the weekend, for example, food shopping, family commitments and short trips away which would undoubtedly be affected while in active treatment.

Conversations with participants revealed personalities play a role in patients’ cancer journey. One participant felt the emotional impact of radiotherapy treatment (and the subsequent withdrawal of routine contact with healthcare staff) more than all the others. The anxieties associated with this perceived void and the mental health impact itself are disruptive to re-engaging with normal life and routines:

…you’ve got people checking on you as in consultants or the breast care specialist nurses, your GP, the radiographer, people ringing you, checking on you, you’re seeing them all the time. After the radiotherapy, I’m suddenly though on my own now. I didn’t realise it was a real security blanket…it was so reassuring to seeing the consultant and seeing this nurses every day. That was the bit where I took a bit of a dive…I had two or three days where I couldn’t stop crying, I thought, ‘Oh I’m on my own now’… (P12)

There were also those who demonstrated a pragmatic mindset viewing any discomfort and impactful delays associated with EBRT sessions as realities to, in effect, take in their stride, ‘ any inconvenience, you just get on with it.’ (P20 )

Travel is clearly an uncomfortable reality and a demanding aspect of receiving EBRT. Many participants complained about the repeated journeys required for EBRT sessions. The burden has been described as ‘ dreadful’ (P7) with people feeling ‘ exhausted’ particularly where the effects of radiotherapy (tiredness and pain) are felt. The burden of travel also manifests as experiencing a longer day overall as well as the sheer cost of public transport used to make the trips independently:

It’s a pain having to go to Location S, there’s a hospital there where the bus doesn’t even go. So, from Location O, I have to get a bus from here to Location H, then wait half an hour, then from Location H to Location W, bus station, then a taxi to the hospital. That all costs 30 quid and is time-consuming, and when you’re sore, it’s not ideal. (P2)

In the majority of cases, there is a reliance on others (the husband but on occasion friends or children) to drive participants to the EBRT sessions. While there are no direct statements indicating a burden to care partners, it is important to note that in these cases both the patient and the care partner endure the repeated journeys:

My husband drove…I’m not a good driver…I certainly can’t park so it was good that he went with me because in case, if something went wrong or something because he’s like my rock he is. (P3)

The location of the EBRT sessions is critical to the quality of patient experience and when participants were presented with two hospitals to choose from it was clearly valued:

…it’s the same surgeons, all the same team, who were in Hospital W or B. I opted for Hospital B, it’s nearer to me and I could get to Hospital B easily, whereas Hospital W was an ordeal for me. (P21)

Two participants who were retired and for who the location of the hospital was particularly close indicated that travel was not a burden and considered themselves ‘ very lucky’ (P22), though of note, one of these participants experienced no EBRT side effects which may have contributed to relatively positive experience.

In contrast to many of the experiences described above, one working group member shared that she was relieved to have regained her independence since she was able to travel to her EBRT sessions herself. However, the working group notes that all their experiences included a difficult period of receiving chemotherapy first where radiotherapy was viewed as the ‘lesser of evils’.

Experience characterised by discomfort from side effects

A wide range of side effects, clearly attributed to EBRT, were reported by the vast majority of participants and this characterises an important part of the EBRT experience. These included varying intensities of tiredness; burning (from warm sensation to blistered and sore); skin-related conditions (dryness, itchiness, rash); pain; and breast size and density changes. The most common complaint was tiredness:

…there were 3 or 4 days the following week when I just had to go to bed, or just have to, you know, lie on the sofa in the afternoon, or you know I was just really bombed out, and I’m not someone who goes to bed and afternoon normally, I’m always busy, and but I had to. I was knackered. (P12)

EBRT sessions can be very uncomfortable. Severe pain, described as ‘ agony’ (P7, P19) was experienced by two participants. One individual, unable to withstand the pain and the tiredness experienced, made an independent decision to stop attending the sessions for a few days:

I was very tired…I think I might have missed a few days, because I couldn’t make it in between…I thought I’ve done so much now, I’m not going to go anymore because it was really, really hurting…I just wanted it to end and go away? And not think about it anymore basically. (P7)

Patients experienced some side effects such pain in the breast or weakness in the arm for a prolonged period—months and years after the EBRT sessions. There were also reports of new symptoms requiring follow-up that were attributed to EBRT. One participant (P8), who felt uninformed about the ‘long-term, lasting and late effects of radiotherapy’ had developed a new pain under her ribs—she reflected:

I would not have had radiotherapy, and I would not be beating myself up about having had it now had I been given the full information about the long-term effects. You know, it’s life-shortening, radiotherapy is life shortening in itself like chemotherapy. (P8)

During discussions at the second working group meeting, members were not surprised by the insights captured from study participants and felt strongly that there were ‘no positives’ from EBRT (particularly when compared with TARGIT-IORT). One member reflecting on their experience with EBRT stated they came away thinking ‘almost anything is better than this.’ (WG member)

Specific anxieties about receiving EBRT

Discussions with the EBRT cohort revealed three main anxieties associated with receiving radiotherapy. First, while there is evidence that more information early on (particularly from consultants) helps to reduce worry, stories of radiotherapy experiences from friends and family members can raise concerns and anxiety levels:

I only knew what I knew as a lay person, you know, various friends have had radiotherapy, unfortunately, people know a lot of people who have all these sort of things… I’d heard about friends having burns… (P12) …and actually, talking to another friend, she said she would do chemotherapy any day over radiotherapy because of how the radiotherapy, the pushing around and making you feel like a piece of meat, how it how it made her feel. (P19)

The second concern was the potential for radiation to cause harm: (a) so soon after surgery and (b) to healthy tissue and organs: ‘ You’ve got to heal a bit; you can’t go straight into radiotherapy because obviously you’re as raw as hell’ (P6). Two participants in particular experienced discomfort in their arms during their EBRT sessions—one participant had a number of lymph nodes removed from the armpit and one participant developed a seroma which is an abnormal accumulation of fluid following surgery. A working group member shared a similar experience where the development of seroma delayed the start of her radiotherapy course.

Another participant who was particularly concerned about unnecessary radiation exposure requested that only half the breast be irradiated because she wanted ‘ the absolute minimum’ (P8). Similarly, another care partner described his wife’s concerns:

One thing is that my wife was worried about was the radiotherapy because obviously there is this thing with radiotherapy, particularly on the breast, of potential damage to the lungs and she was very concerned about that. (P23)

Third, since many of the participants received their EBRT during the COVID-19 pandemic, a few expressed worries about potential delays either due to staff shortages, protocol-driven cancellations (ie, limiting patient numbers) or themselves contracting COVID-19 (since multiple visits are required with EBRT) and thereby being unable to attend hospital:

But COVID was going on and I remember being so scared that my appointments would be cancelled. (P12)

Targeted Intraoperative radiotherapy using Intrabeam

Perception that targit-iort is efficient and aggravation-free.

Of the 14 TARGIT-IORT participants interviewed, 11 indicated the one-off feature of the procedure was appealing. There are many references to how quickly the procedure was completed ‘ it’s lovely to get it all done and finished with on the day’ (P26). Similarly:

…having it done and dusted, and then then waving goodbye at the hospital gates, it was like why, why would I say, ‘No thank you’? (P16)

As a consequence of this efficiency, there is relief that the procedure permitted radiotherapy to be administered without any COVID-19-related delays, or exposure to the COVID-19 virus during travel or hospital during the multiple visits for EBRT, or complications for which three participants had expressed initial concerns:

I was just delighted that it was dealt with really, really quickly, because back at that time the news was full of things where, you know, because of COVID-19, you know, everything has been delayed, and people not getting cancer treatment and that was one of my, I remember having that conversation with the consultant and said, ‘Look are we going to be delayed’. (P25)

There is a similar relief detected in participants discussing the potential positive impact TARGIT-IORT can have on patient’s mental health as one care partner states:

…the alternative would have been [EBRT]… her symptoms of depression are she gets very, very tired… so intuitively our reaction to [TARGIT-IORT] was… actually quite a good idea. (P24)

Going through a cancer diagnosis and receiving treatment was clearly an emotional time. One participant was impressed with TARGIT-IORT precisely because the efficient delivery of radiotherapy facilitates her moving on quickly:

…the beauty of intraoperative radiotherapy is that I could say ‘OK, been there, done that, move on. (P9)

Convenience of performing TARGIT-IORT during surgery is valued

Most participants from the TARGIT-IORT cohort shared why they preferred to receive radiotherapy at the same time as the surgery. There is a recognition of the convenience that TARGIT-IORT brings as a result of not having to attend hospital on multiple occasions, for example, less travel and car parking and supporting independence (particularly for retired individuals):

It’s my choice to have [TARGIT-IORT] because I thought that it was a better option for me particularly because I live on my own and it would allow me to be more independent. (P18)

While the majority of participants were retired, those who did have young children felt TARGIT-IORT supports their caring responsibilities: ‘ I’ve got a [child] and I’ve got to look after him… This is a better way to go…’ (P14). Additionally, one retired participant who valued the independence TARGIT-IORT facilitated concluded it would also suit younger, busy, women well:

…particularly for younger women this would be an extremely good thing, if they're working, it allows them to get back to work without that constant interruption and if they've got a young family. (P18)

Many participants were able to draw from stories and experiences they had heard from friends and families. The apparent inconvenience and impact of daily radiotherapy doses discouraged patients from EBRT when TARGIT-IORT was presented as an option. One participant whose father received daily doses for prostate cancer felt she would ‘ rather get it all over in one go’ (P10). Similarly:

[TARGIT-IORT] was perfect, because it just meant I didn't have to queue up in the car park with the other poor people having radiotherapy, and I did have friends who had serious cancers who were having radiotherapy at the time, and it was just miserable. (P9)

There is also a perception that with TARGIT-IORT recovery times are likely to be faster since it would signify the end of their cancer treatment: ‘ I’m going to get [TARGIT-IORT] and it’s done’ (P16) and ‘ I can just then get on and recover’ (P4). Another participant summarises her main reasons for opting to receive TARGIT-IORT:

So, there were probably 3 reasons I went for [TARGIT-IORT]. You know, COVID, convenience, and the fact that I thought, you know, ultimately, I’d probably recover quicker. (P9)

Only one participant from the TARGIT-IORT cohort, a care partner, described a significant logistical impact due to his wife’s cancer treatment in general:

…created quite a challenge really for me, I mean, I was never going to moan about it, I wasn’t the one who just had cancer surgery! But you know, it meant the days suddenly got very challenging… (P25)

Perception TARGIT-IORT is a safer alternative to standard practice

Five participants felt that they did not experience any complications as a result of TARGIT-IORT and were able to resume their normal activities quickly. While there are a few cases of soreness and itchiness that participants specifically attributed to TARGIT-IORT, most participants did not report the range of side effects seen in the EBRT cohort. As a result, participants gave their endorsements for TARGIT-IORT, respectively:

I moved around, I got up, got changed, got dressed. It was surprising actually, this is why I’ve decided to do this, if this is what it gives you then everyone should have it. You know you don't need to feel debilitated, and you can carry on with your life. I've got a [child], and I've got to look after him. So, if you can, why not. This is a better way to go if the prognosis allows it. (P14) There were no, no after-effects, no problems. It all healed up very well, because it was quite a small incision anyway and very, very successful. (P28)

The majority of participants felt the procedure prevented healthy tissue and organs from being unnecessarily exposed to radiation because ‘ the radiotherapy is directed immediately where the lump [is]’ (P17).

I confess I heard that and thought ‘God, that’s a bloody good idea, why don’t they do that more often?’. Because obviously if you don’t have to beam through loads of flesh and muscle to get at what you're aiming for then that’s got to be better to be honest. (P24)

A few participants described side effects (soreness, tiredness), precautions (new bra needed, seatbelt cushion) and restrictions (no pressure, sport, lifting), however, they were unable to clearly attribute whether these were related to the surgery or the TARGIT-IORT procedure since both occur at the same time.

…yeah, my arm was a little bit sore…I’m sure it must have been the radiotherapy or the operation, I don’t know. (P29) …a special seat belt cushion that protects your breast from the seat belt and I had one another cushion under my breast supporting it… (P11)

Novel nature of TARGIT-IORT impresses while prompting early caution

Although it has been in use for the last 25 years since the first case was done in 1998 TARGIT-IORT is seen as novel and innovative with advantages acknowledged over EBRT. The decision to proceed with TARGIT-IORT is widely considered ‘ easy’ (P28) or ‘ intuitive’ (P24) or a ‘ no brainer’ (P4):

…well, you’re in there, so you might as well get on and do it and that would surely save the need for me having to come back, I can then just get on and recover basically…it was a no brainer for me, an absolute no brainer. (P4)

However, a few participants described their initial concerns since TARGIT-IORT was introduced by the consultants as a clinical trial and was largely unheard or ‘ unknown’ (P9). Care partners, often husbands and sometimes participants’ children wanted to carry out their own research to help making an informed decision about TARGIT-IORT. One participant had already felt she was convinced by the consultant’s explanation and the advantages over EBRT, however, her daughter, who worked in healthcare, stated ‘ …‘hold on a minute, we need to look at the statistics and the recovery times, side effects’…’ (P10). Similarly, another participant’s husband wanted an opportunity to ask the consultant more questions to help feel more reassured:

…but [care partner] just wanted to have the conversation around the intraoperative radiotherapy because it was an unknown really. (P9)

It should be noted that many of the participants were either themselves or their close family (eg, husband) highly educated, often with a science-based background and were able to explore clinical study papers and statistics: ‘ I’ve got a little statistical training…so I looked at the stats and what the mean variation was…what the levels of certitude at either end of the scale were…’ (P24).

TARGIT-IORT patients have high information needs

As mentioned above, due to the relative novelty of TARGIT-IORT and in the absence of experiences of TARGIT-IORT among participants’ friends and family, reliable information from trustworthy sources is critical. The majority of participants (in both EBRT and TARGIT-IORT cohorts) displayed high levels of trust in their consultant. Receiving adequate information from them about TARGIT-IORT, particularly due to its initial availability via a clinical trial, was appreciated:

I think what was good was the way that it was explained in the first place and what the pros and cons were, or if in fact, there weren't any cons really at all…So, you know, we were told that the treatment, doing it during the operation, is just as effective but it would mean that you would have no subsequent radiotherapy and, you know, of course I’m young and foolish, I assume that to be true, we trust the doctor… (P25) [The consultant] said’ ‘This is this, that is that…pluses and minus’…gone through pros and cons and I had made up my mind that that was a good way to go. (P14)

Working group members could relate closely to this subtheme of trust. They explained that the retrospective perception of TARGIT-IORT was always likely to be a ‘no brainer’, however, for a patient going through the highly emotionally charged process of receiving their diagnosis and treatment, at a time when they are already overwhelmed with new information, the relationship with the doctor is important:

…if it’s being offered to you, it’s important how it’s being offered to you. We put out trust in, so much, our doctors. (WG member)

Two participants described receiving explanations from radiation oncologists during their presurgery appointment, however, these discussions were not influential in helping to decide which type of radiotherapy they would receive. A few participants were wary of using the Internet to search for information related to their treatment options: ‘ I’m very cautious of what information I take in from Google’ (P4). However, the majority did conduct their own Internet searches to bolster their understanding of TARGIT-IORT:

I then went away and looked the bugger up, and then you could learn for yourself a little bit, reading between the technical stuff, what it’s all about, the success rate is there or there about the same, it’s not wonderful but for me, it was a no brainer. (P16)

The provision of information was discussed on a number of occasions by working group members. Simple and clear language is particularly important at a time when patients are already in a vulnerable, stressed and emotional state:

…you are so blindsided…the normal way you operate doesn’t necessarily apply. (WG member)

Working group members pointed to the need for information sources to be created adequately in the first place, for example, being written by patients/care partners who possess the lived experiences and so are able to elaborate on the areas that matter.

…there should never be a need for a patient to go home and want to Google, you should go home with the information in hand or go home with reputable evidence-based sources of information. (WG member)

The primary finding of this study is that the subjective experiences of patients and care partners receiving EBRT or TARGIT-IORT differ significantly. Strong recurring themes of appreciation and recognition of innovation, convenience, absence of side effects and lack of disruption to life have emerged from the TARGIT-IORT cohort while in the EBRT cohort, we have largely heard about discomfort and disruption to life. These themes—centring around (a) treatment procedure itself; (b) impact on QoL and (c) information needs—were presented to and were validated by a patient-led working group.

Patients and care partners involved in this study described numerous challenges, concerns and dissatisfaction with elements of the EBRT procedure while processing a difficult and emotional diagnosis. These findings are consistent with the existing literature on EBRT experience. 25 Probst et al 25 also identified procedural grievances, for instance, patients described the radiotherapy sessions as ‘dehumanising’, ‘emotionally draining’ and complained about the tattoos being a permanent reminder of the cancer. Previous studies exploring patient-perceived barriers to radiotherapy include patients’ fear surrounding radiation toxicity which can result in non-compliance and insufficient treatment. 13 40 In fact, research has identified that fears and anxiety regarding the EBRT experience can influence a patient’s decision to opt for a mastectomy over EBRT, despite the latter having equivalent if not non-inferior survival rates. 30 Several studies have demonstrated that as the distance from radiotherapy centre increases, the rate of mastectomy also increases. 41–45 Indeed, this was the primary patient-centric reason that the TARGIT-IORT procedure was originally conceived. 10–12 46

Our study demonstrates the need for improvements in the way EBRT is delivered and has implications for practice that extend to cases where patients are not eligible for TARGIT-IORT. In stark contrast, those receiving TARGIT-IORT have no awareness or recollection of the procedure since radiation is administered during surgery. Patients and care partners found this feature particularly appealing which contributed to their decision to opt for TARGIT-IORT. Indeed, TARGIT-IORT has been widely adopted elsewhere and treated 45 000 patients across 38 countries. 6

A high proportion of our EBRT cohort (12/15) received the FAST-Forward regimen. This regimen of highly compressed higher-dose-per-fraction radiotherapy was adopted in the UK even before the results of the FAST-Forward trial were published with the aim of reducing waiting time pressures during the early part of the COVID-19 pandemic. We recognise this 5-day regimen is indeed not adopted elsewhere in the world—it has much higher toxicity—19 times higher fibrosis and a quarter of women reporting hardened breasts 3 and this toxicity is seen even with the short follow-up of the FAST-Forward trial. 47 48 It is noteworthy that a large part of the patient’s perceived benefit came from the immediacy of TARGIT-IORT due to its administration during the same anaesthetic as their lumpectomy, the resulting convenience and the absence of additional hospital visits for radiotherapy that would be otherwise required for EBRT. In our study, this benefit of TARGIT-IORT was perceived by patients even though the majority of the comparator group received EBRT over just 5 days rather than the international standard of 3 weeks. It is, therefore, likely that the contrasting experience of patients may have even higher significance and the perceived benefit may be greater when TARGIT-IORT is compared with 3 weeks of EBRT.

Patients in the study who received TARGIT-IORT had been given the option to have it because they fulfilled the eligibility criteria ( table 1 ). Since patients made a conscious choice, it is plausible that the results of this study could be biased favouring TARGIT-IORT. However, the authors of this study submit that patients should be given a choice. Our study shows that those who choose TARGIT-IORT have a positive perception of treatment and the overall experience is better than those who opted for EBRT. Others have shown that if given a choice between no radiotherapy, mastectomy, EBRT and TARGIT-IORT, 75% of patients preferentially choose TARGIT-IORT. 24

It is evident that QoL-related benefits and impacts are a central component of radiotherapy lived experiences. Compared with TARGIT-IORT, EBRT has a prolonged impact on patients and perhaps a compounded impact on QoL where patients live alone (lack emotional or practical support), do not drive (reliance on others or public transport with additional costs and travel time) or have caring responsibilities (partners, parents, children and pets). Travel and mobility issues have been recognised as barriers already 49 as has the inconvenience of a prolonged treatment plan which can affect those living in remote areas even more. 13 Our findings demonstrate the advantages TARGIT-IORT offers to those who are eligible. All participants in our study acknowledged the efficiency of the procedure with many drawn to the option (over EBRT) because it was considered ‘straightforward’ and ‘over-and-done’ during surgery. The benefits of TARGIT-IORT to patients in terms of cost, travel time and distance have been demonstrated, in principle, elsewhere. 49 50 Furthermore, the environmental and social impact of the substantially more travel required for EBRT, and a huge reduction in carbon footprint from cancer treatment by use of TARGIT-IORT has also been well documented. 20

In our study, inconveniences and logistical complications were exacerbated by EBRT side effects which were recognised as a key characteristic of the EBRT patient experience and have implications on QoL. Stanton et al 51 investigated factors affecting QoL during and after radiotherapy and found that functional impacts of treatment, particularly breast-specific pain (eg, mobility) are important correlates of QoL. In addition, Schnur et al 27 showed that key patient concerns include the timing of side effects and the impact of side effects on self-esteem affecting patients’ perception of being attractive, good workers, patients and parents. Another study supports these findings and also shares one case of such extreme physical discomfort (pain, burning, etc) that the patient admitted she had considered ending treatment and another saying she would never choose to have radiotherapy again due to the burning sensation. 28 Our study has captured similar cases. This study also underscores the emotional toll, anxieties and stresses that disruptions to life (eg, work-related) cause and have been heard at a NICE Committee meeting. 52 There were fewer reports of side effects directly attributed to TARGIT-IORT in our findings. This is consistent with a study comparing TARGIT-IORT with EBRT (quantitatively) in which patients receiving TARGIT-IORT also reported less pain, fewer breast, and arm symptoms, and better everyday functioning when compared with patients receiving EBRT. 14 We recognise that our study has found stark differences in patient experience and perception between TARGIT-IORT and EBRT. This can seem obvious because patients with TARGIT-IORT have almost no poor experience in relation to actually receiving the treatment (mainly because they are under general anaesthetic when it is given). Our findings resonate with others who also report the negative patient experiences with EBRT and have suggested interventions to improve them. 25 The important qualitative patient benefits identified in this study are of course in addition to the quantitatively proven significant reduction in non-breast cancer deaths, and an improved overall survival in patients with grade 1 and grade 2 cancers within the randomised TARGIT-A trial. 7 However, our study does detect apparent patient obscurity between surgery-linked or TARGIT-IORT-linked side effects—this clearly needs to be addressed through appropriate education and adequate information provision.

Results from our study cohorts point to the need for improvements in communication and information provision. The role of high-quality communication by healthcare staff and access to emotional support services, particularly when radiotherapy treatment ends has been highlighted already. 26 Previous research has also identified that patients can often feel mystified by radiotherapy (EBRT), how it works and will have anxieties about life and health after radiotherapy 27 or feel disempowered and lacking the ability to make an informed choice. 25 In our study, working group members emphasised the importance of trust in connection with information provision, particularly during an emotional cancer diagnosis. Members felt a number of study findings could be addressed adequately by effectively communicating the right information at the right time. Examples include letting participants know clearly that tattoos will be permanent; what the immediate and long-term side effects of both radiotherapy types are; understanding the side effects of surgery thereby avoiding confusion with TARGIT-IORT; ensuring TARGIT-IORT explanations are always supplemented with lay language overviews of the efficacy and safety profile compared with EBRT. One study showed that more than 90% of patients felt that if they were more informed about radiotherapy, they would be less scared about it. 30 The working group advocated for any shared information, such as leaflets, to be written by patients, that is, those who have experiences of receiving radiotherapy, and therefore, have an awareness of where there are likely to be challenges in understanding treatments and their impacts clearly. Similarly, considerations also ought to be given to ensuring people with learning disabilities and communication difficulties are able to make an informed choice by developing accessible information. To satisfy ‘valid consent’, doctors in the UK are now obliged to follow the new GMC guidelines underlining the essential nature of adequate patient information, 53 about all proven treatment options, even if they are not available at their own centre. In the UK, this powerful principle is now fully enshrined in law (Montgomery vs Lancashire Health Board, 2015). 54 55 The substantially better patient perception and experience documented in this study need to be included during consultations with patients when discussing treatment options before they have their surgery for breast cancer.

This qualitative study, co-led by patients, uncovered detailed lived experiences of receiving either EBRT or TARGIT-IORT from patients treated for early breast cancer, as well as those of their care partners. The research demonstrated a patient-perceived superiority of TARGIT-IORT over EBRT—it is considered more efficient with less disruption to life routines. The paper also illustrates the importance of provision of accessible information about all radiotherapy treatment options from trusted sources, at the right time (before breast cancer surgery), to reduce initial anxieties and help patients make informed choices. These new insights need to be taken together with the established quantitative survival and QoL benefits of TARGIT-IORT over EBRT. We believe that these deep insights into the patient’s perspective will substantially improve our understanding of the lived experiences of patients with breast cancer and will help clinicians, patients and policy-makers to comprehensively consider how access to better treatments can improve patients’ lives.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Health and Social Care Research Ethics Committee B (HSC REC B), Office for Research Ethics Committees Northern Ireland (ORECNI). IRAS Project ID number: 320976. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors thank the patients and care partners who volunteered to help in development of the research question, helping design the study and outcome measures. The authors thank patients and care partners (the study participants) who volunteered to participate in this study. We are grateful to them for giving up their time and sharing their treatment experiences and valuable insights during a difficult period in their lives for the benefit of science and clinical research.

• Bentzen SM ,
• Agrawal RK ,
• Aird EGA , et al
• Correa C , et al
• Murray Brunt A ,
• Haviland JS ,
• Wheatley DA , et al
• Vaidya JS ,
• Bulsara M ,
• Wenz F , et al
• Vaidya UJ ,
• Baum M , et al
• Bulsara M , et al
• Joseph DJ ,
• Tobias JS , et al
• Wang JS , et al
• Sperk E , et al
• Saunders CM , et al
• Keshtgar MRS ,
• Williams NR ,
• Andersen KG ,
• Gärtner R ,
• Kroman N , et al
• Hofmann F ,
• Blank E , et al
• Coombs NJ ,
• Coombs JM ,
• Vaidya UJ , et al
• Alvarado MD ,
• Conolly J ,
• Park C , et al
• Saunders C , et al
• Saunders CM ,
• Bulsara MK , et al
• Beattie G , et al
• Rosbottom K ,
• Crank H , et al
• Llewellyn A ,
• Schnur JB ,
• Ouellette SC ,
• Bovbjerg DH , et al
• Dilorenzo TA , et al
• Williams F ,
• Jeanetta SC
• Shaverdian N ,
• Kishan AU ,
• Veruttipong D , et al
• Murchison SC ,
• DeVries KJ ,
• Hickok JT ,
• Morrow GR ,
• Roscoe JA , et al
• Hopwood P ,
• Mills J , et al
• Pickles K ,
• Nickel B , et al
• Sainsbury P ,
• Guidolin K ,
• Brackstone M
• Nattinger AB
• Nattinger AB ,
• Kneusel RT ,
• Hoffmann RG , et al
• Chandwani S ,
• Haffty BG , et al
• Adams-Cameron M ,
• Hunt WC , et al
• Celaya MO ,
• Gibson JJ , et al
• Chinoy RF , et al
• Vogt K , et al
• Bargallo-Rocha JE ,
• Soto-Perez-de-Celis E ,
• Picó-Guzmán FJ , et al
• Stanton AL ,
• Krishnan L ,
• (NICE) NIfHaCE
• General Medical Council U
• Mansell J ,
• ↵ Montgomery v Lanarkshire health board , 2015 . Available : https://www.supremecourt.uk/cases/docs/uksc-2013-0136-judgment.pdf

X @jsvaidya

Contributors SKB, NS, CH, ME-O'C, SF, DW and RW were responsible for the study concept and design. SL and GL identified patients who met the eligibility criteria. SL, GL and JSV contributed to the study design. GL posted cover letters and recruitment adverts to all identified patients. SKB, NS and JR collected the data. SKB and NS analysed and conducted the thematic analysis from the data. ME-O'C, SF, DW and RW approved the initial report. SKB and CH wrote the first draft of this manuscript. JSV and all other authors were involved in interpreting the data and made substantial contributions to the intellectual content of the manuscript and approved the final version. The authors took full responsibility for the manuscript. SKB is responsible for the overall content (as guarantor).

Funding The study was sponsored by MediPaCe. Unrestricted funding was provided to MediPaCe by Carl Zeiss Medtech AG. The manufacturers of the Intrabeam device (Carl Zeiss Medtech AG) did not have any part in concept, design, or management of the study, or in data analysis, data interpretation, or writing of the report. A grant/award number was not issued for the funder.

Competing interests This qualitative study was initiated by MediPaCe, a patient engagement and patient research company. The manufacturers of the TARGIT-IORT device (Carl Zeiss Medtech AG) did not have any part in concept, design, or management of the study, or in data analysis, data interpretation, or writing of the report. Authors SKB, NS, CH and JR are employed at MediPaCe. MediPaCe received payment to independently plan, coordinate and conduct this study. JSV declares Support from University College London Hospitals (UCLH)/ UCL Comprehensive Biomedical Research Centre, UCLH Charities, HTA, NIHR, National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme, Department of Health and Social Care, UK Ninewells Cancer Campaign and Cancer Research Campaign (now Cancer Research UK); Research grant from Photoelectron Corp (1996–1999) and for supporting data management at the University of Dundee (Dundee, UK, 2004–2008) and travel reimbursements and honorariums from Carl Zeiss. SL and GL declare no conflicts of interest.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Read the full text or download the PDF:

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 22 August 2024

EZH2 represses mesenchymal genes and upholds the epithelial state of breast carcinoma cells

• Amador Gallardo 1 , 2 , 3 ,
• Lourdes López-Onieva 1 , 3 , 4 ,
• Efres Belmonte-Reche   ORCID: orcid.org/0000-0002-9327-6182 1 , 2 , 3 ,
• Iván Fernández-Rengel 1 , 2 , 3 ,
• Andrea Serrano-Prados 1 , 2 , 3 ,
• Aldara Molina 1 , 2 , 3 ,
• Antonio Sánchez-Pozo 1 , 2 , 3 &
• David Landeira   ORCID: orcid.org/0000-0002-3267-454X 1 , 2 , 3  

Cell Death & Disease volume  15 , Article number:  609 ( 2024 ) Cite this article

134 Accesses

Metrics details

• Breast cancer

Emerging studies support that the polycomb repressive complex 2 (PRC2) regulates phenotypic changes of carcinoma cells by modulating their shifts among metastable states within the epithelial and mesenchymal spectrum. This new role of PRC2 in cancer has been recently proposed to stem from the ability of its catalytic subunit EZH2 to bind and modulate the transcription of mesenchymal genes during epithelial-mesenchymal transition (EMT) in lung cancer cells. Here, we asked whether this mechanism is conserved in other types of carcinomas. By combining TGF-β-mediated reversible induction of epithelial to mesenchymal transition and inhibition of EZH2 methyltransferase activity, we demonstrate that EZH2 represses a large set of mesenchymal genes and favours the residence of breast cancer cells towards the more epithelial spectrum during EMT. In agreement, analysis of human patient samples supports that EZH2 is required to efficiently repress mesenchymal genes in breast cancer tumours. Our results indicate that PRC2 operates through similar mechanisms in breast and lung cancer cells. We propose that PRC2-mediated direct transcriptional modulation of the mesenchymal gene expression programme is a conserved molecular mechanism underlying cell dissemination across human carcinomas.

Similar content being viewed by others

EZH2 endorses cell plasticity to non-small cell lung cancer cells facilitating mesenchymal to epithelial transition and tumour colonization

A TGF-β-MTA1-SOX4-EZH2 signaling axis drives epithelial–mesenchymal transition in tumor metastasis

PHF13 epigenetically activates TGFβ driven epithelial to mesenchymal transition

Introduction.

Polycomb group (PcG) proteins are hallmark epigenetic regulators of embryo development, stem cell differentiation and cancer [ 1 , 2 , 3 ]. PcG proteins associate to form multimeric complexes termed polycomb repressive complexes 1 and 2 (PRC1 and PRC2) that can post-translationally modify histone tails and repress gene transcription. RING1A/B is the catalytic subunit of PRC1 and monoubiquitinates lysine 119 on histone H2A (H2AK119ub). Likewise, EZH1/2 harbours the catalytic activity of PRC2 and trimethylates lysine 27 on histone H3 (H3K27me3). The coordinated activity of PRCs leads to the formation of chromatin domains enriched in H2AK119ub and H3K27me3 that facilitate transcriptional repression of hundreds of genes across the genome in a cell-type-specific manner. In stem cells, the activity of PRCs leads to the transcriptional repression of hundreds of lineage-inappropriate genes, contributing to maintaining a specific gene expression programme and defining stem cell identity [ 1 , 2 , 3 ].

In the context of cancer, initial studies revealed that PcG proteins act as oncogenes through the transcriptional repression of the INK4A/ARF ( CDKN2A ) tumour suppressor locus [ 4 , 5 ]. However, subsequent studies confirmed that this was just one aspect of the role of PRC in cancer, because different PRC subunits can have senescence-independent prooncogenic activity or tumour suppressor function [ 2 , 6 ]. In fitting with its role as a regulator of cell identity in stem cell biology, recent studies support that PRC2 can regulate dynamic phenotypic changes of cancer cells by modulating their transition between metastable states within the epithelial and mesenchymal spectrum through poorly understood mechanisms [ 6 , 7 , 8 , 9 ]. Reversible transition between the epithelial and mesenchymal states vertebrate carcinoma cell dissemination [ 10 ], and inhibitors of the PRC2 catalytic subunits EZH2 are currently being developed to treat several types of carcinomas as main or adjuvant therapy [ 11 , 12 ]. Therefore, understanding the molecular basis of the function of PRC2 during EMT is crucial for a precise comprehension of cancer dissemination, and for successful application of PRC2 inhibitors in the clinics.

In breast cancer, comprehensive evidence indicates that EZH2 facilitates metastasis [ 13 , 14 , 15 , 16 ], but the underlying molecular mechanism is unclear. While initial reports suggested that EZH2 impacts metastasis progression through non-canonical pathways independent of EZH2 methyltransferase activity (i.e. p38 signalling and integrin B1-FAK) [ 13 , 14 ], later studies supported that the role of EZH2 in breast cancer is based on the H3K27me3-mediated transcriptional repression of key candidate genes (ie. FOXC1 or GATA3) [ 15 , 16 ]. Notably, a recent study hints at the interesting possibility that PRC2 regulates breast cancer metastasis by modulating the EMT process [ 9 ]. This finding is in consonance with evidence obtained in lung carcinoma cells, where the loss of function of EZH2 leads to the acquisition of mesenchymal features and changes in tumour colonisation capacity [ 6 , 7 , 8 , 9 , 17 ]. Importantly, in lung carcinoma cells, the regulation of EMT by EZH2 is mediated by the ability of EZH2 to directly bind the gene promoter regions and co-ordinately modulate the transcription of many mesenchymal-associated genes through H3K27me3 [ 8 ]. Here, we asked whether direct transcriptional regulation of mesenchymal genes by EZH2 also occurs in breast carcinoma cells. We found that EZH2 represses a large set of mesenchymal genes and promotes the residence of breast cancer cells in a more epithelial state. We propose that direct transcriptional modulation of the mesenchymal gene expression programme by PRC2 is a conserved molecular mechanism across different types of carcinomas that contributes to equipping cells with the plasticity required for efficient cell dissemination.

EZH2 represses mesenchymal genes in breast carcinoma cells

To analyse the function of EZH2 in breast cancer, we focused on the human MCF-7 cell line because it is a well-established system to study the molecular basis of metastasis in breast adenocarcinomas. These cells are homozygous null mutants for the CDKN2A locus, which makes them a good model to study the CDKN2A -independent function of EZH2 in cancer. We first set to identify what genes are directly regulated by EZH2 in MCF-7 cells. EZH2 catalyses H3K27me3 at the promoter regions of target genes, and thus, the genome-wide distribution of H3K27me3 is a surrogate measure of EZH2 binding [ 1 , 18 ]. Analysis of H3K27me3 enrichment maps revealed that EZH2 target the promoter region of 1954 genes in MCF-7 cells (Fig. 1A , Table S1 ). In pluripotent cells, many H3K27me3-repressed target genes display chromatin features of active transcription, such as trimethylation of H3K4 (H3K4me3) and they are usually referred to as bivalent genes [ 19 ]. Bivalent chromatin seems to facilitate their transcriptional activation during lineage transition [ 19 ]. Interestingly, a comparison of H3K27me3 and H3K4me3 genome-wide binding maps in MCF-7 cells revealed that 1141 genes (58.4%) out of the 1954 genes marked by H3K27me3 displayed a bivalent state, and hence they accumulated both modifications at their promoter region (Fig. 1A, B , Table S1 ). Importantly, bivalent genes were enriched for genes involved in the regulation of mesenchymal features, EMT and cell migration and included genes that are widely used as mesenchymal markers such as N-CADHERIN and SNAI2 (Figs. 1C, D and S1A ). Thus, we concluded that in MCF-7 cells, EZH2 binds a large set of mesenchymal genes that display features of bivalent chromatin. We hypothesised that this bivalent state might facilitate the coordinated transcriptional activation of mesenchymal genes in response to signalling molecules during EMT.

A Venn diagram comparing sets of gene promoters displaying enrichment of H3K27me3 or H3K4me3 at their promoter regions by ChIP-seq in MCF-7 cells. B Plots showing the ChIP-seq average binding profile of H3K27me3 and H3K4me3 around the TSS of gene promoters identified in ( A ) as bivalent (H3K27me3 + H3K4me3) or H3K4m3-only. C Gene Ontology analysis of bivalent genes identified in ( A ). D Genome browser view of H3K4me3 and H3K27me3 binding profiles at the SNAI2 locus in MCF-7 cells. E Schematic diagram of the treatment of MCF-7 cells with EZH2i (upper panel). The lower panel shows western blot analysis of whole-cell extracts comparing the levels of EZH2 and H3K27me3 during the experiment. ACTIN B provides a loading control. F Heatmap analysis of mRNA expression of 667 differentially expressed genes upon 12 days of EZH2i treatment (FC > 4, p  < 0.05) by RNA-seq of two independent replicates (R1 and R2) in MCF-7 cells. G GSEA of EMT associated genes in MCF-7 cells treated compared to untreated with EZH2i for 12 days. Normalised Enrichment Score (NES) and false discovery rate (FDR) are indicated. H Plots showing H3K27me3 and H3K4me3 enrichment measured by ChIP-seq around the TSS of the promoters of 573 genes identified in cluster II in Fig. 1F . I Brightfield images and quantification of cultured wound healing assays using MCF-7 cells that have been treated or not with EZH2i for four days. The mean and SEM of 3 experiments are shown. Asterisks indicate statistical significance using a Mann–Whitney test (* p  < 0.05). J GSEA of EMT associated genes in MCF-7, MDA-MB-231 and SKBR3 cells plated at low density and treated with EZH2i for 14 days, compared to untreated controls. Normalised enrichment score (NES) and statistically significant false discovery rate (FDR < 0.25) are indicated.

To address whether EZH2 represses the mesenchymal gene expression programme through H3K27me3 in breast cancer cells we treated MCF-7 cells with a highly specific small molecule that inhibits EZH2 methyltransferase activity (GSK126, thereafter referred to as EZH2i) [ 20 ] and analyse whether the loss of H3K27me3 induced the activation of the mesenchymal gene expression programme. Inhibition of EZH2 led to a drastic reduction of global levels of H3K27me3 without sensibly affecting EZH2 protein stability (Fig. 1E ) and produced only mild inhibition of cell proliferation that did not impair long-term culture of MCF-7 cells (Fig. S1B ). Importantly, reduced levels of H3K27me3 during 12 days of culture led to a robust transcriptional activation of the mesenchymal marker N-CADHERIN (Fig. S1C ), which is a H3K27me3-positive direct target of EZH2 (Fig. S1A ). Likewise, analysis of the expression of master EMT transcription factors (EMT-TFs) revealed an evident specific activation of the SNAI2 gene (Figs. S1D, E and S1F ), which is also enriched for H3K27me3 and H3K4me3 at its promoter region (Fig. 1D ). As expected, activation of mesenchymal genes upon EZH2 inhibition was associated to the downregulation of the epithelial marker E-CADHERIN (Fig. S1G, H ). Importantly, transcriptome profiling analysis by mRNA sequencing (mRNA-seq) demonstrated that treatment of MCF-7 cells with EZH2i induced a global reorganisation of the transcriptional programme that involved the upregulation of 573 genes enriched in EMT pathways (cluster II in Fig. 1F ) and mesenchymal markers (Fig. 1G ). As expected, many of these genes displayed H3K27me3 and H3K4me3 at their promoter regions (Fig. 1H ). In addition, we noticed that genes involved in oestrogen response were downregulated upon EZH2i treatment (Fig. 1F , cluster I), suggesting that activation of EMT induces the inactivation of the oestrogen pathway signalling in MCF-7 cells. Importantly, activation of mesenchymal genes in MCF-7 cells upon depletion of H3K27me3 was functionally relevant because cells treated with EZH2i displayed obvious increased mobility compared to untreated cells in cultured wound healing assay (Fig. 1I ). Thus, we concluded that EZH2 targets and represses the transcription of a large set of bivalent mesenchymal genes in the breast cancer cell line MCF-7.

To confirm that mesenchymal genes are transcriptionally induced upon EZH2i treatment we exposed MCF-7 cells to another highly specific inhibitor of EZH2 that is approved for cancer treatment by the US Food and Drug Administration (EPZ-6438, tazemetostat) [ 21 ], and measure its impact on gene expression by mRNA-seq. In keeping with our findings using EZH2i, treatment with EPZ-6438 inhibited H3K27me3 deposition (Fig. S2A ), induced the transcriptional activation of EZH2i-responsive genes (Fig. S2B ) and activated the mesenchymal gene expression programme (Fig. S2C ). To discard that the effects observed in EZH2i-treated cells were due to off-target effects of the EZH2 inhibitors assayed we measured the expression of mesenchymal genes upon shRNA-mediated knockdown of EZH2 protein in MCF-7 cells (shEZH2 cells). As expected, shEZH2 cells showed reduced global H3K27me3 (Fig. S2D, E ), transcriptional induction of mesenchymal genes (Fig. S2F ) and increased protein levels of the mesenchymal TF SNAI2 (Fig. S2G, H ).

To confirm that EZH2 functions as a gatekeeper of epithelial identity in breast carcinoma cells we analysed whether inhibition of EZH2 led to the transcriptional activation of mesenchymal genes in cell lines derived from different types of metastatic breast adenocarcinomas: MCF-7 cells (ER+/PR±/Her2−), SKBR3 cells (ER−, PR−, Her2+) and MDA-MB-231 cells (ER−/PR−/Her2−). Cells were treated with EZH2i for 4 days and then plated at low density to form colonies for fourteen extra days in the presence of EZH2i. Inhibition of EZH2 induced minor effects on cell growth (Fig. S2I ), confirming the suitability of these cell lines to study the proliferation-independent role of EZH2. Notably, inhibition of EZH2 activity induced the expression of mesenchymal genes (Figs. 1J and S2J ) that were H3K27me3-positive prior to EZH2 inhibition (Fig. S2K ). The precise set of EMT genes induced varied in the three analysed cell lines (Fig. S2M ), in fitting with previous observations indicating EMT can be induced through different combinations of EMT genes [ 22 ]. Consistently, different combinations of mesenchymal TFs were activated in the three different cell lines (Fig. S1L ). Of note, SNAI2 was gradually upregulated after 12 days of EZH2i treatment in exponentially growing MCF-7 cells (Fig. S1D , compare day six and day twelve). After 18 days of EZH2i treatment, SNAI1 , TWSIT1 and ZEB1 were also over-expressed. This suggests that the persistent absence of H3K27me3 facilitates a time-dependent gradual activation of the EMT programme and transition into the mesenchymal state.

Overall, we concluded that H3K27me3 deposition through EZH2 is required to maintain the transcriptional repression of mesenchymal genes and favour the residence of breast carcinoma cells in an epithelial state.

TGF-β-induced EMT is reversible in breast carcinoma cells

To examine whether EZH2 regulates transitions between the epithelial and mesenchymal states of breast cancer cells we setup an in vitro system to study the dynamics of EMT and its reverse process (mesenchymal to epithelial transition, MET) (Fig. 2A ). Treatment of MCF-7 cells with 10 ng/ml transforming growth factor beta (TGF-β) and 50 ng/ml epidermal growth factor (EGF) during six days induced the transcription of mesenchymal markers ( N-CADHERIN, NRP2, TWIST1 and SNAI2 ) and the downregulation of the epithelial marker E-CADHERIN (Fig. 2B ). Withdrawal of TGF-β and EGF from the culture media during six additional days led to the reversion of transcriptional changes: downregulation of the expression of mesenchymal genes ( N-CADHERIN, NRP2, TWIST1 and SNAI2 ) coupled to activation of the epithelial marker E-CADHERIN (Fig. 2B ). Importantly, mRNA-seq analyses demonstrated that TGF-β stimulation induced a wide reversible reorganisation of the transcriptome that involved 1108 genes (Fig. 2C ). This included the reversible activation of 750 genes enriched in EMT, cell migration and mesenchyme (cluster II, Fig. 2C ), as well as 358 reversibly repressed genes that included factors involved in oestrogen receptor signalling (cluster I, Fig. 2C ). Importantly, changes in the expression of epithelial and mesenchymal genes were accompanied by expected functional changes in cell mobility in cultured wound healing assays (Fig. 2D ). We concluded that transient stimulation with TGF-β and EGF is a valid system to study EMT-MET in vitro in MCF-7 cells.

A Scheme of the experimental design used to induce EMT-MET in MCF-7 cells. B Analysis of mRNA expression by RT-qPCR of indicated epithelial (black) or mesenchymal (red) genes during EMT-MET. Relative expression level against GAPDH and ACTIN B is shown. C Heatmap showing mRNA expression of 1108 genes that were reversibly regulated (FC > 2, p  < 0.05) during EMT-MET. Two independent replicates (R1 and R2) at day 0 (E), day 6 (M) and day 12 (ER) are shown. Gene ontology analyses of genes in indicated clusters are shown. D Brightfield images and quantification plot comparing the wound healing capacity after 72 h of cells obtained at day 0, 6 and 12 during EMT–MET. The mean and SEM of 3 experiments are shown in ( B ) and ( D ). Asterisks indicate statistical significance using a Mann–Whitney test (* p  < 0.05).

EZH2 is required to repress mesenchymal genes during TGF-β-dependent MET in breast carcinoma cells

To test whether inhibition of EZH2 alters EMT–MET we compared the behaviour of the 1108 reversible genes identified in Fig. 2C during transient TGF-β stimulation in the presence or absence of EZH2i and H3K27me3 marking (Fig. 3A, B ). Inhibition of EZH2 enhanced the transcriptional induction of 376 genes during EMT (cluster I, Fig. 3C ), and hindered the repression of 280 genes during MET (cluster II and cluster III, Fig. 3C ). This set of 461 genes was highly enriched for genes involved in EMT, TGF-ß stimulation, cell migration and mesenchyme (Fig. 3D ), indicating that EZH2 methyltransferase activity is required for reorganisation of epithelial-mesenchymal gene expression programmes during EMT-MET. In consonance, gene set enrichment analysis (GSEA) confirmed that MCF-7 cells treated with EZH2i displayed higher expression of mesenchymal genes than untreated cells (Fig. 3E ). Likewise, analysis of the expression of epithelial ( E-CADHERIN ) and mesenchymal ( N-CADHERIN, NRP2, TWIST1 and SNAI2 ) genes in cells treated or untreated with EZH2i also supported that inhibition of EZH2 promotes activation of mesenchymal markers during EMT and hinders their repression during MET (Fig. 3F ). Expectedly, examination of the in vitro wound healing capacity of MCF-7 cells showed that treatment with EZH2i slightly increased the mobility of cells after six days of EMT (day six) and hindered the restoration of the more immobile epithelial phenotype upon MET (day twelve) (Fig. 3G ). Therefore, we settled that EZH2 is required to downmodulate the expression of mesenchymal genes during EMT–MET in breast cancer cells and to allow efficient restoration of the epithelial state during MET.

A Schematic diagram of the experimental conditions used to study the inhibition of EZH2 activity (EZH2i 5 µM) during reversible EMT–MET (TGF-β + EGF) in MCF-7 cells. B Western blot analysis of whole-cell extracts comparing the levels of EZH2 and H3K27me3 during the EMT-MET experiment described in ( A ). ACTIN B provides a loading control. C Heatmap showing the expression of 1108 reversible genes (identified in Fig. 2C ) during EMT–MET in two biological replicates (R1 and R2) at day 0, day 6 (upon EMT) and day 12 (upon MET), in the presence or absence of EZH2i, are shown. Genes that are differentially expressed due to the presence of EZH2i are labelled as clusters I, II and III. D Gene ontology analysis of genes identified in clusters I, II and III in ( C ). E GSEA of EMT-associated genes in cells treated with EZH2i, relative to untreated, at day 6 (left) or day 12 (right). Normalised enrichment score (NES) and statistically significant false discovery rate (FDR < 0.25) are indicated. F RT-qPCR analysis showing mRNA level of indicated epithelial (black) and mesenchymal (red) genes during EMT–MET in the absence or presence of EZH2i. Expression level is calculated relative to GAPDH and ACTIN B . G Brightfield images and histogram analysing the effect of EZH2i treatment in wound healing closure after 72 h, in cells corresponding to day 6 or 12 during the EMT–MET described in ( A ). The mean and SEM of 3 experiments are shown in ( F ) and ( G ). Asterisks indicate statistical significance using a Mann–Whitney test (* p  < 0.05).

Expression of EZH2 inversely correlates with the expression of mesenchymal genes in human breast tumours

To examine whether EZH2 represses mesenchymal genes in breast cancer cells in vivo, we used genome-wide gene expression datasets of 1904 resected breast cancer tumours available at the Molecular Taxonomy of Breast Cancer International Consortium. Importantly, we found that the levels of EZH2 mRNA inversely correlate with the expression of EZH2 target genes (1126 out of the 1141 genes identified in Fig. 1A were analysed in these datasets) (Fig. 4A ). Negative correlation was more accused for the group of 197 bivalent genes that were induced upon treatment with EZH2i (Fig. 4A ). As expected, no significant correlation was found for a group of randomly selected control genes (Fig. 4A ). The expression values of individual mesenchymal genes such as SNAI2 also displayed the expected negative correlation (Fig. 4B ). Therefore, these analyses support that EZH2 functions as a transcriptional repressor of the mesenchymal gene expression programme in human breast cancer tumours. In agreement with previous reports [ 23 , 24 ], high expression of EZH2 was associated with poor survival probability in our patient cohort (median survival, high: 132.3 months, low: 172.9 months) (Fig. 4C ). Overall, we concluded that augmented expression of EZH2 is associated to reduced expression of EZH2-target mesenchymal genes and poor survival in breast cancer patients.

A Heatmaps of Spearman´s correlation between the mRNA of EZH2 and different subsets of genes in 1904 samples from breast cancer tumours. Bivalent genes include 1126 bivalent genes that were identified in Fig. 1A . Bivalent EZH2i responsive genes include 197 genes that are H3K27me3/H3K4me3-positive and are included in cluster II in Fig. 1F . The pattern of a set of 1126 randomly selected genes is shown for comparison purposes. B Spearman´s correlation of EZH2 and SNAI2 mRNA in 1904 patients of breast cancer. Each dot represents the expression values of one breast tumour sample. C Kaplan–Meier plots showing survival probability of 1904 breast cancer patients depending on the level of expression of EZH2 mRNA.

Metastasis causes around 90% of cancer-associated mortality, and therefore, understanding the mechanisms underlying metastatic dissemination is crucial to developing more effective therapies for cancer [ 10 ]. Metastatic dissemination relies on dynamic changes in carcinoma cell state that occur during epithelial to mesenchymal reversible transitions, and therefore, EMT–MET has emerged as a key druggable pathway in cancer intervention [ 10 , 25 ]. Our study reveals that the PRC2 catalytic subunit EZH2 coordinates the repression of the mesenchymal gene expression programme in breast cancer cells, facilitating MET upon TGF-β stimulation decay. Because MET is required for efficient tumour colonisation [ 10 , 25 ], our findings provide an explanation as to why EZH2-deficient breast cancer cells display reduced capacity to colonise new organs and form metastasis in mice models and patient-derived xenografts [ 13 , 14 , 15 , 16 ], as well as to the oncogenic behaviour of EZH2 as a marker of poor prognosis in breast cancer patients (this study and [ 24 , 26 , 27 ]). Importantly, our discovery that EZH2 coordinates EMT–MET is in consonance with previous reports studying lung cancer cells where it has been shown that the loss of function of EZH2 hinders repression of mesenchymal genes during MET [ 8 ], and reduces tumour colonisation capacity in mouse models [ 6 , 7 , 8 ]. Counterintuitively, it has been recently reported that the loss of function of a non-catalytic PRC2 subunit (EED) in experimentally transformed human mammary epithelial cells (HMLER) leads to increased metastasis in mouse xenograft experiments [ 9 ]. This apparent discrepancy might rely on variations of EZH2 activity in the different loss of function systems: while in our experiments and previous reports in breast [ 15 , 16 ] and lung [ 6 , 7 , 8 ] cancer the function of EZH2 was assayed in systems in which EZH2 protein level was reduced to undetectable levels, EED-depleted HMLER cells display only partial downregulation of EZH2 protein [ 9 ]. We propose that low activity of EZH2 protein in EED-depleted HMLER cells promotes the transition of breast cancer cells into a metastable mesenchymal state without fully impairing MET. This might explain the enhanced tumour colonisation capacity observed in EED mutant cells because the latest reports indicate that cancer malignancy and disease progression rely on the ability of cancer cells to reside in intermediate metastable states within the epithelial–mesenchymal spectrum rather than in extreme epithelial or mesenchymal states [ 28 , 29 , 30 ]. Overall, this study establishes a molecular framework that brings together previous reports in breast [ 9 , 13 , 14 , 15 , 16 ] and lung [ 6 , 7 , 8 ] cancer cells, and supports a cell-of-origin-independent role of PRC2 as a direct modulator of the mesenchymal gene expression programme during EMT–MET in human carcinomas.

Breast cancer cell line culture conditions

The MCF-7, MDA-MB-231 and SKBR3 cell lines were kindly provided by the labs of Mª Jose Serrano and Dr. Juan Antonio Marchal (University of Granada, Spain). Cells were grown at 5% CO 2 and 37 °C in DMEM high glucose media supplemented with 10% heat-inactivated foetal bovine serum (FBS) (Gibco), penicillin/streptomycin (Gibco), l -glutamine (Gibco) and 2-mercaptoethanol (Gibco). Detailed information about the cell lines used is provided in Table S2 .

Induction of in vitro EMT–MET by TGF-β and EGF stimulation in MCF-7 cells

Epithelial MCF-7 cells were plated at a density of 10,000 cells/cm 2 and treated with 10 ng/mL TGF-β (Prepotech) and 50 ng/mL EGF (Prepotech) for 6 days to induce transition into a mesenchymal state (EMT). Thereafter, both cytokines were removed from the culture media and cells were grown for 6 additional days to allow reversion to the epithelial state (MET). Cells were trypsinized, counted and replated at initial density in fresh media every 48 h.

Treatment of breast cancer cell lines with EZH2 inhibitors

EZH2 was inhibited using 5 µM GSK126 (A3446, APExBIO). Treatments with EZH2 inhibitor for 12 days were carried out by refreshing GSK126 every 2 days as MCF-7 cells were split to allow exponential cell growth. Likewise, during EMT–MET experiments, GSK126 was refreshed every two days as cells were diluted to the corresponding density (10,000 cells/cm 2 ). In colony-forming assays, cells were pre-treated with GSK126 for 4 days to reduce the global H3K27me3 level before plating the cells. MCF-7, SKBR3 and MDA-MB-231 cells were seeded at a density of 200 cells/cm 2 to allow the formation of colonies after 14 days. Media was refreshed every 5 days including GSK126 in the treated culture. In growth curves, cell viability and wound healing assays, cells were pre-treated with GSK126 for 4 days to reduce global H3K27me3 levels before the start of the experiment. Cells were maintained in the presence of GSK126 during the experiment as detailed below.

In experiments where EZH2 was inhibited using EPZ-6438, MCF-7 cells were exposed to 2 µM of EPZ-6438 (S7128, Deltaclon) for 12 days, and the inhibitor was refreshed every two days as MCF-7 were split to allow exponential cell growth.

Knockdown of EZH2 using lentiviral shRNA

pLKO.1 Puro plasmid containing a shRNA sequence against EZH2 (5′- CCCAACATAGATGGACCAAT-3′) was purchased in MERCK (TRCN000040077). Lentiviral particles were generated as previously described in [ 8 ]. Briefly, HEK293T cells were lipotransfected with pLKO.1 Puro shRNA-EZH2 (MERCK) together with packaging (psPAX2, Addgene plasmids #12260) and envelope (pMD2.G, Addgene #12259) plasmids (18 µg total DNA; plasmid proportions of 3:2:1, respectively). To generate a control line of lentiviral particles the same protocol was performed using a plasmid with no shRNA expression (pLKO.1 Puro, Addgene#8453). The transfection mixture was removed after 6 h incubation, and 8 ml of total medium was added. The viral supernatants were collected at 48 and 72 h, and filtered through a 0.45 mm filter (Cat#FPE404030, JET Biofil, Guangzhou, China), aliquoted and immediately frozen at −80 °C. Transduction of MCF-7 cells was carried out in three cycles on infection using 2 mL of the supernatant containing lentiviral particles of sh EZH2 (pLKO.1 Puro shRNA- EZH2 ) or control (pLKO.1 Puro) respectively, and 8 µg/mL polybrene in 6x multiwells. sh EZH2 and control MCF-7 cells were selected with 0.75 µg/mL of puromycin for 4 days. After the selection time, the cells were maintained in culture additionally for 12 days before performing the experiments to allow them to acquire mesenchymal features.

Growth curve, cell viability and in vitro wound healing assays

To perform growth curves, cells were plated at a density of 10,000 cells/cm 2 and diluted before confluence to the initial density. Accumulative growth was calculated by applying the dilution factor used. Inhibition of EZH2 was carried out by pre-treating cells with GSK126 for four days and refreshing the inhibitor in every cell dilution.

To analyse cell survival cells were pre-treated with GSK126 for 4 days, plated in 96-well plate at a density of 3000 cells per well. After 48 h, cell media was replaced by fresh media containing 0.1 mM of resazurin. The plate was incubated for 4 h at 37 °C and fluorescence at 585 nm wavelength was measured. Same procedure was applied to GSK126 untreated control cells. Survival was estimated as the ratio of the signal measured in treated cells relative to untreated control.

In cultured wound healing assays MCF-7 untreated or pre-treated for 4 days with GKS126 were grown to 100% confluence. A 1000 µL sterile pipette tip was used to produce a scratch in the monolayer of cells. Standard cell media was changed to media containing 1% of FBS and supplemented or not with GSK126. Cells were allowed to close the wound for 48 h. The area of the wound was imaged every 24 h using a widefield microscope and quantified using Image J software. The area of the wound at each time point was normalised to the area of the wound at time zero.

RNA was isolated using Trizol reagent (Thermofisher), reverse transcribed using RevertAid Frist Strand cDNA synthesis kit (Thermofisher) and analysed by SYBRG real-time PCR using GoTaq qPCR Master Mix (Promega). Primers used are provided in supplementary Table S2 .

Western blot

Western blots of whole cell extracts, or histone preparations were carried out using standard procedures as previously described [ 31 ] (uncropped versions are presented in the original data file 1). The following primary antibodies were used: rabbit anti-EZH2 (Diagenode), mouse anti-H3K27me3 (Active Motif), rabbit anti-SNAI2 (CST), mouse anti-E-CADHERIN (BD), mouse anti-ACTIN B (Sigma-Aldrich), rabbit anti-ACTIN B (Cell signalling). Secondary species-specific antibodies conjugated to horseradish peroxidase were used: anti-rabbit-HRP (GE-Healthcare), anti-mouse-HRP (GE-Healthcare) and anti-goat-HRP (Abcam). Clarity Western ECL reagents (Bio-Rad) were used for detection. More information about the antibodies used is provided in Table S2 .

Immunofluorescence

Immunofluorescence analysis of MCF-7 cells was carried out as described previously [ 8 ]. Briefly, cells were fixed for 20 min in 2% paraformaldehyde, permeabilized for 5 min using 0.4% Triton-X100 and blocked for 30 min in phosphate buffer saline supplemented with 10% goat serum, 0.05% Tween 20 and 2.5% bovine serum albumin. A primary antibody against SNAI2 (CST) was used. The secondary antibody was anti-rabbit Alexa fluor 555 (Thermofisher). Slides were imaged by widefield fluorescence microscope Zeiss Axio Imager and images were analysed using Image J.

ChIP sequencing analysis

Public ChIP-seq datasets of H3K27me3 and H3K4me3 performed in MCF-7, MDA-MB-231 and SKBR3 (Table S2 ) were analysed as follows. Alignment of the sequence reads was done using Bowtie2 [ 32 ], and human genome hg19 was used for mapping. Unmapped and multimapped reads were filtered out with SAMTools [ 33 ] and SamBamba [ 34 ] to keep only uniquely aligned sequences. BigWigs were generated after normalising with their input signal using the BamCompare function of the deepTools suite package [ 35 ]. Peak calling was performed with MACS version 3 [ 36 ] using the input as background for normalisation. Peaks with q ≤ 0.05 values were considered significant. Significant peaks were annotated with Homer software [ 37 ] by defining promoter regions as ±2 kb from the start of the transcription start site (TSS). The coverage of the samples around the TSS was performed with the Bioconductor package coverageView for a genomic window of ±4 kb using a bin size of 10 bp. R version 4.2.2 and RStudio version 2022.7.1.554 were used. BigWigs were generated using the deepTools suite [ 38 ] and reads per million (RPM) were used to represent the ChIP-seq signal.

mRNA sequencing analysis

Total RNA was isolated using Trizol reagent (Thermofisher) or Rnease mini kit (Quiagen). Libraries and sequencing were performed at BGI Genomics. Strand-specific mRNA-seq libraries were generated using 200 ng of total RNA and the DNBSEQ library construction protocol. Libraries were sequenced using DNBSEQ high-throughput platform sequencing technology. Twenty million (150 bp paired-end) reads were obtained for each condition.

RNA sequencing data was analysed using the miARma-Seq pipeline [ 39 ]. First, quality control of reads was performed using FastQC software [ 40 ]. Reads were aligned using STAR 2.5.3a against the reference human genome hg19 (GENCODE assembly GRCh37.p13). To obtain expression values featureCounts 2.0.6 [ 41 ] was used. Reference gene annotations were obtained from the GENCODE assembly mentioned above. Normalisation of gene expression values was obtained by applying the trimmed mean values method (TMM) [ 42 ] using the NOISeq package [ 43 ]. Differential gene expression analysis was performed using the DESeq2 package [ 44 ]. GSEA [ 45 ] was performed against the set “Hallkmark Epithelial to Mesenchymal Transition” from the Molecular Signatures Database (MSigDB) [ 46 ].

Bioinformatic analysis of breast cancer tumour samples

Information of 1904 breast cancer tumour samples from the Breast Cancer METABRIC dataset was analysed using CBioportal tools. Kaplan Meier survival analysis and the correlation analyses between mRNA levels of EZH2 of selected target genes were performed.

Statistical analyses

Statistical significance ( p  < 0.05) was determined by applying a two-tailed non-parametric Mann–Whitney test. Spearman’s correlation coefficient was calculated to measure correlation among variables. All analyses were performed with GraphPad Prism 9 and/or R or Rstudio.

Data access

Datasets are available at GEO-NCBI with accession number GSE247138.

Blackledge NP, Klose RJ. The molecular principles of gene regulation by Polycomb repressive complexes. Nat Rev Mol Cell Biol. 2021;22:815–33.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Piunti A, Shilatifard A. The roles of Polycomb repressive complexes in mammalian development and cancer. Nat Rev Mol Cell Biol. 2021;22:326–45.

Article   CAS   PubMed   Google Scholar  

Schuettengruber B, Bourbon HM, Di Croce L, Cavalli G. Genome regulation by Polycomb and trithorax: 70 years and counting. Cell. 2017;171:34–57.

Bracken AP, Kleine-Kohlbrecher D, Dietrich N, Pasini D, Gargiulo G, Beekman C, et al. The Polycomb group proteins bind throughout the INK4A-ARF locus and are disassociated in senescent cells. Genes Dev. 2007;21:525–30.

Jacobs JJ, Kieboom K, Marino S, DePinho RA, van Lohuizen M. The oncogene and Polycomb-group gene bmi-1 regulates cell proliferation and senescence through the ink4a locus. Nature. 1999;397:164–8.

Serresi M, Gargiulo G, Proost N, Siteur B, Cesaroni M, Koppens M, et al. Polycomb repressive complex 2 is a barrier to KRAS-driven inflammation and epithelial-mesenchymal transition in non-small-cell lung cancer. Cancer Cell. 2016;29:17–31.

Serresi M, Siteur B, Hulsman D, Company C, Schmitt MJ, Lieftink C, et al. Ezh2 inhibition in Kras-driven lung cancer amplifies inflammation and associated vulnerabilities. J Exp Med. 2018;215:3115–35.

Gallardo A, Molina A, Asenjo HG, Lopez-Onieva L, Martorell-Marugán J, Espinosa-Martinez M, et al. EZH2 endorses cell plasticity to non-small cell lung cancer cells facilitating mesenchymal to epithelial transition and tumour colonization. Oncogene. 2022;41:3611–24.

Zhang Y, Donaher JL, Das S, Li X, Reinhardt F, Krall JA, et al. Genome-wide CRISPR screen identifies PRC2 and KMT2D-COMPASS as regulators of distinct EMT trajectories that contribute differentially to metastasis. Nat Cell Biol. 2022;24:554–64.

Lambert AW, Pattabiraman DR, Weinberg RA. Emerging biological principles of metastasis. Cell. 2017;168:670–91.

Kim KH, Roberts CW. Targeting EZH2 in cancer. Nat Med. 2016;22:128–34.

Huang R, Wu Y, Zou Z. Combining EZH2 inhibitors with other therapies for solid tumors: more choices for better effects. Epigenomics. 2022;14:1449–64.

Zhang L, Qu J, Qi Y, Duan Y, Huang YW, Zhou Z, et al. EZH2 engages TGFβ signaling to promote breast cancer bone metastasis via integrin β1-FAK activation. Nat Commun. 2022;13:2543.

Moore HM, Gonzalez ME, Toy KA, Cimino-Mathews A, Argani P, Kleer CG. EZH2 inhibition decreases p38 signaling and suppresses breast cancer motility and metastasis. Breast Cancer Res Treat. 2013;138:741–52.

Hirukawa A, Smith HW, Zuo D, Dufour CR, Savage P, Bertos N, et al. Targeting EZH2 reactivates a breast cancer subtype-specific anti-metastatic transcriptional program. Nat Commun. 2018;9:2547.

Article   PubMed   PubMed Central   Google Scholar  

Yomtoubian S, Lee SB, Verma A, Izzo F, Markowitz G, Choi H, et al. Inhibition of EZH2 catalytic activity selectively targets a metastatic subpopulation in triple-negative breast cancer. Cell Rep. 2020;30:755–770.e756.

Serresi M, Kertalli S, Li L, Schmitt MJ, Dramaretska Y, Wierikx J, et al. Functional antagonism of chromatin modulators regulates epithelial-mesenchymal transition. Sci Adv. 2021;7:eabd7974.

Comet I, Riising EM, Leblanc B, Helin K. Maintaining cell identity: PRC2-mediated regulation of transcription and cancer. Nat Rev Cancer. 2016;16:803–10.

Macrae TA, Fothergill-Robinson J, Ramalho-Santos M. Regulation, functions and transmission of bivalent chromatin during mammalian development. Nat Rev Mol Cell Biol. 2023;24:6–26.

McCabe MT, Ott HM, Ganji G, Korenchuk S, Thompson C, Van Aller GS, et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature. 2012;492:108–12.

Knutson SK, Wigle TJ, Warholic NM, Sneeringer CJ, Allain CJ, Klaus CR, et al. A selective inhibitor of EZH2 blocks H3K27 methylation and kills mutant lymphoma cells. Nat Chem Biol. 2012;8:890–6.

Cook DP, Vanderhyden BC. Context specificity of the EMT transcriptional response. Nat Commun. 2020;11:2142.

Adibfar S, Elveny M, Kashikova HS, Mikhailova MV, Farhangnia P, Vakili-Samiani S, et al. The molecular mechanisms and therapeutic potential of EZH2 in breast cancer. Life Sci. 2021;286:120047.

Jang SH, Lee JE, Oh MH, Lee JH, Cho HD, Kim KJ, et al. High EZH2 protein expression is associated with poor overall survival in patients with luminal A breast cancer. J Breast Cancer. 2016;19:53–60.

Dongre A, Weinberg RA. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat Rev Mol Cell Biol. 2019;20:69–84.

Kleer CG, Cao Q, Varambally S, Shen R, Ota I, Tomlins SA, et al. EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc Natl Acad Sci USA. 2003;100:11606–11.

Alford SH, Toy K, Merajver SD, Kleer CG. Increased risk for distant metastasis in patients with familial early-stage breast cancer and high EZH2 expression. Breast Cancer Res Treat. 2012;132:429–37.

Simeonov KP, Byrns CN, Clark ML, Norgard RJ, Martin B, Stanger BZ, et al. Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states. Cancer Cell. 2021;39:1150–62.e1159.

Lüönd F, Sugiyama N, Bill R, Bornes L, Hager C, Tang F, et al. Distinct contributions of partial and full EMT to breast cancer malignancy. Dev Cell. 2021;56:3203–21.e3211.

Article   PubMed   Google Scholar  

Pastushenko I, Brisebarre A, Sifrim A, Fioramonti M, Revenco T, Boumahdi S, et al. Identification of the tumour transition states occurring during EMT. Nature. 2018;556:463–8.

Gallardo A, Molina A, Asenjo HG, Martorell-Marugan J, Montes R, Ramos-Mejia V, et al. The molecular clock protein Bmal1 regulates cell differentiation in mouse embryonic stem cells. Life Sci Alliance. 2020;3:e201900535.

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31:2032–4.

Richter AS, Ryan DP, Kilpert F, Ramírez F, Heyne S, Manke T, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–5.

Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.

Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol cell. 2010;38:576–89.

Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 2014;42:W187–91.

Andrés-León E, Núñez-Torres R, Rojas AM. miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Rep. 2016;6:25749.

Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data . (Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom, 2010). http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ .

Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.

Tarazona S, Furió-Tarí P, Turrà D, Pietro AD, Nueda MJ, Ferrer A, et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015;43:e140.

PubMed   PubMed Central   Google Scholar  

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–25.

Download references

Acknowledgements

We are very grateful to Dr. Maria Jose Serrano, Dr. Sergio Granados-Principal and Dr. Juan Antonio Marchal for providing cell lines. We thank core facilities at GENYO for excellent technical support.

This article is financed by the Instituto de Salud Carlos III under the European funds of the Recovery, Transformation and Resilience Plan, with file code IHRC22/00007, by virtue of Resolution of the Directorate of the Instituto de Salud Carlos III, O.A., M.P. of December 22, 2022, granting grants Seal of Excellence ISCIII-HEALTH, and Financed by the European Union—NextGenerationEU. The Landeira lab is supported by the Spanish Ministry of Science and Innovation (EUR2021-122005, PID2022-137060NB-I00), the Andalusian regional government (PIER-0211-2019, PY20_00681) and the University of Granada (A-BIO-6-UGR20) grants. Efres Belmonte-Reche is funded by “Plan-Propio UGR” grant (A.CTS.264.UGR23). Lourdes Lopez-Onieva research is supported by the “Plan-Propio UGR” grant (C-CTS-274-UGR23).

Author information

Authors and affiliations.

Centre for Genomics and Oncological Research (GENYO), Avenida de la Ilustración 114, 18016, Granada, Spain

Amador Gallardo, Lourdes López-Onieva, Efres Belmonte-Reche, Iván Fernández-Rengel, Andrea Serrano-Prados, Aldara Molina, Antonio Sánchez-Pozo & David Landeira

Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain

Amador Gallardo, Efres Belmonte-Reche, Iván Fernández-Rengel, Andrea Serrano-Prados, Aldara Molina, Antonio Sánchez-Pozo & David Landeira

Instituto de Investigación Biosanitaria ibs.GRANADA, Hospital Virgen de las Nieves, Granada, Spain

Department of Biochemistry and Molecular Biology I, Faculty of Science, University of Granada, Granada, Spain

Lourdes López-Onieva

You can also search for this author in PubMed   Google Scholar

Contributions

DL designed and conceptualised the study. AG, LLO and DL designed experiments. AG, LLO, ASP and AM performed experiments. EBR and IFR performed bioinformatic analyses. LLO and ASP provided resources and supervision of experiments. AG and DL wrote the paper. All authors revised the paper. DL obtained funding and supervised research.

Corresponding author

Correspondence to David Landeira .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethics approval and consent to participate

Experiments were carried out in accordance with relevant guidelines and regulations.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Nickolai Barlev

Supplementary information

Supplementary figures and table legends., original western blots images, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Gallardo, A., López-Onieva, L., Belmonte-Reche, E. et al. EZH2 represses mesenchymal genes and upholds the epithelial state of breast carcinoma cells. Cell Death Dis 15 , 609 (2024). https://doi.org/10.1038/s41419-024-07011-y

Download citation

Received : 20 November 2023

Revised : 07 August 2024

Accepted : 16 August 2024

Published : 22 August 2024

DOI : https://doi.org/10.1038/s41419-024-07011-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.8(Spec Iss 4); 2015

Breast cancer and associated factors: a review

Mr ataollahi.

* Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

** Aja University of Medical Sciences, Tehran, Iran

MR Paknahad

*** Student Research Committee, Hormozgan University of Medical Sciences, Bandar Abbas, Iran

This article investigated different dimensions of breast cancer and its associated factors. It revealed that breast cancer was and continues to be among the most prevalent and growing malignant diseases among Iranian women in the past four decades. In this article, required information was collected through literature review and keyword (cancer, breast cancer, cell, gene, life quality, women, prevalence, productivity, age, obesity, alcohol, cigarette, menopause, genetic, Cytokine, and mortality) query in credible scientific websites such as SID, Google Scholar, and comprehensive portal of human sciences.

This disease affects all physical, mental, and social aspects of women life. On the other hand, such factors as social and family supports during the illness can reduce its damages. Although, the [exact] etiology of breast cancer is unknown, its associated risk factors were identified. Such factors as aging, history of breast cancer in the family, specific changes in breast(s), gene changes, history of productivity and menopause, lack of physical activity, alcohol consumption, obesity, nutrition, race, and radiation therapy to chest are risk factors of breast cancer.

Introduction

Increased incidence of cancer in recent years and its impact on different physical, mental, and social dimensions of human life have turned it to a major problem of the century [ 1 ]. The incidence of this disease in developed countries varies from 1 to 2 percent, with almost 5% yearly increase in less developed countries [ 2 ]. According to estimates, more than 7 million people globally die from cancer. It is predicted that the number of new cancerous cases rises from 10 to 15 million by 2020 [ 3 , 4 ]. Meanwhile, breast cancer is the most prevalent type of malignant neoplasms among women [ 5 ] with more than one million new cases per year [ 6 ]. In Iran, breast cancer accounts for the major type of cancer among women with the incidence of 21.4 [ 7 ], or 32% [ 8 ]. Breast cancer is the most common type of cancer among women in the US with the incidence rate of 12.5%. The risk of an individual dying from breast cancer is 1-in-35 [ 9 ]. At present, the chance of developing breast cancer over lifespan is 12% (1-in-8) in the United States [ 10 ]. Regarding the importance of this issue, this study sought to investigate breast cancer and its associated factors.

Cancer and quality of life

The World Health Organization (WHO) defines the quality of life as an individual's perception of his/her position in life in the context of the culture and value systems in which he/she lives and in relation to his/her goals, expectations, standards, and concerns [ 11 ]. Cancer affects patients’ quality of life to varying degrees. The major problems affecting patients' life quality are the mental and emotional impacts of illness, diagnostic and therapeutic measures, stress, pain, depression, and disease consequences on family, marital, and social relations, as well as the induced economic burdens, nutritional issues, and treatment complications [ 12 , 13 ]. Determination of the quality of life of cancer patients can provide medical staff with a new solution in helping them become independent in performing life affairs under critical and non-critical situations [ 14 ]. Improvement of quality of life of cancer patients is the primary objective of medical and therapeutic cares. Maximization of job capabilities and improvement of functional status and quality of life of the patients are of the important tasks of health care team [ 15 ].

Breast cancer and its etiology

Breast cancer is the most common type of cancer and the second leading cause of death. This disease is the primary cause of mortality among women aged 45–55 years [ 16 ], and is the second leading cause of cancer-induced death. The incidence of breast cancer is almost 1-in-8 women, requiring complete tissue removal, chemotherapy, radiotherapy, and hormone therapy most of the time [ 17 ]. Breast cancer is a type of tissue cancer that mainly involves inner layer of milk glands or lobules, and ducts (tiny tubes that carry the milk) [ 18 ]. The primary risk factors of cancer include age [ 19 ], high hormone level [ 20 ], race, economic status, and iodine deficiency in diet [ 21 - 23 ]. Breast cancer is a multi-stage disease, in which viruses play a role in one stage of this pathogenic process [ 24 ]. In general, viruses are involved with different cancer types [ 25 ].

Social support and breast cancer

The incidence of breast cancer is 1-in-9 women over lifespan. There are no accurate statistics on the incidence of the disease in Iran, but studies show that breast cancer is the second prevalent type cancer [ 26 ]. Breast cancer is among diseases with severe psychological impact, in which the thoughts of death and mastectomy cause fear and anxiety in the patient. A cancer patient goes through various psychological stages in coping with and diagnosing this disease. The world of a woman with cancer dramatically collapses in the blink of an eye. The patient becomes confused and her small hopes fade away to great disappointments. Nobody can deeply understand her feelings [ 27 , 28 ]; while, she strongly needs support. Studies show that support is a vital and multi-dimensional need that should frequently be provided to clients. Nurses and physicians usually prioritize physical support; whereas, psychological-mental supports are polled as more important than other things by such patients. Researchers in the field have addressed hidden suffering of the afflicted women and analyzed their description of the disease and suffering. They collected different reports of change in life cycle and style, in which various concepts such as transition, transformation, overcoming and exploration of meaning have been defined. The discovered meanings functioned as ways by which the patient obtained the accuracy, truth, balance, and integrity [ 29 ]. In a qualitative study, Hamilton et al. used the grounded theory methodology to investigate the attitude of men as husband, life partner, father, and caregivers about the breast cancer and chemotherapy of their partners. They used semi-structured guided interviews, in which two major subjects were identified: Concentration on the partner's illness, caring for her, and paying attention to family to maintain its flow [ 30 ]. In a qualitative study, Landmark and Wall analyzed experience of 10 women newly diagnosed with breast cancer (aged 39-60 years). They aimed to improve nurses’ perception of mechanism of the patients' experience. Results revealed some aspects of their life. These experiences included emotional reactions, physical changes of body, mental image change, feminine identity, main activities, and social network. Understanding these experiences is very important for nurses as supporters of patients during the treatment and improvement process. Nurses should learn this knowledge and use it as much as possible in helping women with breast cancer and their families in gaining access to adaptive methods [ 31 ]. Regarding the deep impacts of this phenomenon on the patient and her family, and to provide them with appropriate support, putting effort to understand the experience of the involved people is very important. This is because a successful management of them and their family requires a comprehensive understanding of their experience [ 32 ]. Although medical team members may have acquired experience from their personal and professional life, functioning based on these experience limits the power of thinking and judgment [ 33 ]. Along with the concept of therapy, it is believed that therapists are required to understand the suffering of all people and even themselves as individuals to be capable of providing services in an emotional and empathic framework [ 34 ].

Family and breast cancer

Breast cancer is of the most important factors that risk physical, mental, and social health of women. Some therapeutic complications affect the patient's self-awareness, self-confidence, and sense of self-worthlessness and -acceptance. Suffering from disease, concerning about family future, fear of death, therapeutic complications, reduced performance, and mental imagery disorder are among factors that impair the mental health of patients with breast cancer [ 35 ]. To women, the loss of breast means losing feminine identity. In addition, although chemotherapy is an important cancer treatment method, it dramatically affects the quality of life of patients and impairs their physical, mental, social, and spiritual well-being [ 36 ].

Cancer is a disease that involves the whole family. Different studies have reported disruption in daily life of family caregivers. In a qualitative study, two main concepts were found from the experience of partners: concentration of the partner's illness and caring for her, and concentration on family to maintain it. Some marginal concepts in this study included presence, reliance on medical team, decision-making, and handling financial affairs [ 30 ]. Chronic disease of a family member dramatically affects the whole family. In such circumstances, several factors including role change, doubt, losing the sense of control, stepping into an unfamiliar environment, economic issues, etc. lead to family crisis [ 37 ]. According to Landmark and Wall, many women wish that their life patterns become normal, the same as before. This is also true to the whole family and can be seen as an adaptive approach. Women consciously choose activities that bring meaningful experience to their body and spirit. These activities vary from fantasizing to engaging in routine housekeeping duties. In their study, the role of supportive systems has been emphasized. They classified such systems into different categories namely family, other women, as well as institutions and organizations including department of surgery and insurance. The majority of patients find support from relatives. It is a valuable support that encourages women to take up against their illness more seriously [ 31 ].

Results from another study have shown the salient role of family and doctors for several patients. Many participants have mentioned the valuable role of receiving information and support from specialists. Several patients have reported that they have received romantic care and huge support from their families, helping them in adapting to new situation and returning to life. Partners and children, especially daughters, have been an enormous help. Yet, all patients have not had supportive family and have been even left alone [ 38 ].

Religion and breast cancer

Religion is a positive framework for grasping hidden meaning in disease. In the mentioned study, faith was considered as a powerful resource that alleviates concern and stress, and brings real comfort, which can be effective in adaptation with and return to the life [ 39 ].

Cigarette smoking and breast cancer

Identification of Breast cancer, as the most important cancer in women, and exploring its risk factors have interested researchers for many years [ 40 ]; however, the role of cigarette has not been considered as a cause until recently [ 41 ]. Increased incidence of breast cancer parallel with lung cancer in women in recent decades have attracted researchers towards increased rate of female smokers, aiming at finding a similar cause for this ascending trend. It is almost for two decades that researchers have addressed the relationship between breast cancer and cigarette smoking, leading to at least 22 published articles only by the late 80s. Different studies have suggested a weak relationship, lack of relationship, or supportive effect. The emphasis of these articles has been on active cigarette smoking and breast cancer. Investigation into indirect correlation of cigarette smoking with breast cancer has been less undertaken, but has delivered fixed results. Women exposed to cigarette smoke during childhood or married to a cigarette smoker are more prone to breast cancer [ 42 , 43 ]. In a meta-analysis by Kuder et al. into indirect exposure to cigarette-smoke and the risk of breast cancer, a weak relationship was found; therefore, further studies are required to prove this causal relationship [ 44 ]. Results of Reynold et al.’s study on 116,544 women showed increased chance of developing breast cancer in cigarette-smokers, corroborating the role of cigarette in breast cancer etiology [ 45 ]. Rousseau et al. determined the susceptibility of breast tissue through growing and differentiating it [ 46 ]. Breast cells differentiated from the parts 1 and 2 are susceptible to chemical mutagens that occur before menopause; whereas, those differentiated from the part 3 are mutagen-immune. According to this study, it is supposed that exposure period to breast carcinogens determines susceptibility to carcinogenesis. For example, an early exposure, especially before the first pregnancy, may end in breast cancer, due to genotoxic mechanisms; whereas, the subsequent exposures have protective effects because of anti-estrogenic characteristic of cigarette. However, it should be considered that the duration of cigarette-smoking may neutralizes this effect. As a result, it is very important to determine whether the exposure to cigarette smoke was direct or indirect. The discovered relationship of cigarette-smoking with breast cancer (1984) confirmed this protective effect [ 47 ]. A study (1990) showed that the relative chance of developing breast cancer in cigarette-smokers versus non-smokers was 1-in-12 in case studies and 1-in-14 in cohort studies [ 48 ]. The onset of smoking in younger age increases the risk of cancer breast. Women who started smoking at 10-14 years were more prone to breast cancer [ 49 ]. The risk of breast cancer is higher in women with family history of breast cancer, ovarian cancer, or both [ 50 ].

Cancer and genetic factors

Breast cancer is a highly heterogeneous disease that is developed by mutual impact of genetic risk factors and environmental factors. It leads to progressive aggregation of genetic and epigenetic changes in breast cancer cells. Although epidemiological evidence highlight the presence of risk factors (such as age, obesity, alcohol use, and exposure to estrogen in lifetime), family history of breast cancer is the strongest one. Almost 20% of all breast cancers have family origin, and etiologically are dependent to a specific predisposing gene of that disease [ 51 ].

Nutritional factors and breast cancer

Among the nutritional factors, weight gain and high calorie intake are two causes of breast cancer development. Kopans and Greenwald put that obesity and high BMI in post-menopause increases the risk of breast cancer; whereas, there is not such relationship in pre-menopause women [ 52 ]. For the first time in 1940, research findings showed that increased use of fat leads to breast tumor in animals [ 53 ]. Howe and Goodwin reported a positive correlation between high fat intake and the risk of breast cancer [ 54 ]. Another study reported a positive significant relationship between animal protein intake and the risk of breast cancer [ 55 ]. In general, the relationship with the risk of breast cancer development is uncertain [ 56 ]. On the one hand, calorie intake leads to weight gain and obesity; on the other hand, it results in increased height in childhood and preterm menopause. Both factors can establish the context for cancer development in future [ 57 ].

BRCA1 and breast cancer

The main risk factors of non-genetic breast cancer have hormonal origin. For example, gender, the age at menarche and menopause, reproductive history, breast-feeding, and the use of exogenous estrogen (with external origin) can be mentioned. In most cases, non-genetic breast cancer occurs among menopausal women who have high expression of estrogen receptor. Estrogen has at least two main roles in breast cancer development: (1) Estrogen metabolites can mutate or generate DNA-damaging free radicals [ 58 ], and (2) estrogen can proliferate cells in precancerous and cancerous lesions through its hormonal activity. In addition, since an important part of breast carcinoma is estrogen-receptor-negative (or ER-), other mechanisms are also involved in the development of breast cancer [ 59 ]. Mutation of BRCA1 raises the risk of breast cancer to 51% and 85% by the age 50 and 70 years, respectively; it also raises the risk of ovarian cancer to 23% and 63% by the age 50 and 70 years, respectively [ 60 ].

Immune system and breast cancer

The immune system is totally able to combat tumors and many immunological parameters applying cytokines for example IL-12 & IFN-γ play major roles in this regard. IL-12 is also the major cytokine responsible for the differentiation of TH1 cells, which are potent producers of IFN-γ, IFN-γ in turn has a powerful enhancing effect on the ability of phagocytes to produce IL-12 as well as having an important role in cellular immune response [ 61 ].

Breast cancer in Iran

The incidence of this disease is about 20-in-1000 per year in Iran. Therefore, the probability of new cases with breast cancer is 6,000 (almost 1-in-10), out of 30 million women in the country [ 62 ]. Although Iran has lower incidence of breast cancer, as compared to other countries, recent increase of this problem has turned it into the most common type of lesion among Iranian women. This cancer affects Iranian women at least one decade earlier than their counterparts in developed countries (more than 30% of the patients are younger than 30 years) [ 63 , 64 ].

Methodology

In this article, required information was collected through literature review and keyword (cancer, breast cancer, cell, gene, life quality, women, prevalence, productivity, age, obesity, alcohol, cigarette, menopause, genetic, Cytokine, and mortality) query in credible scientific websites such as SID, Google Scholar, and comprehensive portal of human sciences.

Breast cancer was and continues to be among the most prevalent and growing malignant diseases among Iranian women in the past four months. Breast cancer is a disease that involves the patient, family, and community, and wastes many financial and spiritual resources. This cancer is developed in breast tissues including ducts (tiny tubes that carry the milk) and lobules (milk-producing glands). Breast cancer is not gender-specific, but rarely develops in men. Although the exact cause of breast cancer is unknown, specific risk-factors have been identified. Different types of cancer have different risk factors. Some of these risk factors such as cigarette-smoking, alcohol use, and diet can be changed and depend on life style. However, other factors like age, race, gender, and family history are fixed and unchangeable. Having one or more of these risk factors does not necessarily mean infliction.

Although many of these risk factors increase the chance of breast cancer development and progress, its exact mechanism is not clear. It seems that hormone plays a very important role in some types of breast cancer; however, its development and progress mechanisms are not very clear. In general, it can be said that such factors as aging, history of breast cancer development in family, certain changes in breasts, genetic changes, history of productivity and menopause, lack of physical activity, alcohol-use, diet and nutrition, race, and radiation therapy to chest are risk factors of breast cancer.

This disease affects different physical, mental, and social aspects of women life. On the other hand, such factors as social and family supports during the illness can reduce its negative impacts.