I2 = 34.6%) for processed meat, 0.92  for soy food, 0.93  for skim milk, and 0.90  for yogurt. Similar conclusions were obtained in dose-response association for each serving increase: total red meat , fresh red meat , processed meat , soy food , and skim milk . There was a null association between poultry, fish, egg, nuts, total milk, and whole milk intake and breast cancer risk. Higher total red meat, fresh red meat, and processed meat intake may be risk factors for breast cancer, whereas higher soy food and skim milk intake may reduce the risk of breast cancer.Protein is important to the human body, and different sources of protein may have different effects on the risk of breast cancer. Thus, we conducted a meta-analysis to investigate the association between different dietary protein sources and breast cancer risk. PubMed and several databases were searched until December 2015. Relevant articles were retrieved according to specific searching criteria. Forty-six prospective studies were included. The summary relative risk (RR) for highest versus lowest intake was 1.07  1.01–1.14,  Protein is important to the human body. Protein is involved in the constitution of human tissues and the regulation of various physiological functions. It is essential for body growth and development, as well as the transport of many important substances, and the provision of biological energy. Protein deficiency can induce many types of diseases, such as retarded growth and development, fatigue, and nutritional edema, and it can even be life threatening ,2.No food contains exclusively protein; we must always intake different sources of protein from a variety of compounds. Different sources of protein might have different effects on breast cancer risk. For example, carcinogenic byproducts—such as heterocyclic amines and polycyclic aromatic hydrocarbons formed during the high-temperature cooking of red meat ,4—can inMeat, egg, soy food, and milk are common and important sources of dietary protein. In this meta-analysis, we explored the effects of total red meat (including fresh red meat and processed meat), fresh red meat, processed meat, poultry, fish, egg, soy food, nuts, total milk, whole milk, skim milk, and yogurt on breast cancer incidence.This meta-analysis was conducted in adherence to the Preferred Reporting Items for Systematic Review and Meta-analysis statement guidelines . We systStudies were included in the meta-analysis if they met the following criteria: (1) prospective cohort studies, nested case–control studies, or case–cohort studies; (2) the relationship between dietary protein sources consumption and risk of breast cancer was evaluated; (3) the outcome of interest was incidence of breast cancer; (4) the relative risk (RR) with corresponding 95% confidence intervals (CI) of breast cancer was reported. Retrospective studies, cross-sectional studies, correlation studies, studies in animals, and studies in mechanisms were excluded. If the articles were duplicated or from the same study population, only the study with the most recent or the most complete study was included.The following data were extracted by two independent researchers: The first author’s name, publication year, country, study population, study design, duration of follow-up, number of cases, cohort size, person-years for the population, age at baseline, menopausal status, type of protein, RR with corresponding 95% CIs for each exposure category, and covariates. Disagreements were resolved by the group discussion of all researchers. We selected the effect estimates with more adjustment factors. The Newcastle–Ottawa scale was used to evaluate the quality of each study .Relative risk was used for risk estimates, and it was estimated by hazard ratios or RR in cohort studies and odds ratios in nested case–control studies. We used log transformed RR and its corresponding 95% CIs from each eligible study for the meta-analysis. First, we determined the summary RR of breast cancer, comparing the highest versus the lowest categories with a random-effects model . Then, wI2 statistics, and we considered p-values < 0.10 and I2 > 50% to be indicative of significant heterogeneity [p < 0.05. All statistical analyses were performed using STATA version 12.0 .Heterogeneity among studies was assessed using Cochran’s Q and ogeneity ,20. SubgThe flowchart of the literature search is presented in I2 = 63.1% (See p = 0.536) or Egger’s test (p = 0.332).Highest versus lowest category analysis. Eight cohort studies ,41,48,583.1% See . No publI2 = 7.1% (see p = 1.000) or Egger’s test (p = 0.121). We observed a linear association between total red meat intake and increased risk of breast cancer (p = 0.157)  see A.I2 = 53.3% (See p = 0.732) or Egger’s test (p = 0.605).Highest versus lowest category analysis. Twelve cohort studies ,50,55,573.3% See . No publI2 = 56.4% (see p = 0.266) or Egger’s test (p = 0.110). We observed a linear association between fresh red meat intake and increased breast cancer risk (p = 0.292)  see B.I2 = 34.6% (see p = 0.743) or Egger’s test (p = 0.251).Highest versus lowest category analysis. Fourteen cohort studies ,53,55,574.6% see . No publI2 = 11.8% (see p = 0.945) or Egger’s test (p = 0.566). We observed a linear association between processed meat intake and increased risk of breast cancer (p = 0.633)  see C.I2 = 58% (See p = 0.755) or Egger’s test (p = 0.558).Highest versus lowest category analysis. Eleven cohort studies ,41,50,55 58% See . No publI2 = 33.2% (See p = 0.107) or Egger’s test (p = 0.09). We observed a null association between poultry intake and risk of breast cancer.Dose-response analysis. Ten cohort studies ,50,55,603.2% See . No publI2 = 47.9% (See p = 0.705) or Egger’s test (p = 0.613).Highest versus lowest category analysis. Eighteen cohort studies ,53,55,597.9% See . No publI2 = 33.3% (See p = 0.100) or Egger’s test (p = 0.089). We did not observe a linear association between fish intake and risk of breast cancer.Dose-response analysis. Thirteen cohort studies ,55,59,603.3% See . No publI2 = 6.7% (See p = 0.754) or Egger’s test (p = 0.593).Highest versus lowest category analysis. Nine cohort studies ,41,50,536.7% See . No publI2 = 26.9% (See p = 0.266) or Egger’s test (p = 0.340). We found that egg intake did not influence the risk of breast cancer.Dose-response analysis. Eight cohort studies ,41,50,536.9% See . No publI2 = 0.0% (see p = 0.592) or Egger’s test (p = 0.413).Highest versus lowest category analysis. Ten cohort studies ,49,54,560.0% see . No publI2 = 0.0% (see p = 0.764) or Egger’s test (p = 0.981). We observed a linear association between soy food intake and decreased risk of breast cancer (p = 0.908)  see D.I2 = 0.0% (See p = 1.000) or Egger’s test (p = 0.461).Highest versus lowest category analysis. Three cohort studies ,8,47 inv0.0% See . No publI2 = 0.0% (See p = 0.100) or Egger’s test (p = 0.955). We did not observe a linear association between nut intake and risk of breast cancer.Dose-response analysis. Three cohort studies ,8,47 wer0.0% See . No publI2 = 53.5% (See p = 0.495) or Egger’s test (p = 0.292).Highest versus lowest category analysis. Eighteen studies from sixteen cohorts ,52,53,553.5% See . No publI2 = 36.4% (See p = 0.436) or by Egger’s test (p = 0.355). We did not observe a linear association between total milk intake and breast cancer risk.Dose-response analysis. Eleven cohort studies ,50,53,556.4% See . No publI2 = 37.4% (See p = 0.917) or Egger’s test (p = 0.723).Highest versus lowest category analysis. Nine studies from eight cohorts ,50,52,557.4% See . No publI2 = 32.8% (See p = 1.000) or Egger’s test (p = 0.660). We did not observe a linear association between whole milk intake and risk of breast cancer.Dose-response analysis. Five cohort studies ,32,50,552.8% See . No publI2 = 40.1% (see p = 0.266) or Egger’s test (p = 0.616).Highest versus lowest category analysis. Eight studies from seven cohorts ,52,53,550.1% see . No publI2 = 11.9%. (see p = 0.806) or Egger’s test (p = 0.498). We observed a linear association between skim milk intake and decreased risk of breast cancer (p = 0.109)  see E.I2 = 0.0% (see p = 0.764) or Egger’s test (p = 0.77).Highest versus lowest category analysis. Seven studies from five cohorts ,51,53,550.0% see . No publI2 = 0.0% (See p = 1.000) or Egger’s test (p = 0.488). We did not observe a linear association between yogurt intake and risk of breast cancer.Dose-response analysis. Only three cohorts ,53,55 we0.0% See . No publp < 0.1). For soy food and total red meat, we did not conduct the subgroup analysis, due to no evidence of heterogeneity in the summary analysis . Given that only three cohort studies investigated the association between nut and yogurt intake and risk of breast cancer, we also did not conduct the meta-regression and subgroup analysis for nuts and yogurt.Subgroup analysis and meta-regression were conducted for both highest versus lowest analysis  and doseIn sensitivity analysis, we removed one study at a time and calculated overall homogeneity and effect size. We confirmed that the summary results were reliable for most sources of dietary protein (data not shown).This meta-analysis suggested that higher intake of soy food and skim milk could decrease the risk of breast cancer, and that higher intake of processed meat may increase the incidence of breast cancer. Dose-response analysis revealed the summary risk of breast cancer decreased by 9% for soy food, decreased by 4% for skim milk, and increased by 9% for processed meat. For total red meat and fresh red meat, the dose-response analysis suggests that increased intake of these foods could increase the risk of breast cancer, but the results of summary RR for highest versus lowest intake failed to reveal an increased risk. For yogurt, we found it could decrease risk of breast cancer, and the summary RR for highest versus lowest intake was 0.90 (95% CI 0.82–1.00). However, the result was not consistent with the result from dose-response analysis. Only three cohorts were eligible for the dose-response analysis for yogurt. More studies are needed to investigate the association between yogurt intake and risk of breast cancer. We also found a null association between poultry, fish, egg, nut, total milk, and whole milk consumption and risk of breast cancer.N-glycolyneuraminic acid were enriched in red meat, which could promote inflammation, oxidative stress, and tumor formation [Meat is an important source of protein. NHANES data indicated that approximately 58% of the meat consumed was red meat and that 22% of the total meat consumed was processed meat in the USA . Evidencormation ,64,65;  allowed us to quantitatively assess the association of different sources of protein consumption and breast cancer incidence. Thirdly, our results were based on dose-response meta-analysis, which would avoid some influence of potential residual confounding factors. Finally, we explored the sources of study heterogeneity and the influence of potential residual confounding factors through fifteen subgroup analyses and meta-regression.However, this meta-analysis also had several limitations. First, the inherent problems of unmeasured or residual confounders in the included studies might confound the association. Although we included fifteen subgroup analyses and meta-regression to explore and balance the influence of potential residual confounding factors , numerouIn conclusion, our results suggested that diets rich in soy food and skim milk were associated with decreased breast cancer risk. High intake of red and/or processed meat was associated with an increased risk of breast cancer. A null association was noted between poultry, fish, egg, nut, total milk, and whole milk consumption and risk of breast cancer. However, additional well-designed cohort or interventional studies and studies exploring the mechanisms in humans are needed to confirm the association.


In a way, it all started in Long Island, New York. The year was 1993. An apparent cluster of breast cancer cases had been discovered in Nassau and Suffolk counties, and some residents worried that pesticide applications on former farmland could be to blame.Breast cancer, like many cancers, is a challenging disease to understand thoroughly because its causes include both genetic and environmental factors. Breast cancer also takes many years to develop, making it difficult for researchers to identify environmental factors “after the fact” that might have contributed to the initiation of the cancer.Breast tissue receives hormonal signals from several endocrine organs  and responds to a wide range of hormones .“Endocrine disruptors are not expected to act as carcinogens per se—they don’t necessarily cause mutations or formation of DNA adducts,” explains Suzanne Fenton, leader of the NIEHS intramural Reproductive Endocrinology Group. “Rather, they cause a shift in how the body responds to normal hormones that the body produces all the time.”,Over time, studies examining how chemicals might contribute to breast cancer initiation have become better focused and more hypothesis-driven, says Deborah Winn, deputy director of the NCI’s Division of Cancer Control and Population Sciences. “The technology’s better, our questions are sharper, and the evidence is mounting,” she says. “I think we’re getting closer to [understanding environmental factors in a way] that’s clearer and that we can say something definitive about.”At the time of the hypothesized Long Island cluster, breast cancer incidence nationwide averaged about 130 diagnoses per 100,000 people per year.The NIEHS and the NCI established a research study called the Long Island Breast Cancer Study Project (LIBCSP) to find out why. This effort included five distinct case–control sub-studies designed to address specific environmental exposures while controlling for other risk factors, such as age and reproductive history. Their goal was to evaluate whether increased risk of breast cancer was associated with high-priority exposures including organochlorine pesticides (such as DDT and its metabolite DDE); polychlorinated biphenyls ; polyaromatic hydrocarbons ; and electromagnetic fields (such as those generated by power lines and electric blankets).Investigators ultimately reported two significant associations, both of which came from the project’s largest sub-study, the Breast Cancer and the Environment on Long Island Study. One showed that the odds of breast cancer were 50% higher in women with the highest levels of PAH-DNA adducts (which are biomarkers of exposure),Ideally, the predominately negative results would help reduce anxiety among women concerned about their own risk from environmental factors, Winn noted in a 2005 commentary.But some members of the advocacy community on Long Island remained convinced that other exposures were responsible for increased cancer rates. Clearly, the story was far from over. And many would argue it continues today, not just in Long Island but across the country.When those first five studies ended, the LIBCSP did not disappear; it simply entered its next phase of research. At the helm was principal investigator Marilie Gammon, a professor of epidemiology at the University of North Carolina at Chapel Hill. She had headed the Breast Cancer and the Environment on Long Island sub-study from its start in 1993. In 2001 she added a follow-up survivorship study—one of the first to examine the potential influence of environmental factors on breast cancer mortality—and included molecular epidemiological studies that involved examining tissue samples collected during the original research. Over the decades the effort has generated approximately 130 publications, Gammon says—a number that reflects the diversity of exposures and factors studied and the breadth of the findings reported. She continues to lead its investigations into PAHs and other exposures.in vivo and in vitro research to assess how the timing of certain exposures might make the breast more susceptible to damage.Even before results from the Long Island studies were published, the NIEHS sought a greater understanding of the role of the environment in breast cancer causation. As early as 1994, the institute called for new “Scientists were thinking about the idea that early-life exposures were most important and that it was too difficult to determine retrospectively environmental exposures in women who were getting breast cancer today,” says Julia Brody, executive director and senior scientist for the Silent Spring Institute, a Massachusetts-based research organization that has studied links between breast cancer and the environment since 1994.More than 20 years later, that idea remains central to the field. Researchers now realize that timing and length of exposure are critical variables in determining potential environmental influences on breast cancer development. Two particularly sensitive and widely studied exposure windows include the prenatal period and pubertal transition.The breast develops in stages occurring before birth and during puberty,Later, in 2002, the NIEHS convened a landmark workshop on the subject together with the NCI, the nonprofit National Breast Cancer Coalition, and advocacy groups.The workshop’s conclusions soon led to the formation of the federally funded Breast Cancer and the Environment Research Centers (BCERC). Administered by the NIEHS and the NCI over a 7-year span,More than 1,200 girls aged 6–8 years enrolled in the BCERC study. Researchers followed them at regular intervals to evaluate relationships among diet and obesity, exposures to a variety of endocrine-disrupting and carcinogenic chemicals, and pubertal development. The effort was unique in its combination of bench science, epidemiology, and public participation via community members and advocacy groups in one comprehensive program. Today this transdisciplinary approach is a hallmark of the NIEHS’s work in the field.,,The BCERC study eventually provided a rich set of findings showing what may happen in the body as a result of various environmental stressors during the pubertal window.What it didn’t do, however—and indeed, was not designed to do—was translate these results to cancer risk. That task falls to risk assessors based on evidence from research as it accrues over time. And more was on the way from another major research program at the NIEHS that was starting up just as BCERC was winding down: the Sister Study.In 1999, at the time the Sister Study was proposed,Prospective studies, or those that measure actual exposures during critical developmental windows and evaluate disease risk later in life, would be more definitive. However, existing prospective cohort studies at that time were focused mainly on diet, lifestyle, or hormone use rather than the chemical environment.Furthermore, Sandler says, because effects of environmental chemicals might be difficult to detect due to low levels of exposure, a prospective study of chemical exposures and breast cancer in the general population would require a large number of women. Such a study would take a long time, potentially decades, before enough women developed breast cancer that researchers could identify contributing factors in a statistically meaningful way.The solution? Improve statistical power by studying women who have an above-average genetic risk of developing breast cancer—women whose sisters had already been diagnosed.“If you have a sister or a daughter or a mother with breast cancer, you have on average twice the chance of getting breast cancer as someone else in the population,” Sandler says. “By focusing on sisters, we increased the likelihood that we could pick up relationships between genes, exposures, and breast cancer risk.”Sandler discovered another strength of the design during recruitment for this study: women whose families had been affected by breast cancer were highly motivated to participate. The study surpassed its recruitment goal, ultimately enrolling more than 50,000 women in the United States and Puerto Rico between the ages of 35 and 74. The women allowed researchers inside their homes to collect samples of blood, urine, nail clippings, and house dust.“We also collected comprehensive histories of medication use, occupational factors, and residential factors in addition to the usual questions about reproductive factors and lifestyle that most studies include,” Sandler adds, “plus information on trauma, stress, use of personal care products, and more.”Today the study continues tracking the health of these women. Each year participants report any changes to their health, and every 3 years they complete detailed questionnaires about their health, lifestyle, and chemical exposures. In addition, the research team has taken advantage of technological advances in genomics and other areas to expand the usefulness of the study data as a resource for other breast cancer researchers.The study has generated more than 65 papers since 2009.The investigators are also looking for previously unsuspected environmental risk factors for breast cancer, Sandler says. Only now, after roughly 3,000 new cases of breast cancer have been diagnosed since the women were enrolled, are investigators able to conduct the sorts of analyses they imagined from the start. “We’re now on the cusp of having a very large comprehensive database with which to generate new information on environment and breast cancer,” Sandler says.The Sister Study offered such a powerful design that early in its development it inspired a spin-off. In addition to the main prospective study, Sandler’s co-investigator Clarice Weinberg began setting up a companion retrospective family-based study called the Two Sister Study. Weinberg is the former chief of the Biostatistics and Computational Biology Branch at the NIEHS.The Two Sister Study focused on women with young-onset breast cancer (those diagnosed before age 50) and their families.In 2008 Weinberg received a grant from the Susan G. Komen Foundation to recruit and enroll more than 1,400 women recently diagnosed with young-onset breast cancer, plus their breast cancer–free sister who was already enrolled in the Sister Study and one or both parents. Participants provided detailed information about family and medical history, diet, and occupation, as well as samples of house dust and DNA.The Two Sister Study has produced 7 papers to date, including most significantly a genome-wide association study that investigates maternal factors and other genetic mechanisms in young-onset breast cancer.Still, as with the Sister Study, the best is likely yet to come. According to Weinberg, it could take another 10 years or so to fully analyze the retrospective data.Another way to find answers is through animal studies, which are often used as a counterpart to case–control and cohort studies or in some cases as a first line of evidence. This approach, too, has grown considerably more sophisticated over the years. Along with university-based research funded by the NIEHS, the institute’s National Toxicology Program (NTP) has focused research efforts on the role of environmental chemicals in mammary tumor risk under Fenton’s direction.The NTP program benefits from its ability to conduct lifetime rodent studies, which are often prohibitively expensive for university labs. Lifetime studies with mice and rats make it possible to expose animals to low doses of endocrine-disrupting chemicals at levels and on timelines that mimic human lifetime exposures, and evaluate mammary gland development and tumor incidence later in life. These rodent studies capture in 2 years what it could take 70 years to study in people.“We’re generating data that other people can’t afford to,” Fenton says. “There’s a lot that we don’t know about the chemicals that affect the breast. Some of the missing information may be mechanistic in nature, but we still don’t know which chemicals change the susceptibility of the breast to tumor formation. We know some chemicals that are bad actors, but we don’t have a very long or well-thought-out list, mostly because of the time and funding needed for these long-term studies.”One of the lab’s most important tools is a “whole-mount” preparation for assessing mammary gland development in mice and rats. This entails removing the intact mammary gland from the animal and viewing it in one piece. Whole mounts allow researchers to view the tissue in 3 dimensions, as opposed to sectioned into thin slices on dozens of separate slides. The whole-mount method offers an inexpensive way to identify preneoplastic lesions, precursors of tumor formation.Fenton and her colleagues use whole mounts to assess mammary gland development following early-life and pubertal exposures to endocrine disruptors such as herbicides and their metabolites, surfactants, phenolic compounds used in food packaging, and lipophilic flame retardants and pollutants. One recent study by the group, for example, shows that prenatal exposure to the common household chemical perfluorooctanoic acid (PFOA) causes significant and persistent mammary developmental delays in mice.,Between 2010 and 2014 the NIEHS funded a second phase of BCERCWith the launch of a third phase in 2015, researchers further expanded the program’s focus to include earlier and later exposures. For example, a newly funded study out of City of Hope, a California-based research and treatment center, is investigating whether exposures to endocrine-disrupting chemicals during the menopausal transition may mimic the effects of hormone therapy and promote the development of breast cancer.The latest BCERP studies also reveal a growing interest in breast density as an intermediate marker of breast cancer risk.Michigan State University professor of physiology Sandra Haslam has conducted animal research under BCERP since its origins as BCERC in 2003, although she first began studying hormones, human health, and breast cancer with NIH support as far back as 1980. Today she serves as co–principal investigator of a study investigating a new chemical of potential concern: oxybenzone, a common ingredient in sunscreen.“In reviewing the literature, there were indications that oxybenzone might have estrogenic activity,” she says. “There have been several studies in both mice and rats. By itself it doesn’t cause cancer, and [other researchers] have just looked at a lifetime exposure, but they never looked at intermediate points or specific effects on target tissues like the breast.”Five other studies in this latest round of grants seek to answer a wide range of questions that together illustrate how far the field has come and the challenges that remain. Researchers at the University of Massachusetts are studying how exposures to xenoestrogens  may erode the protective effect of pregnancy on breast cancer development.Outside BCERP, the NIEHS funds many other researchers with similar interests, including Maggie Louie, a biochemistry professor at Dominican University of California. She’s now investigating how chronic low-level cadmium exposures act at the molecular level to promote breast cancer development and progression. Her current studies build on previous work showing that cadmium, present in cigarettes, food, water, and some cosmetics, can cause more aggressive breast cancers.,Although breast cancer mortality rates have declined dramatically in recent decades, incidence rates today hover around the 130-per-100,000 mark first reached in the late 1980s.In 2008 Congress passed the Breast Cancer and Environmental Research Act, which among other provisions mandated the establishment of the Interagency Breast Cancer and Environmental Research Coordinating Committee (IBCERCC). In 2013 this interagency group of government, academic, and advocacy representatives issued recommendations for the future of breast cancer research.The committee pointed out that only about 10–11% of federally funded breast cancer research focuses on environmental contributions to breast cancer. “Breast cancer prevention is underfunded at the federal level in both research and public health programs, and future investments must focus on this area,” they wrote.Granted, investments in diagnosis and treatment have paid significant dividends. Breast cancer has a far better prognosis today than it did 3 decades ago. In 1986, 1 in 4 women diagnosed with breast cancer died from it, but in 2013, the latest year for which government statistics are available, that number was closer to 1 in 6.While similar progress has not yet been made in reducing incidence rates, many researchers believe we can get there through a fuller understanding of the complex environmental and genetic factors influencing breast cancer development. Science-based preventive measures could one day mean fewer cases of breast cancer on the front end, just as research into treatment has lessened its impact post-diagnosis.“We’re never going to be totally free of every chemical in the environment that could be detrimental,” Haslam says, “but we can make more intelligent choices about the ones that we have more information about. Hopefully we can address enough causative factors that we can reduce risk.”


Here, we evaluated whether obesity is differentially associated with the risk of breast tumor subtypes, as defined by 6 immunohistochemical markers , in the prospective EPIC-Germany Study  tumor tissues of 657 incident breast cancer cases were used for histopathological analyses. Associations between BMI and breast cancer risk across subtypes were evaluated by multivariable Cox regression models stratified by menopausal status and hormone therapy (HT) use.low, Bcl-2+ and p53- tumors , but not with risk of more aggressive tumor subtypes. Among postmenopausal users of HT, BMI was significantly inversely associated with less aggressive tumors . Finally, among pre- and perimenopausal women, Cox regression models did not reveal significant linear associations between BMI and risk of any tumor subtype, although analyses by BMI tertiles showed a significantly lower risk of less aggressive tumors for women in the highest tertile .Among postmenopausal non-users of HT, higher BMI was significantly associated with an increased risk of less aggressive, i.e. ER+, PR+, HER2-, Ki67Overall, our results suggest that obesity is related to risk of breast tumors with lower aggressiveness, a finding that requires replication in larger-scale analyses of pooled prospective data.The online version of this article (10.1186/s12885-018-4548-6) contains supplementary material, which is available to authorized users. Associations between etiological factors and cancer risk have been shown to be differential across molecular tumor subtypes in earlier epidemiological studies , 2. Withlow, Bcl-2+ and p53- tumors).The aim of the present study was to examine the associations between obesity with breast cancer risk across more refined tumor subtypes. For this purpose, we assessed six well-established immunohistochemical markers  in tumor samples of breast cancer cases from the prospective European Prospective Investigation into Cancer and Nutrition (EPIC)-Germany Study. We hypothesized that obesity would be particularly related to the development of less aggressive tumors  in the age range between 35 and 65 years were recruited at the study centers in the cities of Heidelberg and Potsdam between 1994 and 1998 , p = 0.005), while a significant inverse association was observed among HT users  (Table p = 0.72).Among postmenopausal non-users of HT, BMI was directly associated with higher overall breast cancer risk . BMI was not associated with more aggressive cluster 2 and cluster 3 tumors ; again, no significant associations with the risks of more aggressive cluster 2 and cluster 3 tumors were observed. While risk analyses per 5 kg/m2 did not reveal significant associations between BMI and risks of any tumor subtype in pre- and perimenopausal women, it is of note that women in the highest BMI tertile showed a significantly lower risk of less aggressive cluster 1 tumors as compared to women in the lowest BMI tertile . Sensitivity analyses excluding in situ cases yielded similar highly similar results . Importantly, no formal heterogeneity of associations between anthropometric factors and breast cancer risk across tumor subtypes, as either derived from hierarchical clustering or defined by individual IHC markers, was observed.The directions of associations with risk of tumor subtypes were highly similar when using waist and hip circumference as anthropometric indices of obesity instead of BMI, while the associations between waist-to-hip ratio and breast cancer risk were weaker and non-significant (data not shown). Risk associations among premenopausal women low, Bcl-2+, p53-) or a combination of these markers derived from hierarchical cluster analysis (cluster 1). By contrast, we observed no significant associations between BMI and risk of more aggressive tumors, irrespective of whether subtype classification was based on single markers or on marker combinations (clusters 2 and 3). Among HT users, higher BMI was linearly associated with reduced relative risk of less aggressive  tumors, while there were no significant associations with more aggressive tumors. Analyses by single markers did not reveal any significant associations among pre- and perimenopausal women, whereas risk of cluster 1 tumors was lower among women in the highest BMI tertile compared to those in the lowest.Here, we examined associations between BMI and breast cancer risk by tumor subtypes characterized by six immunohistochemical markers. Among postmenopausal women who did not use HT at the time of recruitment, higher BMI was significantly associated with increased risk of less aggressive tumors, as either defined by individual markers , Bcl-2 positivity and p53 negativity – immunohistochemical characteristics that are each associated with better prognosis [Various studies have shown associations between obesity and an increased risk of breast cancer among postmenopausal non-users of HT, particularly of ER+ / PR+ breast cancer, but not ER- / PR- breast cancer , 22, 23.rognosis , 24–26. rognosis . Our resrognosis , the ass2 (PGE2), which results in a local upregulation of aromatase activity and estrogen production [Biological mechanisms that may underlie the association between obesity and breast cancer include altered sex hormone metabolism, adipokine signaling, subclinical inflammation, hyperglycaemia, hyperinsulinaemia, and increased IGF-1 signaling , 29. Difoduction , and estoduction , 32. Bcloduction , 25, 33 oduction , 34. ThuOur findings among postmenopausal non-users of HT might suggest better prognosis in obese breast cancer patients, as they may be more likely to have less aggressive tumor subtypes than lean patients. Yet, prospective analyses in cohorts of breast cancer patients have clearly shown that breast cancer-specific survival is negatively impacted by obesity irrespective of menopausal status or hormone receptor status of the tumor , 36. TheSeveral limitations apply to our study. First, by using TMAs from preserved tumor material to assess tumor subtypes, we ensured homogeneity of testing conditions. However, when compared to full-slice IHC staining done for diagnostic purposes, IHC performed on TMAs may be more prone to misclassification of subtypes, especially when the tumor tissue exhibits heterogeneous expression of the markers in question and visual estimation of positive tumor cells is used. To minimize such misclassification, we used two tissue cores per tumor. Nevertheless, we cannot rule out that misclassification of tumor subtypes diluted associations in our study to some degree. Second, case numbers in our study may have been too low to detect weaker associations in some subgroups, especially for the more rare and aggressive cancer subtypes. Due to lower numbers of these tumors, tests for statistical heterogeneity in the associations between obesity and breast cancer risk across tumor subtypes were limited. In this context, it is worth mentioning that in previous analyses of the full European EPIC cohort, heterogeneity in BMI breast cancer risk associations by ER/PR status was restricted to women older than 65 years at diagnosis , and thalow, Bcl-2+ and p53- tumors, i.e. such with lower aggressiveness, in postmenopausal women. Further mechanistic studies are needed to determine which biological mechanisms underlie the detected associations, and larger pooled analyses of prospective cohort data will be required to further investigate relationships between obesity and molecular breast tumor subtypes, and particularly the less frequent subtypes, in more detail.In the present study, we evaluated associations between obesity and breast cancer risk by tumor subtypes, as defined by six immunohistochemical markers used in clinical routine to guide treatment and determine prognosis. Our data suggests that obesity is related to ER+, PR+, HER2-, Ki67Additional file 1:Figure S1. Flow Chart. (DOCX 29 kb)Additional file 2:Table S1. Characteristics of breast cancer cases with and without available immunohistochemistry (IHC) markers; Table S2. Antibodies; Table S3. Frequency of histological tumor types; Table S4. Hazard ratios of breast cancer across tertiles of BMI by clusters of breast tumors from hierarchical clustering, after exclusion of situ tumors; Table S5. Hazard ratios of luminal A breast cancer across tertiles of BMI; Table S6. Hazard ratios of breast cancer subtypes across tertiles of BMI among postmenopausal non-users of hormone therapy; Table S7. Hazard ratios of breast cancer subtypes across tertiles of BMI among postmenopausal users of hormone therapy; Table S8. Hazard ratios of breast cancer subtypes across tertiles of BMI among pre- and perimenopausal women; Table S9. Hazard ratios of breast cancer subtypes across tertiles of BMI among postmenopausal non-users of hormone therapy, after exclusion of situ tumors; Table S10. Hazard ratios of breast cancer subtypes across tertiles of BMI among postmenopausal users of hormone therapy, after exclusion of situ tumors; Table S11. Hazard ratios of breast cancer subtypes across tertiles of BMI among pre- and perimenopausal women, after exclusion of situ tumors. (DOCX 84 kb)


It is unclear if the use of hormone therapy (HT) in carriers of BRCA mutations improves the quality of life (QOL) without increasing the risk of breast cancer following a risk-reducing salpingo-oophorectomy (RRSO). Our objective was to assess the effect of HT on QOL and breast cancer risk, after RRSO.We searched MEDLINE, EMBASE, CINHAL, and others, from inception to July 22, 2016, to identify relevant studies. Two reviewers independently screened identified records for controlled trials and observational studies that addressed the effect of HT on QOL and breast cancer risk in women with BRCA mutations, post RRSO. Two reviewers independently extracted data on populations, interventions, comparators, outcomes, and methodological quality. Studies addressing the same outcome were synthesized using written evidence summaries or tables.Of the 1,059 records identified, 13 met our inclusion criteria. All studies were observational. Six studies assessed the effect on QOL. Of these, 3 showed improvement in QOL with HT use. The risk of breast cancer was evaluated in 4 studies. The mean duration of follow-up was 2.6 years (range 0.1-19.1). The risk of breast cancer did not change with HT use in all 4 studies.Cumulative evidence from our review suggests that short-term HT use following RRSO improves QOL. The effect on breast cancer risk is still unclear. There are too few long-term studies to draw any strong conclusions. The need for future well-designed RCTs for more established evidence is imperative.The online version of this article (doi:10.1186/s12905-017-0370-6) contains supplementary material, which is available to authorized users. BRCA mutations are associated with an increased risk of breast and ovarian cancer. In women with mutations of BRCA1 genes, the average cumulative risk for breast cancer by age 80 years is 67% and for ovarian cancer 45% .An immediate consequence of RRSO in premenopausal women is surgical menopause. Surgical menopause is associated with symptoms that can significantly affect a woman's quality of life (QOL), including vasomotor and urogenital symptoms, sexual dysfunction, sleep disturbances, and mood changes . FurtherThe concern in women with BRCA mutations is that HT may further increase breast cancer risk following a RRSO. The Women's Health Initiative (WHI) randomized trials found an increased risk of breast cancer with estrogen plus progestin, although not with estrogen alone . Data frCarriers of BRCA mutations and women at high risk for breast cancer are often challenged by the decision to undertake RRSO due to the health consequences associated with surgical menopause, and the need for HT that may further increase their breast cancer risk. In 2014, Marchetti et al addressed this important topic in a narrative review, but the lack of details of the literature review method lead to concern about the rigor and completeness of the review . SimilarWe, therefore, performed a systematic review to assess the effect of HT on QOL and breast cancer risk in women who have BRCA mutations and who also underwent RRSO for breast and ovarian cancer-risk reduction. The effect of HT on other short and long-term outcomes was also evaluated.Our study was designed and conducted in accordance with the guidelines for Meta-Analyses and Systematic Reviews of Observational Studies (MOOSE) .Eligible studies included women who had BRCA1/2 mutations or who had a high risk of breast and ovarian cancer  but had not undergone genetic testing, and who had undergone RRSO for cancer-risk reduction. Studies comparing the effect of HT  to placebo, non-exposed group or baseline, qualified for inclusion. All controlled trials and observational studies  were included. Review papers were screened for cited articles. Exclusion criteria included qualitative studies, hypothetical decision analysis, editorials and studies that did not assess the effect of HT on outcomes of interest. Studies that included women with a personal history of breast cancer were not explicitly excluded.Primary outcomes were QOL  and breast cancer risk. Secondary outcomes included: vasomotor symptoms, vulvovaginal atrophy (VVA), sexual function, mood, sleep disturbance, bone loss, cardiovascular disease, stroke, venous thromboembolism, and mortality.A systematic literature search was conducted by a librarian (SC) to identify all relevant published and unpublished studies. Searches using both controlled vocabulary and natural language were performed in databases including MEDLINE , EMBASE , and CINHAL  , with disagreements resolved by consensus. First, titles and abstracts were screened to select articles eligible for further review. Second, full-text of relevant articles was reviewed for eligibility. Reviewer agreement for confirmation of eligibility was 100%.Data extraction was completed independently by two reviewers , and discrepancies resolved by a third reviewer (NY). Data elements extracted included: manuscript characteristics; study design and settings; population characteristics; interventions; comparators; outcomes; and adjustments for potential confounders. The risk of bias assessment was conducted independently by two reviewers  and discrepancies resolved by consensus. The quality of studies was evaluated using the Jadad scale for RCTs,  and releOutcome data were synthesized by tabulating together all studies reported on specific outcomes. For each study, the outcomes reported were grouped by HT users versus non-users, with mean differences or measures of association as relevant. Descriptive analysis was used for each outcome.When sufficient homogeneity was demonstrated, outcome data were pooled quantitatively via a meta-analysis , or using the Sexual Activity Questionnaire (SAQ), Female Sexual Function index (FSFI) or Female Sexual Distress Scale – revised (FSDS) , 36, 38.Four studies measured the effect on VVA –34, 38. Three studies evaluated the effect of HT on bone loss , 33, 37.In our rigorously conducted systematic review, women with BRCA mutations who had RRSO had improvements in overall menopause-specific QOL with the use of HT, as well as reduction in vasomotor symptoms and VVA. The association of HT with breast cancer risk is still unclear due to the lack of long-term quality studies.QOL after RRSO is an important consideration for women who elect to have RRSO. QOL in this population is comparable with the general population, , 40 thouThe risk of breast cancer is the greatest concern women in the general population have when considering HT , 48. ThiSeveral recently published narrative reviews evaluated whether HT counteracts the breast cancer risk-reducing effects of RRSO , 23, 52.Among the other outcomes we studied, our systematic review found that HT was associated with a reduction in vasomotor symptoms. The benefits of HT on vasomotor symptoms is already well established . VVA wasThere are several limitations associated with our study, mainly related to the limitations of the included studies. First, all of the studies included in this review were observational with a small sample size. Evidence from these studies cannot be considered as robust as those from RCTs. Second, very few studies provided sufficient outcome data suitable for meta-analysis limiting the value of these analyses (Additional file Despite the limitations, our systematic review possesses several strengths that differentiate it from previous less-structured reviews on this topic , 22, 23.Cumulative evidence from our review highlights the benefits of HT in improving QOL and managing common menopausal symptoms induced by RRSO. However, no conclusions can be drawn about the safety of HT, as far as breast cancer risk is concerned. There are too few well-designed long-term studies to draw firm conclusions to guide women and their clinicians in their decision-making about HT. Future well-designed RCTs are needed. In the absence of clear evidence to inform the use of HT post RRSO, clinicians and patients must carefully discuss the potential benefits of HT as well as non-hormonal therapies in improving QOL, in the context of the unknown risk of breast cancer in this population. However, this may not be of concern for women who opt for risk-reducing bilateral mastectomy since the risk of breast cancer in this population is negligible.


BRCA1/2. We compared quality-adjusted survival after contralateral prophylactic mastectomy (CPM) with surveillance only (no CPM) among women with breast cancer incorporating the degree of family history.Family history of breast cancer is associated with an increased risk of contralateral breast cancer (CBC) even in the absence of mutations in the breast cancer susceptibility genes We created a microsimulation model for women with first-degree, second-degree, and no family history treated for a stage I, II, or III estrogen receptor (ER)-positive or ER-negative breast cancer at the ages of 40, 50, 60, and 70. The model incorporated a 10-year posttreatment period for risk of developing CBC and/or dying of the primary cancer or CBC. For each patient profile, we used 100,000 microsimulation trials to estimate quality-adjusted life expectancy for the clinical strategies CPM and no CPM.CPM showed minimal improvement on quality-adjusted life expectancy among women age 50–60 with no or a unilateral first-degree or second-degree family history ) and was unfavorable for most subgroups of women age 70 with stage III breast cancer regardless of degree of family history (range –0.08 to –0.02 QALYs). Sensitivity analysis showed that the highest predicted benefit of CPM assuming 95 % risk reduction in CBC was 0.57 QALYs for a 40-year-old woman with stage I breast cancer who had a first-degree relative with bilateral breast cancer.BRCA1/2 mutation carriers. For most subgroups of women, CPM has a minimal to no effect on quality-adjusted life expectancy, irrespective of family history of breast cancer.Women age 40 with stage I breast cancer and a first-degree relative with bilateral breast cancer have a QALY benefit from CPM similar to that reported for  BRCA1/2 mutation, hereditary mutations account for only 5–10 % of cancers [Despite the minimal survival benefit of contralateral prophylactic mastectomy (CPM) and a declining incidence of contralateral breast cancer (CBC)  since 19 cancers , 7.BRCA1/2 mutation), CPM has been shown to be cost-effective compared with surveillance in terms of life expectancy [BRCA1/2 mutation, Portschy et al. [Several decision analysis models have been developed for comparing CPM with surveillance only (no CPM) for the outcomes of life expectancy, quality-adjusted life expectancy, and cost-effectiveness –10. For pectancy . In womey et al.  showed aBRCA1/2 breast cancer susceptibility gene [BRCA1/2 mutation with any first-degree relative with bilateral breast cancer have CBC risk levels similar to those of BRCA1/2 mutation carriers [BRCA1/2 [Family history of breast cancer is considered to be an important risk factor for developing CBC even among women without mutations in the ity gene , 13. As carriers . Severalcarriers , 16 and [BRCA1/2 , 18. HowBRCA1/2 mutation, taking into consideration age at diagnosis, disease stage, ER status, and degree of family history of breast cancer. We hypothesized that women with a higher degree of family history would experience the greatest long-term quality-adjusted survival benefit from CPM.The aim of this study was to determine the impact of CPM on 20-year overall and disease-free survival and quality-adjusted life expectancy for women without a We developed an individual-level state-transition model to simulate the long-term survival outcomes of women who undergo CPM and women who do not following a unilateral mastectomy or breast-conserving surgery of the primary breast cancer Fig. . The modEach year, after treatment, the women may remain in a cancer-free (survivor) state, die of the primary cancer, develop a CBC, or die of other causes. The risk of CBC was incorporated into a 10-year period from the time of initial treatment. A 10-year period was chosen to look for the first event of a CBC because this time frame is sufficient to capture events in the natural progression of the disease . If a CBWe categorized patients who survive for 10 years without developing a CBC or since developing a CBC as long-term survivors. Long-term survival risks approach those of the general population after 10 years  and wereAlthough many clinical characteristics, such as tumor grade or ER status, may influence the growth rate and the metastatic potential of small tumors , the staThe annual risk of developing a CBC is thought to be 0.5–0.75 % , 24–29. BRCA mutation who had a primary breast cancer in four US cancer registries. The study population may have included women who were treated with a unilateral mastectomy or breast-conserving surgery followed by radiation and/or chemotherapy [The risks of CBC according to family history are an average annual risk of developing CBC estimated from a population-based case–control study of women without otherapy .To incorporate both ER status and family history into the risk for developing CBC, we assumed that the average risk for each degree of family history  is a weighted average of risks for patients with and ER-negative tumors. Then, using the proportions of ER-positive and ER-negative tumors, estimated to be 75 % and 25 %, respectively , and theThe stage distribution of a CBC was derived from a study of the Oregon State Cancer Registry database by Quan et al. . In thatWe incorporated quality of life for each health state using estimates of utilities reported in the literature , 31. UtiWe conducted a sensitivity analysis of base case parameter estimates of reductions in the risk of developing CBC after CPM. The variable means and ranges were established on the basis of published estimates Table . For our. Greater benefits were seen in patients age < 60, ER-negative status, disease stage I or II, and having a first-degree family history of breast cancer, especially with bilateral breast cancer. The benefits of CPM decreased in patients with characteristics associated with a greater risk of dying of primary breast cancer or other causes: higher disease stage and older age.CPM was associated with gains (0.02–0.82 years) in life expectancy for all patient subgroups , the maximum absolute difference in life expectancy was 0.77 life years when the CPM benefit was 100 % and the annual probability of developing a CBC was 1.68 % in women with a first-degree relative with bilateral breast cancer. The results were not sensitive to the first-year utility for CPM. For example, a threshold analysis of the first-year utility for CPM for a 40-year-old woman with stage I breast cancer and no family history of breast cancer indicated that a first-year utility above 0.482  would always result in CPM being the optimal preventative strategy. In the probabilistic sensitivity analysis for 40-year-old women with stage I breast cancer and no family history of breast cancer, 100 % of the trials had more effective CPM than no CPM irrespective of family history.BRCA1/2 mutation status, and younger age at diagnosis [CPM is performed most frequently among women with a family history of breast cancer, iagnosis –36. The iagnosis , suggestBRCA1/2 mutation status among women with a personal history of breast cancer and a strong family history of breast cancer has increased in importance [BRCA1/2 mutation carriers to identify other germline breast cancer susceptibility mutations, such as p53, PTEN, PALB2, and CDH1 [The determination of portance . Life exportance . In our portance  conducteand CDH1 –40.Although having a strong family history increases the risk of CBC, a recent study comparing outcomes among women age 41 or younger with breast cancer showed that having a family history of breast cancer did not worsen overall survival  also argOur decision model applied individual-level simulation to compare quality-adjusted health outcomes of CPM and surveillance only (no CPM). A strength of this analysis method was that it enabled us to estimate individual patient outcomes, such as the development of a CBC. As with all modeling studies, ours was subject to several limitations. Although our model considered a 10-year time frame in which patients were at risk for dying from the primary cancer, in reality breast cancer patients remain at risk of dying from their breast cancer after this time period, although the risk is reduced drastically , 42, 43.CPM reduces the risk of CBC and is associated with a gain in quality-adjusted life expectancy and disease-free survival in younger women who have an early-stage breast cancer and a history of breast cancer in first-degree relatives. However, even in this subgroup, CPM offers only a minimal overall survival advantage and the long-term psychosocial outcomes are unclear. Patients with ER-negative breast cancer consistently have a higher gain in quality-adjusted life expectancy than patients with ER-positive cancer, and this highlights the importance of anti-estrogen therapy as an alternative strategy for CPM among women with ER-positive breast cancer. Because patients are concerned about their lifetime experience of cancer, specifically driven by fear of developing a second cancer in the unaffected breast , quality


BRCA1-mutation, and oncogene-driven breast cancer and we proposed that RANKL/RANK might be involved in the initiation of breast tumors. We now report that in postmenopausal women without known genetic predisposition, high RANKL and progesterone serum levels stratify a subpopulation of women at high risk of developing breast cancer 12-24 months before diagnosis . In women with established breast cancer, we demonstrate that RANKL/OPG ratios change dependent on the presence of circulating tumor cells (CTCs). Finally, we show in a prospective human breast cancer cohort that alterations in RANKL/OPG ratios are significantly associated with breast cancer manifestation. These data indicate that the RANKL/RANK/OPG system is deregulated in post-menopausal women at high risk for breast cancer and in women with circulating tumor cells. Thus, serum levels of RANKL/OPG are potentially indicative of predisposition and progression of breast cancer in humans. Advancement of our findings towards clinical application awaits prior validation in independent patient cohorts.Breast cancer is the most common female cancer, affecting approximately one in eight women during their lifetime in North America and Europe. Receptor Activator of NF-kB Ligand (RANKL), its receptor RANK and the natural antagonist osteoprotegerin (OPG) are essential regulators of bone resorption. We have initially shown that RANKL/RANK are essential for hormone-driven mammary epithelial proliferation in pregnancy and RANKL/RANK have been implicated in mammary stem cell biology. Using genetic mouse-models, we and others identified the RANKL/RANK system as a key regulator of sex hormone,  Baset cancer , 13. Mort cancer  and othet cancer  have rect cancer –18. Furtt cancer , 20.Breast cancer is the most common female cancer, affecting approximately one in eight women during their life-time in North America and Europe , 22. IdeIn order to test whether deregulation of the RANKL/OPG system is associated with breast cancer in women without known genetic predispositions, we analyzed serum levels in postmenopausal women participating in the prospective UKCTOCS  study , 25 Tab. This coWe did not observe significant differences in RANKL, OPG or RANKL-to-OPG ratios in controls and women who developed breast cancer 12-24 months after serum sampling Figure . Since wp = 0.0269) fold risk (Table p = 0.0169) fold risk to develop breast cancer 12-24 months after diagnosis (Table p = 0.02) fold risk for developing breast cancer. Thus, high serum levels of RANKL and high serum progesterone stratify a subpopulation of postmenopausal women without known genetic predisposition at high risk of developing breast cancer 12-24 months before diagnosis.To assess whether high progesterone and high RANKL indeed define a subgroup of women with increased risk of developing breast cancer, we classified all women - cases and controls considered together - into tertiles according to their serum progesterone levels. Within each group women were classified into low, medium or high RANKL, OPG and RANKL/OPG based on controls within the corresponding progesterone group. Using logistic regression women with high progesterone and high RANKL exhibited a 4.8 . However, among this cohort high progesterone/high RANKL levels were not associated with an increased incidence of breast cancer Figure . IntriguTo test this hypothesis, we analysed serum RANKL/OPG in women with breast cancer diagnosis but in the absence of a tumor in the breast - a similar scenario as in the UKCTOCS cohort close to breast cancer diagnosis. We analyzed 116 ER positive breast cancer patients from the SUCCESS trial after surgery and before systemic therapy Table . Figure p = 0.006; paired t-test), whereas RANKL serum concentrations were not altered in subjects with other types of new-onset cancer and those remaining free of neoplastic disease , whereas no such increase was observed in patients with other types of cancer or subjects free of neoplastic disease  , 25, a mFor the current study, eligible cases, who developed estrogen receptor positive invasive breast cancer after joining the trial being identified through the cancer registries or self-reporting on follow-up questionnaire, donated a serum sample between 5 and 24 months prior to diagnosis (N = 98). Out of these 98 women 58 women donated a sample between 12 and 24 months prior to diagnosis (median age at sample taken 60.49 years) and 40 between 5 and 12 months (median age at sample taken 64.84). Eligible controls were matched UKCTOCS participants who had no history of breast cancer at last follow-up (median time between sample collection and follow up is 3.24 years) and had donated serum samples on the same day and in the same clinic (N = 180) as cases (median age at sample taken 62.94 years). Clinicopathological data of the cases are provided in Table 2)-Fluorouracil (500 mg/m2)-Cyclophosphamide  chemotherapy every three weeks (q3w) followed by 3 cycles of Docetaxel  q3w versus 3 cycles of FEC q3w followed by 3 cycles of Gemcitabine -Docetaxel  q3w. After chemotherapy, patients were randomized to receive two (q 3 months x 24 months) versus five years of zoledronate (q 3 months x 24 months followed by q 6 months x 36 months). Women with hormone receptor positive disease received endocrine treatment. Premenopausal women received tamoxifen alone or in combination with goserelin for two years if they were younger than 40 years of age or became premenopausal within 6 months of chemotherapy. Hormone receptor positive postmenopausal patients were treated with tamoxifen for two years followed by anastrozole for three years. 2026 patients had a blood sample drawn after R0 resection of the primary tumor before the start of adjuvant chemotherapy. All blood samples were obtained after written informed consent. The study was approved by all responsible ethical boards and conducted in accordance with the Declaration of Helsinki. Ethics number: 076-05 . EudraCT Nr.: 2005-000490-21; Protocol ID: SUCCESS - Vorlage Nr.: 4030471.Eligible patients were defined as women with histologically confirmed invasive breast cancer  who had their tumors completely removed and agreed to participate in the randomized phase III SUCCESS A study. The main inclusion criterion was the indication for adjuvant chemotherapy defined by either positive axillary nodal status or node negative disease with additional factors associated with an increased risk for recurrence, i.e. pT ≥ 2, grade 3, age younger than 35 years or negative hormone receptor status. The SUCCESS A study compared the disease-free survival after randomization in patients treated with 3 cycles of Epirubicin . According to the manufacturer both intra- and inter-assay coefficients of variation were lower than 10% with detection limits of 0.095 nmol/l for progesterone. Serum levels of soluble RANKL and OPG were measured using a sandwich and competitive enzyme immunoassay  as previously reported. According to the manufacturer, both the intra- and inter-assay coefficients of variation were lower than 10% with detection limits of 0.14 pmol/L for OPG and 0.08 pmol/L for soluble RANKL. Details on the measurement of OPG, free RANKL, and progesterone k cohort , 29.e-transformed to approximate a normal distribution. Variable levels prior to and after cancer manifestation were compared with the paired t-test. Changes of variable levels among subjects with incident breast cancer, subjects with new-onset non-breast cancer and those who remain free of neoplastic disease were compared by means of general linear models (GLM) for repeated measurements. The same analyses were performed for male subjects. To account for the varying time intervals and to eliminate potential effects of ageing, all OPG and RANKL measurements were adjusted to baseline age of given individuals by linear regression techniques. Cox proportional hazard models were used to assess whether baseline levels of OPG and RANKL or the RANKL/OPG were independent risk predictors for the manifestation of breast, prostate, and non-breast/non-prostate cancer. Subjects who developed cancer were censored with respect to subsequent follow-up. Proportional hazard assumptions were confirmed by testing each variable with an interaction for time (Cox models with time-dependent covariates). Sensitivity analyses excluded subjects who received a cancer diagnosis within three years after baseline and yielded virtually identical results for all computations specified above. All reported P values are two-sided.For analysis of the Bruneck cohort, statistical calculations were performed using the SPSS 15.0 and BMDP software packages. Concentrations of OPG and RANKL, and the RANKL/OPG ratio were logFor the UKCTOCS studies and SUCCESS trial, statistical analyses were conducted with the use of R software, version 2.11.1. To compare univariate distributions standard boxplots were utilized with 0.25, 0.5 and 0.75 quantiles standing for box bottom, horizontal line and box top, and samples extremes for whiskers, to test difference between distributions Mann Whitney U test (Wilcoxon test) has been used. To stratify the risk for women with high progesterone Odds ratios and 95% confidence intervals were calculated through the conductance of logistic regression analyses for the categorical values. One tertile (1st for RANKL and 3rd otherwise) was referred as a reference value and Odds Ratios (ORs) of two other tertiles have been calculated with respect to the reference value. These ORs have been considered separately for cases with time to diagnosis more than 1 year and less, and accompanied with the corresponding confidence interval (CI) and P Value of the hypothesis that OR is different from one.


In addition to tumor characteristics and lifestyle factors, cancer relapses are often related to the risk of death but have not been jointly studied. We investigate the prognostic factors of recurrent events and death after a diagnosis of breast cancer and predict individual deaths including a history of recurrences.The E3N  study is a prospective cohort study that was initiated in 1990 to investigate factors associated with the most common types of cancer. Overall survival and three types of recurrent events were considered: locoregional recurrence, metastasis, and second primary breast cancer. Recurrent events and death were analyzed using a joint frailty model.The analysis included 4926 women from the E3N cohort diagnosed with a first primary invasive breast cancer between June 1990 and June 2008; during the follow-up, 1334 cases had a recurrence (median time of follow-up is 7.2 years) and 469 women died. Cases with high grade, large tumor size, axillary nodal involvement, and negative estrogen and progesterone receptors had a higher risk of recurrence or death. Furthermore, smoking increased the risk of relapse. For cases with a medium risk profile in terms of tumor characteristics and lifestyle factors, the probability of dying between 5 and 10 years after diagnosis was 6, 20 and 36% for 0, 1 or 2 recurrences within the first 5 years after diagnosis, respectively.Our study showed the importance of considering baseline lifestyle characteristics and history of relapses to dynamically predict the risk of death in breast cancer cases. Medical experience coupled with an estimate of a patient’s survival probability that considers all available information for this patient would enable physicians to make better informed decisions regarding their actions and thus improve clinical output.The online version of this article (10.1186/s12885-018-4076-4) contains supplementary material, which is available to authorized users. Breast cancer is the most common type of cancer in women worldwide and the leading cause of cancer death in women . BetweenTumor characteristics are the main prognostic factors for breast cancer outcome. A tumor size larger than 2 cm, axillary nodal involvement, negative estrogen and progesterone receptors, and high grade have been shown to increase the risk of death after breast cancer diagnosis and the risk of locoregional recurrence and metastases . Young aLifestyle factors might also influence the risk of recurrence and/or death after breast cancer diagnosis. Excess weight, which is associated with a higher risk of postmenopausal breast cancer, is also associated with a higher risk of breast cancer recurrence and death . SmokingReproductive factors associated with breast cancer risk could influence breast cancer survival: women with a diagnosis of breast cancer before menopause have a greater risk of recurrence and death . Althougt is not affected by the occurrence of an event at a previous time point [Most studies on prognostic factors for breast cancer recurrence were performed using Cox proportional hazard models or competing-risks regression models . In thesme point .To overcome these problems, Mauguen et al.  proposedIn this paper we applied a joint model to study the effects of tumor characteristics, lifestyle characteristics and reproductive factors on the risk of relapse and death after a first invasive breast cancer diagnosis using incident cases within the population-based E3N cohort. More specifically, we studied the dynamic prediction of the risk of death considering baseline lifestyle characteristics and history of recurrent events.http://epic.iarc.fr/index.php.The E3N  study is a prospective cohort study that was initiated in 1990 to investigate factors associated with the most common types of cancer . It inclThe time-to-event was defined as the time between the date of diagnosis of the first invasive breast cancer and the date of each recurrent event or death. Three types of recurrent events were considered: locoregional recurrence, metastasis, and second primary breast cancer. Primary cancers and recurrent events were self-reported to the E3N team by the participants or their next-of-kin or were identified among the causes of death on the death certificate. The information was then validated via pathology reports or any other medical document. We then considered the date of the medical record as the exact date for the first breast cancer diagnosis and for the successive recurrent events. The participants’ vital status was regularly updated through the health insurance plan, postal service, and next-of-kin; causes of death were obtained from the French National Service on Causes of Death. In this work we considered all-cause mortality among breast cancer patients.N = 735) or a metastatic breast cancer at first diagnosis (N = 29) were not included.The study population consisted of 5690 E3N women who were diagnosed with invasive non-metastatic breast cancer confirmed by medical records between June 1990 and June 2008. End of follow-up was defined as the date of death, date of last response to a questionnaire (censored), date of diagnosis of a recurrent event, or June 25, 2008, whichever occurred first. Women with in situ breast cancer  and 1 day before death for the others because the delay between cancer and death was less than 2 years.Recurrent events such as locoregional recurrence, metastasis, or second primary breast cancer may lead to death; conversely, because death prevents any new recurrent events, it is a competing event for recurrences, and these two processes may be correlated. Joint frailty models allow us to study the joint evolution over time of two survival processes by considering all successive recurrent events and the terminal event (death) , 19. ProWe proposed also a dynamic prediction tool to predict the risk of death over a certain period of time, given the history of relapses and the past history in terms of covariates for a patient. The estimated probability can be updated following a new disease relapse. Different scenarios were considered by varying the window of prediction and the number and timing of recurrent events as previously performed in Mauguen et al. . Three dDuring follow-up, among the 4926 cases of the study sample, 549 patients 11.2%) were lost to follow-up, 1334 recurrent events occurred including 343 locoregional recurrences, 603 metastases, and 388 s primary breast cancers; 18% of women suffered at least one recurrent event, 12% suffered exactly one event, 4% suffered two events and 2.1% suffered three or more) (see Table 1.2% wereThe median time between breast cancer and any recurrent event was 7.2 years (IQR: 3.9–11.2 years). The median time between breast cancer diagnosis and the first recurrence was 3.8 years for the first event, 5.9 years for the second event, and 6.8 years for the third event. Locoregional recurrences generally occurred sooner after breast cancer diagnosis  than metastases  or second primary breast cancers . Most events occurring after the first event were metastases . For the women who died, the time between the last non-fatal recurrent event and death decreased with the increasing number of recurrent events. In total, 4.4% of those with no recurrence, 20% of those with one recurrence, 50% of those with two recurrences and more than 70% of those with three or more recurrences died during follow-up.We observed that 13% of the cases were double hormone receptor negative; 40% were poorly differentiated; 19% had a tumor larger than 2 cm and 28% had nodal involvement .Individual dynamic prediction of death was performed on three profiles of risk factors identified on the basis of the previous results: a low, medium, high-risk profile see Fig. . When thThe number of recurrent events greatly affected the risk of death: for the medium risk profile, the probability of death (P1) between 5 and 10 years after cancer diagnosis was 6%  for no recurrence in the first 5 years after diagnosis, 19%  for one recurrence, 36%  for two recurrences and 53%  three previous recurrences; between 5 and 15 years, the risk of death was 13%  if no recurrent events occurred and 36% , 59%  and 76%  if one, two or three events occurred, respectively. The risk of death was also affected by risk factors: after two recurrent events, the risk of death between 5 and 10 years after the cancer diagnosis was 7, 36 and 51% in women with low-, medium-, and high-risk profiles, respectively.To estimate whether the predictions are accurate, error of prediction curves based on the brier score were used. Prediction errors (not presented here) were influenced by the number and timing of relapses. Indeed, with a Cox model, the prediction error of the risk of death between 5 and 10 years was 9%, whereas in the joint frailty model, which accounts for relapse times, the prediction error was 8% with probability P2 and 7% with probability P1.In this article, we studied jointly the prognostic factors for successive relapses and death on a large cohort of women with a primary breast cancer. We proposed a dynamic individual prediction of the risk of death after breast cancer using a history of cancer recurrences, tumor characteristics, lifestyle characteristics and reproductive factors, showing that at a given time after diagnosis, the risk of death was strongly dependent on the number of relapses.To date, relatively few studies have attempted to study this association. Our results confirm the relevance of the association between established prognostic factors, MTH and cigarette smoking and the risk of recurrent events or death after breast cancer. The association between lifetime cigarette smoking and risk of breast cancer recurrence has been previously described into the literature . The tumSmoking exposure after breast cancer diagnosis was associated with an increased risk of relapse but not with the risk of death. This might be explained by the fact that almost half of the smokers stopped smoking after their cancer diagnosis. Obesity, a factor that has been previously associated with the risk of recurrence and death after breast cancer , was notA major limitation of this study is the absence of variables describing the cancer treatments. The year of breast cancer diagnosis, in combination with tumor characteristics, can be viewed as a proxy variable linked to the treatment evolution. The results reflect a lower risk of relapses for those recently diagnosed who are likely using aromatase inhibitors and practicing the technique of sentinel lymph node. However, for patients diagnosed after 1999, the follow-up time is shorter and the probability of observing a relapse is consequently lower.We have illustrated that some specific profiles of breast cancer patients vary according to tumor characteristics, lifestyle characteristics, reproductive factors and the history of relapses; these factors may predict different probabilities of dying in the future. The proposed approach was able to show that the number of relapses greatly affects the predicted risk of death and that better predictions of death are obtained by considering the history of relapses rather than only considering prognostic factors. For example, the “naïve” probability of death that does not consider the history of relapses was overestimated. Using this individual dynamic prediction tool, we illustrated that death probabilities must be updated each time additional relapses are recorded. In our analysis, we did not consider time-dependent covariates, such as modifications in the patient’s treatment, BMI, smoking habits or MHT use. However, similar to how these time-dependent covariates are allowed in the joint modeling, they are allowed in the prediction calculation. Although all of the analyses were performed using a standard statistical R package, a level of expertise in programming may be required. An easy web application as Adjuvant Online  could prIn conclusion, survival outcomes after a breast cancer are affected by the occurrence of relapses, and the proposed approach (with joint model) is really appropriate to both study their link and to predict the prognosis of patients suffering from a primary breast cancer and with possible relapses. These dynamic prediction tools would be valuable in everyday medical practice. That is, medical experience coupled with an estimate of a patient’s survival probability that considers all available information for this patient would enable physicians to make better informed decisions regarding their actions and patient care and thus improve clinical output.Additional file 1:Joint frailty model and prediction (DOCX 27 kb)


Breast cancer comprises several molecular subtypes with different prognoses and possibly different etiology. Reproductive and hormonal factors are associated with breast cancer overall, and with luminal subtypes, but the associations with other subtypes are unclear. We used data from a national screening program to conduct a large nested case-control study.We conducted a nested case-control study on participants in the Norwegian Breast Cancer Screening Program in 2006 − 2014. There was information on estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) for 4748 cases of breast cancer. Breast cancer subtypes were defined as luminal A-like (ER+ PR+ HER2-), luminal B-like (ER+ PR- HER2- or ER+ PR+/PR-HER2+), HER2-positive (ER- PR- HER2+) and triple-negative (ER- PR- HER2-). Conditional logistic regression was used to estimate odds ratios (ORs) of breast cancer associated with age at first birth, number of pregnancies, oral contraceptive use, intrauterine devices and menopausal hormone therapy. Analyses were adjusted for age, body mass index, education, age at menarche, number of pregnancies and menopausal status.p-trend ≤0.02), and although not statistically significant, with HER2-positive  and triple-negative cancer . Women who had ≥4 pregnancies were at >40% lower risk of luminal-like and HER2-positive cancers than women who had never been pregnant. However, there was a larger discrepancy between tumor subtypes with menopausal hormone use. Women who used estrogen and progesterone therapy (EPT) had almost threefold increased risk of luminal A-like cancer  compared to never-users, but were not at elevated risk of HER2-positive  or triple-negative  subtypes.Number of pregnancies was inversely associated with relative risk of luminal-like breast cancers (Reproductive factors were to some extent associated with all subtypes; the strongest trends were with luminal-like subtypes. Hormone therapy use was strongly associated with risk of luminal-like breast cancer, and less so with risk of HER2-positive or triple-negative cancer. There are clearly some, but possibly limited, etiologic differences between subtypes, with the greatest contrast between luminal A-like and triple-negative subtypes.Not applicable. There is substantial evidence for a role of female hormones in the etiology of breast cancer. Reproductive factors, such as early age at menarche, nulliparity, and late age at first birth –3 are alIn earlier studies, subtypes were defined by immunohistochemical analysis (IHC) of the two hormone receptors only, i.e. the estrogen receptor (ER) and the progesterone receptor (PR) –31. It iAnother challenge has been that many studies have not had adequate power to assess the effect of risk factors on all subtypes, which makes it difficult to determine the effect of risk factors on the less common subtypes. The overall published evidence , 47–50 sWe decided to take advantage of a national screening program in Norway to examine potential associations between reproductive and hormonal factors and the various breast cancer subtypes - specifically, luminal A-like, luminal B-like HER2-negative, luminal B-like HER2-positive, HER2-positive, and triple-negative disease - in a large study. We therefore conducted a large nested case-control study within the Norwegian Breast Cancer Screening Program.The Cancer Registry of Norway (CRN) is responsible for the administration of the Norwegian Breast Cancer Screening Program . Women aAll women aged 50–69 years who are included in the National Population Registry are invited to undergo screening as part of the Norwegian Breast Cancer Screening Program every 2 years. The average attendance rate in each round is about 76%.We conducted a nested case-control study within the study cohort. Only women with no history of ductal carcinoma in situ prior to the study start (1 January 2006) and no history of diagnosis of another invasive cancer (except non-melanoma skin cancer) were eligible for the study. The cases were women diagnosed with a first occurrence of invasive breast cancer (ICD10: C50) during the study period, with information on ER, PR, and HER2 status (see subsequent text). For each woman with breast cancer, we randomly selected five controls individually matched to cases by year of birth (+/- 3 years) and year of last screening before breast cancer diagnosis (+/- 3 years). Controls had to be alive and resident in the country at the time of the diagnosis of breast cancer in the matching case. We ended up with 6471 patients with breast cancer (cases) and 32,355 controls. The study was approved by the Regional Committee for Medical and Health Research Ethics in the South-East Health Region of Norway.Information on ER, PR and HER2 status was assessed by IHC and extracted from pathology reports submitted to the CRN. From 2006 to January 2012, tumors were classified as ER-negative if there was <10% reactivity. From February 2012 onwards, the threshold for ER-negative tumors was changed to <1% reactivity as a result of change in the treatment protocols for patients attending clinics in Norway. We used these official thresholds. PR-negative tumors were defined as those with reactivity of <10%, and PR-positive tumors as those with reactivity ≥10%. HER2 expression status was determined at each laboratory with IHC and/or in situ hybridization. Cases with no (0) or weak (1+) immunostaining were defined as HER2-negative, while cases with moderate (2+) or strong immunostaining (3+) were defined as HER2-positive. In situ hybridization (fluorescence (FISH), chromogenic (CISH), or silver (SISH) in situ hybridization methods) was usually used to confirm HER2 status if IHC yielded 2+ results. If IHC was 2+ and FISH, CISH, or SISH were missing, or if IHC was missing but FISH, CISH, or SISH were positive, the tumor was classified as HER2-positive. If IHC was 2+ and FISH, CISH, and SISH were negative, the tumor was regarded as HER2-negative. Data on Ki-67 were recorded by the CRN from late 2011 and therefore, we did not include this marker in our analysis.Our primary exposures of interest were hormonal risk factors including reproductive factors , and other hormonal factors  and menopausal hormone therapy use). Menopausal hormone therapy use included use of estrogen alone , or use of combined estrogen and progestin therapy (EPT). For current exposures we chose the questionnaire at the last screening before breast cancer diagnosis for the cases and the corresponding time for the controls. If the questionnaire or any values were missing, information was used from the previous screening questionnaire. Menopausal status was defined according to whether or not women were still menstruating, or whether they menstruated irregularly. Menopausal age was defined as the age when menstruation ended.We considered age at screening , body mass index (BMI) at screening , education , age at menarche , number of pregnancies lasting at least six months  and menopausal status  as potential confounders and adjusted for these when appropriate. We tried to control more tightly for age at screening using a continuous variable, but the results remained largely the same, and we therefore retained the 5-year categories.n = 532 patients), age at menarche (n = 298 patients), education (n = 66 patients), number of pregnancies (n = 164 patients), and menopausal status (n = 59 patients). This left us with 5352 women with breast cancer (cases) for analysis. Of the 32,355 controls, we excluded controls based on missing information on the following: BMI (n = 3296 controls), age at menarche (n = 1641 controls), education (n = 371 controls), number of pregnancies (n = 779 controls), and menopausal status (n = 336 controls). This left us with 25,932 controls for analysis.Women with missing values for an exposure variable were excluded from the analyses of that variable, while women with missing information on the potential confounding variables listed previously were excluded from all analyses. Of the 6471 patients with breast cancer, we excluded women due to missing information on the following variables: BMI ) as a measure of relative risk associated with various exposures. Trend tests were performed by fitting ordinal values corresponding to exposure categories and testing whether the slope coefficient differed from zero. To test for heterogeneity between breast cancer subtypes we ran case–case analyses, comparing each subtype to the luminal-A-like subtype. We used likelihood ratio tests comparing the likelihood ratio of the case–case model with confounders only to that of the adjusted case–case model with the exposure variable. We considered a two-sided Because we used conditional logistic regression we included all women in the risk estimates  were included in analysis of age at the start of OC, and premenopausal women were included in analysis of age at menopause), but they were not included in the trend test .BMI, age at first birth, education, age at menopause, duration of use of oral contraceptives and intrauterine devices, and menopausal hormone therapy use were positively associated with overall breast cancer risk  whereas age at menarche, number of pregnancies and postmenopausal status were associated with a decreased risk Table .Table 1ANumber of pregnancies was inversely associated with risk of breast cancer overall and risk of several breast cancer subtypes Table . Comparep-trend = 0.07 and 0.01, respectively). The ORs associated with the oldest age at first birth (>30 years) were also similarly elevated, but not significantly so, for HER2-positive  and triple-negative  subtypes . However, the test for heterogeneity comparing each subtype to luminal A-like breast cancer was not statistically significant for age at first birth.Later age at first birth was associated with an increased risk of breast cancer overall and with several breast cancer subtypes Table . Comparep-trend = 0.14) (Table p-trend = 0.01) and HER2-positive  subtypes  Table . Postmenp-heterogeneity with luminal A-like breast cancer = 0.009)  Table .Table 3Ap-heterogeneity = 0.006). Previous studies have suggested that the effect of menopausal hormone therapy may be modified by BMI, with stronger risk estimates in lean than in obese women. We therefore examined the effect of duration of EPT in different BMI strata. For breast cancer overall, the ORs associated with longer duration of EPT was significantly higher in lean (BMI <25) than in obese (BMI ≥25) women (p for interaction = 0.001)  of breast cancer overall (Table 1) Table . We obse1) Table .Table 4AWhen we added grade to the luminal A-like and both luminal B-like definitions, the results changed slightly, but were largely the same . The larn = 1813) were diagnosed within a month of completing the questionnaire. Because these women could have been symptomatic when they completed their questionnaire, we ran a sensitivity analysis excluding these women. However, this did not affect the results (results not shown).Among the patients (cases), 38% . Some studies have added additional markers, to better define the subset that is basal-like, by HER1+ and/or cytokeratin 5/6+ , 49, 56.Our observation of an inverse association between parity and luminal A-like breast cancer is consistent with the vast majority of studies as summarized in the recent systematic review of 38 studies  and the Although not statistically significant, we found that parity was associated with a decreased risk of HER2-positive breast cancer. This is consistent with the findings in the Nurses’ Health Study , but incHowever, although not statistically significant, we found that parity was associated with a decreased risk of triple-negative breast cancer.n = 34) or had four or more children (n = 36). More notably, our study consisted of older women (aged 50–69 years). Although we did not have information on time since the last pregnancy, we can only speculate that few women in our study had had a recent pregnancy, a factor associated with increased risk of triple-negative cancer [This is inconsistent with several other studies in which parity was associated with no risk , or was e cancer . PerhapsWe found that age at first birth (26 years or older) was associated with an increased risk of luminal-like breast cancer. This is consistent with a systematic review and  and two We found no protective effect of breastfeeding on breast cancer occurrence overall or for any subtype. This is inconsistent with the systematic review that reported that breastfeeding is associated with decreased risk of luminal A-like, luminal B-like and triple-negative subtypes .This mayFor long duration of oral contraceptive use (>10 years), we observed a slight increase in risk of all the subtypes except for the luminal B-like HER2-positive and HER2-positive subtypes. Our positive association between duration of oral contraceptive use and triple-negative breast cancer is consistent with the systematic review . HoweverWe observed a large increased risk of luminal A-like and a moderate increased risk of luminal B-like breast cancer with use of menopausal hormone therapy, albeit only significantly so for the luminal A-like subtype. This is consistent with a systematic review  and the We hypothesized that EPT would have a stronger effect on thin women than on heavier women, and our results suggested modification of the effect of the duration of EPT when we analyzed leaner women (BMI <25) and heavier women (BMI >25) with the luminal-like subtypes of breast cancer. This is consistent with a previous population-based case–control study of women aged 55–72 years .Pregnancies have been reported to ultimately reduce plasma estrogen   and follOur results, together with those from other large studies, further suggest that the associations between these hormonal-related factors  and risk of the luminal B-like subtypes are similar to the associations between these factors and risk of the luminal A-like subtype. However, although the association with luminal A was the strongest, it is clear that EPT also increases the risk of breast cancer with bad prognosis .We observed some intriguing associations with HER2-positive breast cancer. The effect of pregnancies and age at first birth were similar for the HER2-positive subtype and luminal cancer. Also, age at menopause was strongly associated with increased risk only of the HER2-positive subtype. Further, we observed a positive trend of association between age at menopause and the luminal B-like HER2-positive subtype. These results might imply that there are hormonal mechanisms involved in the expression of the HER2 protein. On the other hand, exogenous hormone use (EPT) was not associated with this subtype, suggesting that perhaps only some hormonal mechanisms play a role in HER2-positive subtypes. The contrasting results between the luminal-like subtypes and triple-negative breast cancer are also consistent with previous literature. Hormonal factors have a stronger effect on ER+ PR+ tumors, which suggests that the etiology of triple-negative cancer is different from that of the luminal subtypes. Specifically, this suggests that triple-negative tumors may not be as easily prevented hormonally.In summary, the strongest discrepancy across subtypes was for the use of combined hormone therapy, where the effect was clearly much stronger for luminal A-like than for other cancers. Otherwise, the associations with hormonal risk factors were stronger for luminal A and B-like subtypes than for HER2-positive and triple-negative subtypes. Our results suggest that reproductive factors may to some extent be associated with HER2-positive tumors, but that triple-negative tumors have a different etiology.Strengths of this study include its population-based design, the large size, being the largest single study of breast cancer subtypes conducted so far, and the availability of prospectively collected detailed information on many risk factors for breast cancer.n = 1813), and could have been symptomatic when they completed their questionnaire. However, exclusion of women who were diagnosed within a month of completing the questionnaire did not affect the results (results not shown).Although the study is the largest to date, there was still limited power for analysis of the rare breast cancer subtypes. Another weakness was the lack of molecular expression data. This may have obscured differences between the subtypes. Another possible weakness was that a subset of women were diagnosed within a month of completing the questionnaire (Women who attend screening might be more health-conscious and have a healthier lifestyle than women who do not attend. This could have contributed to obliteration of protective effects of “healthy” habits, such as an effect of breastfeeding overall. At the same time, women who attend screening are more likely to have breast cancer detected. Thus, the picture becomes complicated with these potential biases, and it is not clear how this would explain the results of this paper. The associations between well-established risk factors and overall risk of breast cancer were largely as expected. Furthermore, it is unlikely that any such bias would have differentially affected the results for different subtypes.Reproductive factors and menopausal hormone therapy use were more strongly associated with luminal-like breast cancer, but reproductive factors were also associated with HER2-positive and triple-negative breast cancer. The differences between subtypes were greatest for menopausal hormone use. Our results add to the literature showing that there are etiologic differences between luminal breast cancer subtypes and basal-like or triple-negative breast cancer subtypes, but suggest that the differences may be limited.