Breast cancer survivors (BCS) are at high risk for the development of obesity, type 2 diabetes mellitus, and metabolic syndrome. There is increasing interest in the association between depression and metabolic dysfunction, which is relevant in this population as depression is often present in the chronic phase of cancer recovery. Thus, the aim of this study was to evaluate metabolic risk in BCS with and without depression compared to non-cancer controls.2) to non-cancer controls (N = 28). Center for Epidemiologic Studies Depression Scale (CES-D) >16 or antidepressant medication usage was used to classify depression. Metabolic status was defined by 2-hr glucose during an OGTT and classification of metabolic syndrome.African American (46 %) and Caucasian (54 %) postmenopausal BCS  were matched for race, age (±2 years), and BMI (P’s < 0.05). Conversely, HDL-C was 16 % higher in BCS (P < 0.05). Forty three % of BCS were on antidepressants compared to 14 % in non-cancer controls, despite similar mean CES-D scores (6 ± 1). Depressed BCS (46 %) had a higher BMI, waist circumference, fasting glucose, and more metabolic syndrome components than non-depressed BCS (P’s < 0.05).BCS have a heightened prevalence of depression that may be associated with an increased prevalence of metabolic syndrome. These results support the need to monitor weight gain, depression, and the progression of metabolic abnormalities after cancer diagnosis and treatment. Further studies into the mechanistic link between depression and metabolic disease are necessary to identify strategies that can offset their impact on obesity and associated cardiovascular risk following a breast cancer diagnosis. Due to better breast cancer screening modalities and cancer treatment options , the numDepression may affect the ability to make positive lifestyle changes and comply with medical therapy , while aThe tendency for weight gain with aging places postmenopausal women at increased risk for metabolic dysfunction . In addi2). All women reported being sedentary  and weight stable (<2 kg weight change) over the prior six months. Women without a history of breast cancer were selected from a larger subset of individuals from a previously published manuscript [This cohort study was observational in nature. Groups were comprised of 28 postmenopausal BCS and 28 postmenopausal women without prior breast cancer diagnosis matched for race, age (±2 years), and BMI , or a plateau in VO2 (<200 ml/min change). If such criteria were met, the highest level of VO2 was defined as VO2max.VOescribed . VO2max Risk for depression was measured with the Center for Epidemiologic Studies Depression Scale (CES-D). The CES-D is composed of 20 items and assesses risk for depression in the general population. A cut off of 16 was used to indicate “mild” depressive symptomatology . Subject2) and waist to hip ratio (WHR) calculated. Blood pressure was measured on the non-affected cancer side for BCS and on the right side in the non-cancer controls after 10 min of resting on three occasions and averaged.Height, body weight, and waist and hip circumferences were measured using standardized protocols , and BMIBlood was collected after a 12 h fast. Plasma triglyceride and cholesterol levels were analyzed using enzymatic methods , HDL-C measured in the supernatant after precipitation with dextran sulfate, and low-density lipoprotein cholesterol (LDL-C) calculated as LDL-C = total cholesterol- HDL-C -TG/5 . A oral 2 tests. Pearson correlation coefficients were calculated to determine relationships between variables. All tests were two-tailed, and P < 0.05 were considered statistically significant.Data were analyzed using SPSS Version 20. Data were analyzed for normality using the Kolmogorov–Smirnov test. Mean ± SEM were calculated for continuous variables and compared using Student’s t test after log transformation of variables (plasma triglycerides), as appropriate. Percentages were calculated for categorical variables  and compared using χ2max was 25 % higher in BCS (P < 0.05) (Table The women were 46 % African American and 54 % Caucasian. BCS were an average of 94 ± 18 (range 9–384) months since diagnosis. Treatment was lumpectomy in 54 % and mastectomy in 57 %. Forty-three percent underwent chemotherapy alone, 46 % radiation therapy alone, and 11 % received both. At study entry, 25 % of BCS were taking an aromatase inhibitor, 12 % tamoxifen, and 63 % were not receiving hormone therapy. BCS were well matched to non-cancer controls with regard to age, BMI, waist circumference, and WHR, but VOOn average, BCS had slightly elevated fasting glucose, with 9 % higher fasting glucose than non-cancer controls (P < 0.05). BCS also had higher HDL cholesterol (P < 0.05), tended to have higher total cholesterol (P = 0.08), and 39 % more required a lipid lowering medication (P < 0.01). Diastolic and systolic blood pressure and hypertension medication usage was similar between groups. BCS were 2.5 times more likely to have metabolic syndrome , with blood pressure (64 %) and waist circumference (61 %) being the most prevalent component in BCS.CES-D scores >16 were observed in ~14 % of non-cancer control and BCS subjects. Despite similar mean CES-D scores, three times more BCS were being treated with an antidepressant medication . Twenty-six % of non-cancer controls and 46 % of BCS were classified as depressed (CES-D >16 and/or on an antidepressant) (P = NS).As anticipated, BCS classified with depression had higher mean CES-D scores than BCS without depression . Despite similar age, racial profiles, fitness levels, and time from cancer diagnosis (data not shown), BCS with depression also had a higher BMI, waist circumference, fasting glucose, and more metabolic syndrome components  axis, which is involved in the pathogenesis of central adiposity and metabolic syndrome . Greater2max was observed in BCS than non-cancer controls. Despite this finding, many studies suggest that depressive symptoms and metabolic dysfunction improve with exercise in BCS [Often the failure to recover after breast cancer treatment  is attre in BCS –46.This study is limited by a small sample size, which prevented stratification by breast cancer treatments and the control of length of time post-cancer treatment. As there is evidence that breast cancer treatments, including chemical variations in drugs used for chemotherapy, may have varying effects on metabolism , future In summary, our results support the need to monitor weight gain, depression, and the progression of metabolic abnormalities during treatment and longitudinally in BCS, as their development may affect long-term survivorship. Further studies into the mechanistic links between depression and metabolism are necessary to identify strategies that can offset their impact on survivorship following a breast cancer diagnosis.


Our study examines the impact of breast cancer-related lymphedema on women’s work and career. Our research addresses a significant knowledge gap regarding the additional impact of lymphedema on breast cancer survivors.An online national survey was conducted with 361 women who either had breast cancer without lymphedema  or breast cancer with lymphedema . Participant recruitment was supported by the Breast Cancer Network Australia and the Australasian Lymphology Association.Both breast cancer and lymphedema had a significant negative influence on women’s work and career. Respondents reported changes in employment resulting from stress and/or physical impairment, which affected attendance and work performance. The perceived negative impact of breast cancer on respondents’ work and career was noticeably greater in Group 2 (63 %) than Group 1 (51 %) (p = 0.03). Of the participants who were in paid employment at some time (either at diagnosis of lymphedema or at the time of the survey (n = 103), 43 (42 %) indicated that lymphedema impacted their work performance. The impact of lymphedema on work was incremental with increased severity of lymphedema (range 22–75 %). The annual number of days off work for subclinical/mild lymphedema participants was 1.4 versus 8.1 days for moderate or severe participants (p = 0.003).This study identifies an additional detrimental effect of lymphedema on women’s work and career over and above the initial impact of breast cancer and provides empirical evidence for future prospective studies and policy improvement. As the number of breast cancer survivors increases with better treatments, the number of patients with long-term side-effects including fatigue, cognitive problems, sexual dysfunction and fear of recurrence is also growing  managed to return to work but the rate steadily increased as the period of time after cancer treatment increased. This ranged from, on an average, 40 % at 6 months post diagnosis to 62 % at 12 months, 73 % at 18 months, and to 89 % at 24 months after cancer diagnosis  and then breast cancer for all patients.Individuals eligible for participation were: female; over 18 years of age; previously diagnosed with primary stage I, II or III breast cancer who had completed treatment at least 1 year prior to recruitment and fluent in English. Individuals who fulfilled these criteria only became the control group. In addition, we targeted individuals who fulfilled all the criteria above, but with a confirmed diagnosis of lymphedema, either by a doctor or lymphedema therapist, including participants with subclinical lymphedema diagnosed with bioimpedance spectroscopy (L-Dex) alone; who had sought therapist advice; and/or were wearing compression garments. Participants completed the study questionnaire online.Women previously diagnosed with breast cancer were approached for study participation through an Australian community-based breast cancer consumer organization, the Breast Cancer Network of Australia (BCNA). An e-mail invitation was sent by a contact person within the BCNA to members who had previously agreed to receive notifications about research studies. Participants with lymphedema were also asked to consider the study through the Australasian Lymphology Association (ALA) and by notices in the clinics of authors . It was the responsibility of the women who received the e-mail to determine their eligibility for the study. A total of 361 women agreed to participate. Following online consent, participants anonymously completed the questionnaire that took approximately 30 min to complete. The conduct of this research was approved by the Macquarie University Human Research Ethics Committee.No problem: no noticeable swelling. We later termed this category as sub-clinical lymphedema detected by a therapist or clinician using girth measures or bioimpedance spectroscopy (L-Dex).Mild lymphedema: soft swelling that is not obvious to others and comes and goes.Moderate lymphedema: swelling with occasional hardness in some areas that is obvious to others and is always present.Severe lymphedema: profuse swelling with thickened skin, constant hardness, and a very large, heavy arm that is extremely obvious to others and is always present.We asked a screening question in order to classify our respondents’ lymphoedema stage. We asked them to reflect on their condition for the last month and first, report on its severity by choosing one of the following categories.p values are two-sided using the two-sample t test, unless otherwise specified. This paper will focus on the impact of lymphedema over and above breast cancer on work and career.Participants with breast cancer were asked specific questions about how their cancer affected the following domains: (1) Work/career; (2) Family Life (3) Social/Leisure (4) Self Image and (5) Feeling about Self. For participants given a diagnosis of lymphedema, in addition to the above domains, data was also collected on the impact of lymphedema on employment, cost of seeing therapists and the cost of compression sleeves. Data collection occurred between November 2014 and March 2015 using Qualtrics. All Of 361 participants, 209 (58 %) had breast cancer (BC) (Group 1) and 152 (42 %) had a diagnosis of BC and lymphedema (BC + LE) (Group 2). The severity of lymphedema was “not noticeable” in 14 (9 %), mild in 77 (51 %), moderate in 55 (36 %) and severe in six participants (4 %). Ninety-two of 209 (44 %) BC participants were aged under 55 compared to 54 of 152 (34 %) of BC + LE participants (p = 0.105). The duration since completion of all breast cancer treatment was <5 years for 75 % of the BC group and 56 % for the BC + LE group (p < 0.001). The time since the onset of lymphedema was <5 years in 65 % of the BC + LE group. Other demographic features of the study participants are shown in Table Both breast cancer and lymphedema had a significant impact on a person’s ability to work. Breast cancer had an impact on the ability to work in 51 % of participants in Group 1 but participants with lymphedema perceived their breast cancer diagnosis to have had a greater impact on their work (63 %) (p = 0.03)  did not disclose their diagnosis of lymphedema to their supervisor and 40 (38.8 %) did not disclose their diagnosis of lymphoedema to their coworkers (p = 0.147). Table Lymphedema is a feared complication of breast cancer and impacts physical, functional, psychological and social well-being of participants after breast cancer treatment. Yet, existing scholarship is in early stages of development regarding many aspects of this condition. Our first in-depth cross-sectional study, shows that, when compared to breast cancer survivors without lymphedema, individuals living with lymphoedema are worse off in terms of work and career.In a previous meta-analysis of the impact of a diagnosis of cancer on work, a non-supportive work environment, manual work, cancer types associated with an unfavourable prognosis, the presence of fatigue and physical symptoms, and perceived employer discrimination because of cancer and treatment were reported as barriers for returning to work Mehnert . Our stuFew studies, however have examined the impact of lymphedema over and above the diagnosis of breast cancer. For example, Johansson et al.  exploredIn a study of 67 participants with perceived lymphedema who were working, Mehnert  found thGartner et al.  also exaFinally, Fantoni et al.  studied Our study has limitations particularly as it used a cross-sectional rather than a longitudinal survey design. In addition, as we asked participants to self-report on the impact of lymphedema over and above breast cancer, the study could be characterised by recall bias. However, a cross-sectional design and the use of an online survey allowed for a good sample size, and indicators of lymphoedema status were included, such as number of symptoms, time since diagnosis and we included only participants who had seen a lymphedema therapist. We did not examine education level and subsequent impact on work but previous studies have found that cancer survivors were likely to be unemployed if they did not complete high-school, were previously receiving social security benefits and women were 23 % less likely to find a job after they received employment assistance and support, such as job-hunting services or on-the-job training than men (Chan et al. These limitations notwithstanding, this is the largest study showing the impact of lymphedema over and above breast cancer, and shows how its impact worsens as the condition progresses. The issues are complicated and impact on work from an illness does depend on multiple competing factors. To adequately review these factors, we are planning a prospective study to further differentiate the impact on work of lymphedema versus the impact of breast cancer and its short-term treatments.The findings from this study have implications for clinical practice, future research and for policy makers. Health professionals involved in the care of women with lymphedema need to be aware that these women are at risk of not only experiencing psychological distress and body image disturbance (Alcorso et al.


Use of menopausal hormonal therapy (MHT)-containing estrogen and a synthetic progestin is associated with an increased risk of breast cancer. It is unclear if progesterone in combination with estrogen carries a lower risk of breast cancer. Limited data suggest differences between progesterone and progestins on cardiovascular risk factors, including cholesterol and glucose metabolism. Whether this translates to differences in cardiovascular outcomes is uncertain. We conducted a systematic review and meta-analysis to synthesize the existing evidence about the effect of progesterone in comparison to synthetic progestins, each in combination with estrogens, on the risk of breast cancer and cardiovascular events.We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus through 17 May 2016 for studies that enrolled postmenopausal women using progesterone vs. synthetic progestins and reported the outcomes of interest. Study selection and data extraction were performed by two independent reviewers. Meta-analysis was conducted using the random effects model.We included two cohort studies and one population-based case-control study out of 3410 citations identified by the search. The included studies enrolled 86,881 postmenopausal women with mean age of 59 years and follow-up range from 3 to 20 years. The overall risk of bias of the included cohort studies in the meta-analysis was moderate. There was no data on cardiovascular events. Progesterone was associated with lower breast cancer risk compared to synthetic progestins when each is given in combination with estrogen, relative risk 0.67; 95 % confidence interval 0.55–0.81.Observational studies suggest that in menopausal women, estrogen and progesterone use may be associated with lower breast cancer risk compared to synthetic progestin.The online version of this article (doi:10.1186/s13643-016-0294-5) contains supplementary material, which is available to authorized users. Menopausal hormone therapy (MHT) is highly effective for the treatment of symptoms related to menopause . MHT regMicronized progesterone is a bioidentical hormone with a molecular structure identical to that of endogenous progesterone produced by the ovary. Synthetic progestins have a different chemical structure from progesterone. These compounds mimic some of the effects of progesterone but may have different actions on progesterone receptors . SynthetAlthough some data suggest that MHT increases the risk of breast cancer , the risProgesterone and synthetic progestins also demonstrate varied effects on lipids, coagulation factors, glucose, and insulin and may therefore differentially impact cardiovascular risk, though data are sparse .The PEPI trial previously demonstrated that, when combined with CEE, progesterone, unlike MPA, did not negate the positive effects of CEE on high-density lipoprotein cholesterol (HDL-C) . A recenA predefined protocol was developed by experts from the Endocrine Society to conduct this systematic review. The protocol included explicit criteria for study selection and plans for the data extraction and analysis. We followed the standards set in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)  statemenWe included comparative/controlled studies that enrolled women aged 45–59 years who were within 10 years of menopause and received MHT. The studies had to compare estrogen with progesterone  with any of the synthetic progestins in combination with estrogen and report outcomes of interest for a follow-up period ≥6 months. The outcomes of interest were the risk of breast cancer and cardiovascular disease. We excluded non-comparative studies, case series, and non-original papers.The search included the electronic databases of MEDLINE, MEDLINE in-process and other non-indexed citations, EMBASE, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, and Scopus. We expanded the search to include all languages, with the latest date of inclusion to be 17 May 2016. The database search was conducted by an experienced Mayo Clinic reference librarian. Controlled vocabulary supplemented with keywords was used to search for comparative studies of progesterone vs. synthetic progestins and risk of breast cancer and cardiac events. A manual search for the included studies’ bibliographies and previous relevant systematic review were also conducted. A detailed search strategy is described in the Additional file k level) was 0.7 and 0.8 for abstract screening and full-text screening, respectively. Disagreements were harmonized by consensus and, if not possible, by consensus through arbitration by a third reviewer.Using an online reference management system DistillerSR , abstracts and titles that resulted from the electronic search strategy were independently evaluated by two reviewers for potential eligibility, and the full-text versions of all potentially eligible studies were obtained. Two reviewers working independently considered the full-text reports for eligibility. The level of agreement between the two reviewers , two reviewers independently extracted data from each study and later reconciled differences, if present. Reviewers determined the methodological quality of studies and collected descriptive, methodological, and outcome data. We extracted the following variables from the studies: study characteristics , baseline characteristics, and patient demographics, and outcome data.We used a modified Newcastle-Ottawa Scale (NOS)  to appraI2 statistic was used to assess heterogeneity of the treatment effect among studies for each outcome. I2 value >50 % and p value <0.10 of the Cochrane Q test suggested substantial heterogeneity that is due to real differences in study populations, protocols, interventions, and/or outcomes. Publication bias was not assessed due to the small number of the studies included. The statistical analyses using DerSimonian and Laird random effects model were performed with CMA version 2 .We extracted or calculated the relative risk (RR) of the outcomes of interest with 95 % confidence interval (CI). The The initial search resulted in 3410 citations. After screening the abstracts, this was limited to 46 potentially relevant articles. These were reviewed in full text by two authors and eventually two cohort studies and one population-based case-control study were included with 44 being excluded for the reasons shown in Fig. I2 = 42 % with p value of <0.0001). The quality of evidence was low due to the observational nature of the study design, and Fig. p value of <0.0001).Based on the meta-analysis of the two included cohort studies, progesterone was found to be associated with lower breast cancer risk compared to synthetic progestins in combination with estrogen  The effect of combined estradiol and progesterone in comparison with estradiol and synthetic progestin on breast cancer incidence showed a RR 0.42 (95 % CI 0.13–1.31) in Espie , whereasThe study by Espie  reportedWe also identified a population-based case-control study that reported similar results. The study showed no significant increased risk of breast cancer among women treated with progesterone in combination with estrogen, odds ratio (OR) 0.80 (CI 95 % 0.44–1.43). However, there was a non-significantly increased risk of breast cancer among users of estrogen with synthetic progestins, OR 1.57 (95 % CI 0.99–2.49) .Based on this systematic review and meta-analysis, progesterone may be associated with lower breast cancer risk compared to synthetic progestins, when each is given in combination with estrogen. No studies have been found reporting on the risk of cardiovascular disease in postmenopausal woman receiving estrogen with progesterone vs. those who are receiving estrogen with synthetic progestins, and no previous systematic reviews have evaluated this question.Progestin is utilized in MHT regimens for women with an intact uterus to prevent endometrial hyperplasia. Progestins used for MHT regimens can be administered orally, transdermally (patch containing norethisterone/norethindrone), or directly to the endometrium (levonorgestrel intrauterine system). While the levonorgestrel intrauterine system has been shown to be adequate for endometrial protection , it is nThe potential role of progestins in increasing breast cancer risk associated with MHT has come under greater scrutiny after the Women’s Health Initiative trial suggested increased risk of breast cancer with continuous use of CEE and MPA for greater than 5 years compared with CEE alone, which showed no increased risk . In factBoth progesterone and synthetic progestins and the dosing regimen may impact breast cancer risk. In the E3N cohort study, MHT regimens containing estrogen and progesterone or dydrogesterone were not associated with a statistically significant increase in breast cancer risk. All other progestins were associated with an increased risk, with no difference between various progestins . The resProgesterone and synthetic progestins may be administered continuously with estrogen or sequentially for 10–14 days per month. Some, but not all, studies comparing these regimens have shown increased risk of breast cancer with continuous dosing compared to sequential dosing –23. RandAccumulating evidence suggests that important differences in risks and benefits exist between various MHT regimens, making individualization of MHT essential. Women with an intact uterus require the use of progesterone for endometrial protection when using systemic estrogen therapy for the management of menopausal symptoms. While an additional study is needed to confirm these results, data suggest lower risk of breast cancer with progesterone and dydrogesterone and do not support a class effect of progestins on breast cancer risk . More stThe strength of our review relates to following a predefined protocol, rigorous database search, and duplicate study selection and data extraction. The main limitations are the observational nature of the evidence, which lowers the confidence in the estimates, and the small number of studies included. We were unable to ascertain the presence and impact of publication bias due to the small number of studies. In terms of the individual studies, the major strength of the Fournier study  was the Observational studies suggest that in menopausal women taking estrogen, progesterone use may be associated with lower breast cancer risk compared to synthetic progestin.MHT, menopausal hormone therapy; CEE, conjugated equine estrogens; MPA, medroxyprogesterone acetate; HDL-C, high-density lipoprotein cholesterol; CI, confidence interval; RR, relative risk; OR, odds ratio; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analysis; NOS, Newcastle-Ottawa Scale; GRADE, Grading of Recommendations Assessment, Development and Evaluation


In a recent case–control study, long-term use of calcium channel blocking drugs was associated with a greater-than-twofold increased breast cancer risk. If prospectively collected data confirm that calcium channel blocker use increases breast cancer risk, this would have major implications for hypertension treatment. The objective of this study was to determine whether women using calcium channel blockers for 10 years or more were at increased risk of developing breast cancer compared with women not using calcium channel blockers.The Sister Study is a prospective volunteer cohort study of women from the USA and Puerto Rico designed to evaluate environmental and genetic risk factors for breast cancer. Beginning in 2003, women between the ages of 35 and 74 were recruited. They were eligible to participate if they had a sister with breast cancer but had not been diagnosed with breast cancer themselves. In total, 50,884 women enrolled in the cohort between 2003 and 2009; 50,757 women with relevant baseline data and available follow-up data are included in this study. The exposure of interest is current use of calcium channel blocking drugs and the reported duration of use at entry into the cohort. Secondary exposures of interest were the duration and frequency of use for all other subclasses of antihypertensive drugs. Our main outcome is a self-reported diagnosis of breast cancer during the study follow-up period. With patient permission, self-reported diagnoses were confirmed using medical records.Results showed 15,817 participants were currently using an antihypertensive drug, and 3316 women were currently using a calcium channel blocker at study baseline; 1965 women reported a breast cancer diagnosis during study follow-up. Using Cox proportional hazards modeling, we found no increased risk of breast cancer among women who had been using calcium channel blockers for 10 years or more compared with never users of calcium channel blockers .We saw no evidence of increased risk of breast cancer from 10 years or more of current calcium channel blocker use. Our results do not support avoiding calcium channel blocking drugs in order to reduce breast cancer risk.The online version of this article (doi:10.1186/s13058-016-0720-6) contains supplementary material, which is available to authorized users. Antihypertensive drugs constitute the most commonly prescribed drug class in the USA. In 2010, Americans filled 678.2 million antihypertensive drug prescriptions [A recent case–control study of women aged 55–74 reported a twofold increased risk of breast cancer for women who were current calcium channel blocker users with at least 10 years of use –5 or witContemporary prospective cohort studies with many long-term calcium channel blocker users are needed to determine whether breast cancer risk is associated with long-term use of these drugs. Although two recent cohort studies with large sample sizes did not detect increased risk of breast cancer among users of calcium channel blockers , 13, stuThe Sister Study is a prospective cohort study of 50,884 women who had a sister with breast cancer and thus are at increased risk of developing breast cancer themselves. This study was designed specifically to capture possible interactions between environmental and lifestyle risk factors such as antihypertensive drug use, and the genetic risk factors underlying familial breast cancer risk. These data were collected in a time period when long-term antihypertensive use was increasingly common among women aged 50 and older, and therefore provide a unique opportunity to evaluate the effect of long-term calcium channel blocker use on breast cancer risk in a higher-risk population of women.The Sister Study is a volunteer cohort of women recruited through media and community outreach across the USA and Puerto Rico. Women were eligible to participate if they had a sister with breast cancer and had never been diagnosed with breast cancer themselves. Between 2003 and 2009, the cohort enrolled 50,884 women and participants completed in-person physical examinations and baseline telephone interviews detailing medical, lifestyle, dietary, and reproductive history. Participants are followed for incident breast cancer diagnoses with annual health updates supplemented with detailed biennial and triennial questionnaires with follow-up response rates ≥94 % , 16. TheN = 3) are not included as cases in this analysis. Of the 77 % of cases (N = 1638) who had records available at the time of analysis, there was close agreement between self-report and the medical record for the diagnosis of breast cancer (99.5 %), classification of an invasive cancer (98.5 %), and ER+ status (99 %), providing a high degree of confidence in the use of self-report for those women with missing medical record data. All available breast cancer diagnoses collected during or prior to the last completed follow-up phase as of 26 April 2013 were included.Sister Study participants are surveyed for development of breast cancer annually, and are asked to report a new breast cancer diagnosis between follow-ups. Reported cases are confirmed with medical record retrieval. Self-reports and medical records were also used to compile and confirm information on tumor histology and hormone receptor status. At the time of analysis (Data Release 2.0), approximately 91 % of women with breast cancer consented to retrieval of their medical records. All women who reported a breast cancer diagnosis had an opportunity to disavow their report; women who disavowed their initial report of a breast cancer diagnosis . Products that contained more than one active ingredient were assigned multiple class codes.We examined the effect of duration and class of antihypertensive drug use on breast cancer risk. We included use of antihypertensive drugs for all disease indications and reported use without a specific indication listed. Covariates were obtained from the baseline Sister Study questionnaires or measured during examiner at-home visits; these included age, race/ethnicity, underlying medical conditions and medical history, family medical history, history of benign breast conditions, smoking status, alcohol consumption habits, level of reported physical activity, body mass index (BMI) from examiner-measured height and weight, age at menarche, menopausal status, hormonal exposures including hormone replacement therapy and oral contraceptives, selected dietary factors, and history of pregnancy and breastfeeding. Baseline menopause status was determined by a women’s self-reported age at last menstrual period (LMP); a women was considered postmenopausal if 12 months or more had passed since her LMP at baseline, or if her last LMP was determined to have occurred pre baseline based on subsequent reports during study follow-up. Women with hysterectomy or uterine surgeries were considered postmenopausal when they reached the age of 56.We characterized use of calcium channel blockers and other antihypertensive drugs in the cohort using standard contingency table methods with chi-square tests. Univariate and multivariate logistic regression analyses were used to identify potential covariates associated with different levels of current and lifetime antihypertensive use across drug subclasses. All statistical analyses were conducted using SAS 9.3 .To determine the association between calcium channel blocker use and breast cancer risk, we calculated hazard ratios (HRs) for strata of lifetime use and incident breast cancer using multivariate Cox proportional hazard regression. Calcium channel blocker use was categorized into five levels: (1) never used calcium channel blocker; (2) former user of calcium channel blocker; (3) current user with less than 5 years of use; (4) current user with 5–10 years of use; and (5) current user with 10 years or more of use. The same stratifications were used for use of any type of antihypertensive drug, as well as for the following classes of antihypertensive drugs: beta-blockers, angiotensin-converting enzyme (ACE) inhibitors, diuretics, angiotensin receptor blockers, and miscellaneous drugs. Calcium channel blockers were also stratified into dihydropyridines  and nondihydropyridines . Women who switched from one antihypertensive drug subclass to another subclass before the baseline survey were considered former users for their previous drug subclass, current users for their current subclass, and current users of antihypertensives in general.N = 234), or with adjustment for prophylactic tamoxifen and reloxifene use, but these factors had no material effect on our risk estimates and were excluded from the final model.Attained age serves as the time scale for the Cox proportional hazards regression models. Participants enter the analysis with their age at study enrollment, and accrue person-time until they either exit at the age of breast cancer diagnosis for cases or the age at their last completed health questionnaire. Participants who failed to respond to their most recent eligible health update were censored at the earliest date among death or the midpoint of the interval between the last completed health update and the end of the window of eligibility for responding to their first missed health update. We ran crude unadjusted Cox proportional hazards regression models as well as a multivariable model with adjustment for the following variables selected as a-priori potential confounders based on existing literature: baseline-measured BMI, race/ethnicity, smoking status, parity, menopause status, age at menarche, history of breast conditions, current drinking level, hormone replacement therapy use, and statin use. We also ran models excluding women who reported prophylactic mastectomy at baseline . A larger proportion of calcium channel blocker users were black (21 %) compared with never users . Calcium channel blocker users had higher BMIs than nonusers (p < 0.0001) and were more likely to have used hormone replacement therapy (p < 0.0001), both of which are established risk factors for postmenopausal breast cancer. Demographics for users of all classes of antihypertensive drugs compared with nonusers were very similar to calcium channel blocker use alone  reported ever taking an antihypertensive drug in their lifetime; and 15,817 (31.2 %) were currently using an antihypertensive drug, 3316 of whom were currently using a calcium channel blocker at study baseline. The majority of current antihypertensive users (90.1 %) were postmenopausal at baseline. Demographic characteristics of women who had and had not taken calcium channel blockers are presented in Table Of the 3844 women in the cohort who reported current use of calcium channel blockers Table , 820 hadA total of 1965 women reported a breast cancer diagnosis during study follow-up: 1372 cases of invasive breast cancer, and 558 cases that were classified as in situ, and 35 cases in which invasiveness of the tumor was unknown. The mean follow-up time for participants in the cohort was 5.3 years (SD 1.69). Of invasive cancers, 1027 were classified as invasive ductal carcinomas and 130 were invasive lobular carcinomas. Of the 558 in-situ cases, 459 were characterized as ductal carcinoma in situ (DCIS).Calcium channel blocker use patterns and use of antihypertensive drugs in general did not differ between women who developed breast cancer during follow-up and those who remained free of breast cancer Table . Of womeCox proportional hazards regression showed no evidence of increased breast cancer risk with long-term calcium channel blocker use either in unadjusted models , women aged over 55, or stratification by ethnicity revealed no increased risk for calcium channel blocker use , for invasive lobular carcinoma (N = 130 cases), or for ductal carcinoma in situ (N = 459 cases), which were the most common types of incident breast cancers reported during follow-up (Table N = 8057) or with at least 10 years of use (N = 3879), no increased risk of breast cancer was observed with calcium channel blocker use compared with users of the other classes of antihypertensive drugs . The small number of long-term users of the calcium channel blocker subclass resulted in wide CI estimates.The study by Li et al.  was a laOur observed lack of association between calcium channel blocker use and breast cancer risk in this Sister Study is consistent with the results of recently published analyses from the Nurse’s Health Study  and the Although a small number of women may change medications after enrollment questionnaire assessment, such changes would not be expected to bias risk estimates which are based solely on medication use up to the time of enrollment. This analysis is also limited by a relatively short mean follow-up time of 5.3 years.While there was no association between calcium channel blocker use and development of breast cancer, we did observe that calcium channel blocker use, and use of antihypertensives in general, is associated with several breast cancer risk factors. Women who used calcium channel blockers in our study were more likely to have a history of smoking, to be overweight, to be postmenopausal, and to have used hormone replacement therapy. These associations might help to explain the inconsistencies observed in past studies of breast cancer risk and antihypertensive drug use. Incomplete adjustment for the differences in risk profiles between calcium channel blocker users and nonusers might produce associations between calcium channel blocker use and breast cancer, particularly in smaller studies.Reports of potential excess risk due to antihypertensive drugs are likely to be of particular concern for women with increased breast cancer risk from genetic or other factors. Such concern might lead to less effective treatment for hypertension. Our findings in women with a family history of breast cancer and the findings from other recent prospective studies indicate that calcium channel blocking drugs can be used for long-term hypertension treatment without concern about increases in breast cancer risk.ACE, angiotensin-converting enzyme; BMI, body mass index; CI, confidence interval; DCIS, ductal carcinoma in situ; ER–, estrogen receptor negative; ER+, estrogen receptor positive; HR, hazard ratio; LMP, last menstrual period; SR, sustained release; XR, extended release


Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile . The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report. Breast cancer is the most common cancer among Western women, with approximately 1.67 million cases diagnosed annually worldwide . StratifBRCA1 and BRCA2 genes explain less than 20% of the two-fold familial relative risk (FRR)  have led to the discovery of multiple common, low-risk variants (single nucleotide polymorphisms [SNPs]) associated with breast cancer risk (Both genetic and lifestyle factors are implicated in the aetiology of breast cancer. Women with a history of breast cancer in a first-degree relative are at approximately two-fold higher risk than women without a family history . Rare hisk (FRR)  and accocer risk , many ofcer risk ,13. Rececer risk .Risks conferred by SNPs are not sufficiently large to be useful in risk prediction individually. However, the combined effect of multiple SNPs could achieve a degree of risk discrimination that is useful for population-based programmes of breast cancer prevention and early detection ,18. In tSupplementary Tables 1–3 (available online) show study designs and numbers of breast cancer cases and control women included.Study participants for the primary analyses (set 1) were 89 049 women of European origin participating in 41 studies in BCAC. All studies were approved by the relevant institutional review boards, and all individuals gave written informed consent. Samples were genotyped using a custom Illumina iSelect array (iCOGS) comprising 211 155 SNPs (P < 5x10-8) by COGS or previous publications, with either breast cancer overall or ER-negative disease. SNPs and regions included are summarized in Supplementary Table 4 (available online).Analyses were based primarily on variants reported to be associated (at Supplementary Methods (available online).Tests for pair-wise SNP*SNP interactions (departures from a multiplicative model) were carried out using logistic regression, with breast cancer as the outcome. The two SNPs were each coded as a categorical variable , while the interaction term (SNP1*SNP2) was included as continuous covariate. All analyses were adjusted for study and seven principal components (PC) to account for population substructure . AdditioTo investigate the association between breast cancer risk and the combined effects of 77 SNPs, a PRS was derived for each individual using the formula:βk is the per-allele log odds ratio (OR) for breast cancer associated with the minor allele for SNP k, and xk the number of alleles for the same SNP , and n = 77 is the total number of SNPs. Thus, the PRS summarizes the combined effect of the SNPs, ignoring departures from a multiplicative model (Supplementary Table 4 (available online).where ve model . SNPs anth to 60th percentile) as the reference. Observed odds ratios for breast cancer by percentile of the PRS were compared with predicted odds ratios under a multiplicative polygenic model of inheritance. Modification of the PRS by age or by family history of breast cancer in a first-degree relative was evaluated by fitting additional interaction terms in the model. All tests of statistical significance were two-sided. The thresholds for statistical significance are indicated below.Logistic regression models were used to estimate the odds ratios for breast cancer by percentile of the PRS, with the middle quintile category (40Supplementary Methods (available online).The absolute risk of overall breast cancer, ER-positive and ER-negative breast cancer for individuals in each risk category, was calculated taking into account the competing risk of dying from other causes apart from breast cancer. Approximate confidence limits for the absolute risk were derived from the variance-covariance matrix of the log (relative risk) parameters in the logistic regression analysis. Detailed methods are provided in Supplementary Table 3, available online). There was no strong evidence for interaction between any particular SNP pair after Bonferroni correction . Plots of expected vs observed log10P values for SNP*SNP interaction tests showed slight departure from the null hypothesis of multiplicative effects , and the number of statistically significant interactions with Pinteraction values of less than .01 was larger than expected by chance . Meta-analysis of SNP*SNP interaction test results from the iCOGS dataset with those from 72 014 additional women in BCAC yielded similar results . Given that no SNP pair showed strong evidence for departure from the multiplicative model, subsequent analyses were based on a PRS that included the main effects of SNPs but no SNP*SNP interaction terms.Data on 46 450 breast cancer cases and 42 599 controls from 41 studies were included in the interaction analyses (y chance . To inveth to 60th percentile) are shown in th to 90th percentile . For women in the lowest 1% of the PRS distribution, the estimated odds ratio compared with women in the middle quintile was 0.32 (95% CI = 0.25 to 0.40). By contrast, for women in the highest 1% of the PRS distribution, the estimated OR compared with women in the middle quintile was 3.36 . When PRS were derived separately for ER-positive and ER-negative disease, the corresponding odds ratios were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively . The log OR per unit SD was also similar for pKARMA alone (log OR per unit SD = 0.4).As predicted by the polygenic, multiplicative model, the number of breast cancer risk alleles and the 77-SNP PRS approximated a normal distribution for both breast cancer cases and control women . The oddectively , B and Cectively . A validSupplementary Figure 2 (available online). There was a statistically significant interaction between PRS and age, the association between PRS and breast cancer risk decreasing with age ; however, we observed a stronger effect of family history for women at the lowest 1% of the PRS .A family history of breast cancer in one or more affected first-degree relatives was reported by 18.5% of breast cancer cases and 11.1% of control women. The odds ratio for family history was attenuated from 1.81 to 1.68 (12.6% attenuation) after adjusting for the PRS . At younThe discriminative accuracy of the PRS, as measured by the C-statistic, was 0.622 (95% CI = 0.619 to 0.627); discrimination was similar when restricted to pKARMA alone, with an area under the curve of 0.615 (95% CI = 0.608 to 0.616) (data not shown).The estimated risk of developing breast cancer by age 80 years for women in the lowest and highest 1% of the PRS was 3.5% (95% CI = 2.6% to 4.4%) and 29.0% (95% CI = 24.9% to 33.5%), respectively . For theWe estimated the 10-year absolute risk of breast cancer at different ages and evaluated the age at which women at different levels of the PRS reach a threshold of 2.4%, which corresponds to the average 10-year risk of breast cancer for women age 47 years. This threshold was reached at 32 years for women whose PRS is above the 99th percentile of the PRS, and 57 years for women in the 20th to 40th percentiles of the PRS, and was never reached for women in lower percentiles . As expeIn this report, we evaluated the degree of breast cancer risk stratification that can be attained in women of European ancestry using data for 77 common genetic variants, summarized as a PRS. Our results show that the PRS stratifies breast cancer risk in women without family history and refines genetic risk in women with a family history of breast cancer.P less than .01. This could be the result of underlying population stratification not accounted for by principal components adjustment or reflect the presence of multiple interactions too weak to be established individually. A recent study also found no evidence for interactions among SNPs with weaker evidence for main effects  is the most efficient, assuming that SNP odds ratios combine multiplicatively  . Evaluat effects . AlthougThe 77-SNP PRS was associated with a larger effect than previously reported for a 10-SNP PRS . For exaThere has been little evidence of differences by age in the per-allele odds ratio for individual SNPs. However, we observed a small but statistically significant decrease in odds ratio for PRS with increasing age. As expected, the odds ratio for family history was reduced after adjustment for the PRS. This attenuation (~12.6%) was consistent with the estimated fraction of the two-fold FRR explained by the 77-SNPs under a polygenic risk model . The joith percentile of the PRS was 3.5% (95% CI = 2.6% to 4.4%) and 29.0% (95% CI = 24.9% to 33.5%), respectively. UK NICE guidelines recommend enhanced surveillance for women with a family history with lifetime risk of developing breast cancer over 17% . Numbers were also limited to explore the effects at very young ages, and estimates were less precise for ER-negative disease. There was heterogeneity among the studies, both in population and design, but we saw no evidence of heterogeneity in SNP odds ratios among studies, suggesting that the estimates should be broadly applicable. Oversampling for family history could have led to a bias in the odds ratios by PRS, and for this reason we excluded studies that were sampled on the basis of family history. Finally, we were not able to consider lifestyle/environmental risk factors in our model, as data on all of these risk factors were not consistently available across all studies. Interactions between the PRS and environmental factors will need to be explicitly tested for in future studies.In previous reports, improvement in risk discrimination by genomic profiling over that conferred by known risk factors was not substantial ,29, althCancer Research-UK  and the European Community’s Seventh Framework Programme (223175 [HEALTH-F2-2009–223175]) (COGS). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009–223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research (CIHR) for the “CIHR Team in Familial Risks of Breast Cancer” program, and the Ministry of Economic Development, Innovation and Export Trade of Quebec (PSR-SIIRI-701). This work was also supported by Breakthrough Breast Cancer funding (to MGC). Analysis was supported in part by the National Institutes of Health Post-Genome Wide Association initiative (1U19CA148065 (DRIVE) and 1U19CA148537 (ELLIPSE)). Laboratory infrastructure was funded by Cancer Research UK (C8197/A10123). This work was also supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research and the Ministère de l’enseignement supérieur, de la recherche, de la science et de la technologie du Québec through Génome Québec for the PERSPECTIVE project. Breast Cancer Association Consortium meetings were funded by the European Union European Cooperation in Science and Technology (COST) programme (BM0606).This work was supported by The Australian Breast Cancer Family Study (ABCFS), Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) studies were supported by the US National Cancer Institute (UM1 CA164920). The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation , and the Victorian Breast Cancer Research Consortium. JLH is a National Health and Medical Research Council (NHMRC) Australia Fellow and a Victorian Breast Cancer Research Consortium Group Leader. MCS is an NHMRC Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. JLH and MCS are both group leaders of the Victoria Breast Cancer Research Consortium. The content of this manuscript does not necessarily reflect the views or policies of the US National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR.The Amsterdam Breast Cancer Study (ABCS) was supported by the Dutch Cancer Society  and BBMRI-NL, which is a Research Infrastructure financed by the Dutch government (NWO 184·021·007).The Australian Breast Cancer Tissue Bank (ABCTB) study was supported by the National Health and Medical Research Council of Australia, The Cancer Institute New South Wales and the National Breast Cancer Foundation.The Bavarian Breast Cancer Cases and Controls (BBCC) study was partly funded by ELAN-Fond of the University Hospital of Erlangen.The British Breast Cancer Study (BBCS) was funded by Cancer Research UK and Breakthrough Breast Cancer and acknowledges National Health Service funding to the National Institutes for Health Research Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BBCS GWAS received funding from The Institut National de Cancer.The Breast Cancer In Galway Genetic Study (BIGGS) was supported by National Institutes for Health Research Comprehensive Biomedical Research Centre, Guy’s & St.Thomas’ NHS Foundation Trust in partnership with King’s College London, United Kingdom (ES), and the Oxford Biomedical Research Centre (IT).The Breast Cancer Study of the University Clinic Heidelberg (BSUCH) was supported by the Dietmar-Hopp Foundation, the Helmholtz Society, and the German Cancer Research Center (DKFZ).The CECILE Breast Cancer Study (CECILE) was funded by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Ligue contre le Cancer Grand Ouest, Agence Nationale de Sécurité Sanitaire (ANSES), Agence Nationale de la Recherche (ANR).The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev Hospital.The Spanish National Cancer Centre Breast Cancer Study (CNIO-BCS) was supported by the Genome Spain Foundation, the Red Temática de Investigación Cooperativa en Cáncer, and by grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario .The California Teachers Study (CTS) was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398). The CTS study was also funded by the Lon V. Smith Foundation (LVS39420) to HAC. Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885.For the DietCompLyf Breast Cancer Survival Study (DBCSS) the University of Westminster curated the DietCompLyf database, created by and funded by Against Breast Cancer Registered Charity No. 1121258. The University of Westminster’s Against Breast Cancer Research Unit acknowledges funding from the charity Against Breast Cancer (Registered Charity Number 1121258).The Esther Breast Cancer Study (ESTHER) was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe).The Familial Breast Cancer Study (FBCS) study was supported by funds from Cancer Research UK , a US Military Acquisition (ACQ) Activity, an Era of Hope Award (W81XWH-05-1-0204), and the Institute of Cancer Research UK. CT is funded by a Medical Research Council (UK) Clinical Research Fellowship. The FBCS acknowledges National Health Service (NHS) funding to the Royal Marsden / Institute of Cancer Research National Institutes for Health Research (NIHR) Specialist Cancer Biomedical Research Centre.The German Consortium for Hereditary Breast & Ovarian Cancer (GC-HBOC) was supported by Deutsche Krebshilfe (107 352).Gene Environment Interaction and Breast Cancer in Germany (GENICA) was funded by the Federal Ministry of Education and Research (BMBF) Germany , the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA), Bochum, and the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany.The Genetic Epidemiology Study of Breast Cancer by Age 50 (GESBC) study was supported by the Deutsche Krebshilfe e. V. (70492) and the German Cancer Research Center (DKFZ).The Hannover Breast Cancer Study (HABCS) was supported by an intramural grant from Hannover Medical School.The Helsinki Breast Cancer Study (HEBCS) was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (grant number 266528), the Finnish Cancer Society, the Nordic Cancer Union, and the Sigrid Juselius Foundation.The Hannover-Minsk Breast Cancer Study (HMBCS) was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation.The Hannover-Ufa Breast Cancer Study (HUBCS) was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017) and by the Ministry of Education and Science of the Russian Federation , the Russian Foundation for Basic Research (14-04-31169 mol_a) and State task of the Ministry of Education and Science of the Russian Federation (310-14).The Karolinska Breast Cancer Study (KARBAC) was supported by the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, the Gustav V. Jubilee foundation, and the Bert von Kantzows foundation.The Kuopio Breast Cancer Project (KBCP) was supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, the Academy of Finland, and by the strategic funding of the University of Eastern Finland.The kConFab study was supported by the National Breast Cancer Foundation and previously by the National Health and Medical Research Council, the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania, and South Australia, and the Cancer Foundation of Western Australia. The kConFab Clinical Follow Up Study was funded by the National Health and Medical Research Council . RB was a Cancer Institute NSW Fellow.The AOCS study was supported by the United States Army Medical Research and Materiel Command (DAMD17-01-1-0729), the Cancer Council of Tasmania, the Cancer Foundation of Western Australia and the National Health and Medical Research Council (199600), and a National Health and Medical Research Council grant to GCT.The Leuven Multidisciplinary Breast Centre (LMBC) study is supported by the “Stichting tegen Kanker”  and by the FWO and the KULPFV/10/016-SymBioSysII to DL.The Mammary Carcinoma Risk Factor Investigation (MARIE) study was supported by the Deutsche Krebshilfe e.V. , the Hamburg Cancer Society, the German Cancer Research Center (DKFZ), and the Federal Ministry of Education and Research (BMBF) Germany (01KH0402).The Milan Breast Cancer Study Group (MBCSG) was supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws .The Mayo Clinic Breast Cancer Study (MCBCS) was supported by the National Institutes of Health , a National Institutes of Health Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Breast Cancer Research Foundation, the Komen Race for the Cure, and by a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation.The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian National Health and Medical Research Council  and by infrastructure provided by Cancer Council Victoria.The Multi-ethnic Cohort (MEC) was supported by the National Institutes of Health .The Memorial Sloan-Kettering Cancer Center (MSKCC) was supported by grants from the Breast Cancer Research Foundation and the Robert and Kate Niehaus Clinical Cancer Genetics Initiative.The work of Montreal Gene-Environment Breast Cancer Study (MTLGEBCS) was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701). MG received an Investigator Award from the CIHR and a Health Scholar Award from the Fonds de la recherche en santé du Québec. J Simard is chairholder of the Canada Research Chair in Oncogenetics.The Norwegian Breast Cancer Study (NBCS) was supported by grants from the Norwegian Research council  to VNK and a Swizz Bridge Award to ALBD.The Nashville Breast Health Study (NBHS) was supported by National Institutes of Health (grant R01CA100374).The Nurses Health Study (NHS) was funded by National Institutes of Health (PO1 CA87969), and this project was (in part) supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network (U19 CA148065).The Oulu Breast Cancer Study (OBCS) was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland, the University of Oulu, and the Oulu University Hospital.The Leiden University Medical Centre Breast Cancer Study (ORIGO) was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16).The NCI Polish Breast Cancer Study (PBCS) was supported by the Intramural Research Programs of the Division of Cancer Epidemiology and Genetics and the Center for Cancer Research of the National Cancer Institute.The Karolinska Mammography Project for Risk Prediction of Breast Cancer (pKARMA) study was supported by Märit and Hans Rausings Initiative Against Breast Cancer and Cancer Risk Prediction Center, a Linneus Centre (contract 70867902) financed by the Swedish Research Council.The Prospective Study of Outcomes in Sporadic Versus Hereditary Breast Cancer (POSH) study was supported by Cancer Research UK .The Rotterdam Breast Cancer Study (RBCS) was funded by the Dutch Cancer Society .The Singapore and Sweden Breast Cancer Study (SASBAC) was supported by the Agency for Science, Technology and Research of Singapore (A*STAR), the National Institutes of Health, and the Susan G. Komen Breast Cancer Foundation.The Sheffield Breast Cancer Study (SBCS) was supported by Yorkshire Cancer Research  and the Sheffield Experimental Cancer Medicine Centre.The Southern Community Cohort Study (SCCS) is funded by National Institutes of Health (R01 CA092447). The Arkansas Central Cancer Registry is fully funded by a grant from the National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC).Study of Epidemiology and Risk factors in Cancer Heredity (SEARCH) was funded by a programme grant from Cancer Research UK (C490/A10124), the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge, and a Cancer Research UK grant (C8197/A10123) to AMD.The Städtisches Klinikum Karlsruhe Deutsches Krebsforschungszentrum Study (SKKDKFZS) was supported by the Deutsches Krebsforschungszentrum (DKFZ).The IHCC-Szczecin Breast Cancer Study (SZBCS) was supported by grant (PBZ_KBN_122/P05/2004).The Triple Negative Breast Cancer Consortium Study (TNBCC) was supported by the National Institutes of Health (CA128978) and the National Institutes of Health Specialized Program of Research Excellence in Breast Cancer (CA116201), the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation, the Stefanie Spielman Breast Cancer fund and the Ohio State University (OSU) Comprehensive Cancer Center, DBBR , the European Union  and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: ARISTEIA.The DEMOKRITOS study was supported by a Hellenic Cooperative Oncology Group research grant (HR R_BG/04) and the Greek General Secretary for Research and Technology (GSRT) Program, Research Excellence II, funded at 75% by the European Union.The OSU study was funded by the Stefanie Spielman fund and the OSU Comprehensive Cancer Center.The Roswell Park Cancer Institute (RPCI) study was supported by RPCI DataBank and BioRepository (DBBR), a Cancer Center Support Grant Shared Resource (P30 CA016056-32).The UCIBCS was supported by the National Institutes of Health, National Cancer Institute grant CA-58860 and the Lon V Smith Foundation grant LVS-39420.The UK Breakthrough Generations Study (UKBGS) was funded by Breakthrough Breast Cancer and the Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the Royal Marsden Hospital/ICR National Institutes for Health Research Biomedical Research Centre.The US Three State Study (US3SS) was supported by Massachusetts (R01CA47305 to KME), Wisconsin (R01 CA47147 to PAN), and New Hampshire (R01CA69664 to LTE) centers and Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services.The US Radiologic Technologists Study (USRT) was funded by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services.Biological sample preparation for several studies was conducted at the Epidemiology Biospecimen Core Lab, supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485).


Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk.We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs).42 = 7.2, P value 0.13) than BCRAT . The IBIS model also showed better discrimination  than did the BCRAT model . In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk .The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer  = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL XBRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer,  Accurate assessment of a woman's absolute breast cancer risk is needed in clinical management decisions about mammographic screening, risk-reducing surgeries and other preventive interventions. In the United States, annual screening mammography and magnetic resonance imaging (MRI) beginning at age 30 years are recommended for women with a lifetime risk of 20% or greater . In addiBRCA1/2 carrier status, and polygenes such as the BOADICEA model [BRCA1/2 mutation status or extended family history . In contrast, the IBIS model includes extended family history, BRCA1/2 genetic status with nongenetic risk factors such as age, age at menarche, parity, age at first live birth, age at menopause, history of hormone replacement therapy use, history of hyperplasia/atypical hyperplasia, history of lobular carcinoma in situ, height and body mass index (BMI).Several statistical models have been developed for assigning absolute risk of developing breast cancer -6. Some EA model , whereasEA model ,8-10 andEA model . This moin situ (DCIS) [The BCRAT model has been evaluated in several large cohorts ,12-14 anu (DCIS) . ConsequBRCA1/2 mutation) versus subgroups typically classified as above-average risk.Here, we compare these two models as they are applied clinically because they capture both nongenetic and family history data, and they are commonly used in the clinic. A single model applicable to all women would be useful, particularly in view of the large differences apparent in Figure BRCA1 or BRCA2 mutation carriers [The New York site of the Breast Cancer Family Registry (BCFR) has recruited and followed 4,991 participants  from 1,322 families since 1995 (for details see -26. Eligcarriers . After iin situ breast cancer (women with both DCIS and LCIS were excluded). For this study, the family history information is defined based on each individual at the time they were recruited into the study. In this cohort of 1,857 women unaffected at baseline, 641 were without a first-degree female relative with breast cancer and only 110 were BRCA1 or BRCA2 positive. Thus, the cohort spans the continuum of risk including women at very high risk (mutation carriers) and those at lower risk (mutation negative and/or with more distant relatives with cancer). Figure For this study, we further restricted eligibility to the 1,857 women from 938 families with at least one subsequent update on cancer and vital status, and who at cohort entry were aged 20 to 70 years and had no history of bilateral prophylactic mastectomy, or invasive or BRCA1 and 6174delT in BRCA2. In addition, for all non-Ashkenazi Jewish families, we screened the youngest affected individual using full sequencing methods. If the youngest affected individual had a mutation, the remaining family members were all tested for this family-specific mutation. In our cohort for this study, 800 women were tested for BRCA mutation, of which 110 tested positive. If the youngest affected member did not have a mutation in either BRCA1 or BRCA2, additional testing was not performed. For the purposes of our analyses, these women were separated and labeled 'not tested'. However, because the youngest affected member of these families did not have a mutation the probability that she had a mutation would be very low and thus for interpreation the women labeled 'not tested' can be interpreted as being 'negative' [All self-identified Ashkenazi Jewish participants were screened for the three founder mutations, 185delAG and 5382insC in egative' .BRCA1/2 - and a hypothetical low-penetrance dominant gene. Nongenetic risk factors are included via a proportional hazard model [We assigned each subject a 10-year breast cancer risk using the software packages BCRAT and IBIS ,6,9,10, For BCRAT, we calculated 10-year risks using the latest update August 2011) of the Statistical Analysis Systems (SAS) macro  that all11 of theπ of developing breast cancer within 10 years of risk assessment and before dying of other causes. This probability isWomen were classified into quartiles based on the predicted 10-year risk from the models. For each quartile, we used the survival data to estimate the 'observed' 10-year risk, defined as the probability B(t) Iand D(t) Idenote the hazard rates for breast cancer and death, respectively, at t years from baseline [π for each risk quartile by obtaining nonparametric estimates of the hazard rates B(t) Iand D(t) Iand using these estimates in equation (1). In the absence of censoring, the quartile-specific estimates where baseline . If the lined in  to estimWe assessed model calibration by comparing the mean model-assigned risk to observed breast cancer incidence in each of the four assigned risk quartiles, using the Hosmer-Lemeshow (HL) chi-squared goodness-of-fit statistic . To examWe assessed the models' abilities to discriminate the women who did and did not develop breast cancer within 10 years of risk assignment by estimating each model's area under the receiver-operator characteristic curve (AUC). This measure ranges from 0.5 (no discriminative ability) to 1 (perfect discrimination). We calculated AUC estimates using the R packages 'ROCR' and 'pROC' ignoring censored subjects, which is valid under the assumption that the censoring mechanism is unrelated to the risks of breast cancer and death . We alsoBRCA mutations, 110 were positive, the remaining nontested are assumed negative (see Methods). Among all subjects, 83 developed breast cancer and 55 died of other causes within 10 years of baseline, 730 were breast-cancer-free 10 years after baseline and 989 were last observed without breast cancer within 10 years of baseline. The mean follow-up length was 8.1 years (range 0.1 to 14.5). A total of 76% of the cohort were observed for five or more years, and 4% were observed for one year or less.Table 42 = 22.0, P value <0.001). For IBIS, the mean assigned risks were nonsignificantly lower than the observed risks in quartiles 1, 2, 3 and nonsignificantly higher in quartile 4, with little evidence of poor fit . The receiver operating characteristic (ROC) plots in Figure Figure in situ breast cancer, it did contain other women for whom BCRAT is not recommended, that is, women aged less than 35 years at risk assignment and women known to carry BRCA mutations. Accordingly, we also compared the two models after excluding these women from the cohort. We found that the superior performance of IBIS persisted: the HL statistic showed better calibration for IBIS  than BCRAT , and better discrimination  than BCRAT .Although the cohort did not contain women with a history of 42 = 15.1, P value <0.01), and that the overall AUC estimate decreased to 67.8% (CI = 62.0 to 73.7). When BRCA status was also omitted from IBIS, the AUC decreased further to 62.2% (CI = 56.5 to 67.9), similar to that of BCRAT. Although the differences in these AUCs are not statistically significant, they suggest that, even when a risk model captures BRCA mutation status, its discrimination can be improved by including second-degree family history.Which covariates unique to IBIS explain the observed differences in their performance? To address this question, we omitted information on second-degree family history from IBIS and found that the resulting 'pruned' risks, like those of BCRAT, were too low in the first three quartiles (HL XBRCA mutation). The only exceptions were the subgroups of women with age at first birth less than 25 years, women with at least one prior breast biopsy, and women who are not Non-Hispanic White. Mean IBIS risks were significantly lower than observed risks in subgroups of women without first-degree relatives with breast cancer and without relatives with ovarian cancer, and were significantly higher than observed risks for mutation carriers.Table N = 46) represent cases whose subsequent breast cancer occurrence was better identified by IBIS than BCRAT, while points below the line (N = 37) represent cases better identified by BCRAT than IBIS. The mean CRP across cases for a model is its AUC. Using the Wilcoxon signed-rank test, we also found that the median IBIS CRP was statistically significantly different than that of BCRAT . Figure BRCA mutations, and one case has a first-degree relative with ovarian cancer, information used by IBIS but not BCRAT. In contrast, the outlying case in the lower right region with higher BCRAT than IBIS CRP had a prior biopsy, information used by BCRAT but not IBIS.As noted by Pepe and Longton , a usefuComparison of BCRAT and IBIS AUCs within covariate-specific subgroups is provided in Table et al [BRCA1/2 mutation carriers and women under age 35 years at risk assignment. Overall, our cohort is of higher risk than the general U.S. population. For example, compared to SEER rates based on the age distribution of our cohort, our observed rates were 3.1 times that expected of an average risk population. Thus, while the overall improved performance of IBIS over BCRAT may not be unexpected given our higher risk cohort, what was unexpected was that this improved performance extended to subgroups containing woman considered at average risk, such as those with no family history and no BRCA1/2 mutations.Breast cancer risks for women in the present cohort span the continuum of risk. Our intention was to compare two commonly used models that include nongenetic factors, BCRAT and IBIS, as used by clinicians. We observed better overall calibration and discrimination for IBIS than for BCRAT, in agreement with the findings of Amir et al , with thet al , and heret al . The BCRet al  and in 2BRCA mutation status. This improvement, although not statistically significant, is nevertheless plausible, as extended family history captures all of the many breast cancer genetic risk factors in addition to BRCA1/2 mutations as well as nongenetic shared familiar environmental factors not captured in the model. A practical barrier to the broad use of models incorporating extended family history data are patients' incomplete knowledge about the health of their more distant blood relatives.By comparing the performance of the full IBIS model to a pruned version lacking second-degree family history information, we found that this extended family information increased the AUC estimate, despite the pruned model's capture of BRCA1/2 mutation carriers. Only in women with a prior breast biospy was the discrimination better in BCRAT. BCRAT includes number of biopsies, regardless of their outcome, while the IBIS model only includes atypical hyperplasia diagnosed via biopsy. This may limit the precision of IBIS estimates, because pathology of biopsies is incompletely obtained from self-report, while number of biopsies can be more readily recalled and accurately reported.Risks assigned by the IBIS model also discriminated future cases from noncases better than did those assigned by BCRAT, although the differences were not statistically significant. Discrimination was better for IBIS than BCRAT in almost all subgroups, and as expected, the difference was particularly large for Oncologists and genetic counselors would be well served by a single model that avoids having to choose among several models on this basis of patient characteristics. Our cohort spans the continuum of risk with a proportion of the women below population average risk  .BRCA1/2 mutation negative). Interestingly, the highest quartile of BCRAT-assigned risks was the only one in which the mean BCRAT risk did not differ signficantly from the observed risk. Thus, not only did IBIS outperform BCRAT in subgroups whose risks are typically considered average  but the BCRAT model was well calibrated only in the risk group in which it is unlikely to be used by clinicians, that is, the highest quartile of assigned risk. These findings need replication in other large cohorts spanning a broad range of risks. They suggest the complexity of applying risk models in the clinic based on a priori assumptions of risk defined by family history and genetic status. Models that have been developed based on extended family history and genetic data, such as the IBIS model, may perform well in women considered at average risk. Extending models that already capture extended family history and genetic information to include a larger array of nongenetic risk factors may help risk models play a major role in disease prevention.In summary, we found that the IBIS model performed better in this cohort whose risks span the continuum of breast cancer risk. This was true even in subgroups containing women typically considered average risk (for example, no family history of breast cancer, AUC: area under the receiver-operator characteristic curve; BCFR: Breast Cancer Family Registry; BCRAT: Breast Cancer Risk Assessment Tool; CI: confidence interval; CRP: case risk percentile; HL: Hosmer-Lemeshow chi-squared goodness-of-fit test; IBIS: International Breast Cancer Intervention Study.The authors declare that they have no competing interestsMBT is the PI of the parent study and initiated this current study. ASQ, ASW, MBT worked closely together at all stages of this current study and drafted the manuscript. ASQ carried out the analysis. ASW developed the methods for quantile-specific risk performance metrics and led the overall statistical analyses. TS was integral to the refinement of the family pedigree data and other data used in the risk models. ASQ, ASW, TS, KS and MBT contributed to the interpretation of the data, revised the manuscript, and approved of the final manuscript.


Treatment with tamoxifen or chemotherapy reduces the risk of contralateral breast cancer (CBC). However, it is uncertain how long the protection lasts and whether the protective effect is modified by patient, tumor, or treatment characteristics.The population-based WECARE Study included 1521 cases with CBC and 2212 age- and year of first diagnosis-matched controls with unilateral breast cancer recruited during two phases in the USA, Canada, and Denmark. Women were diagnosed with a first breast cancer before age 55 years during 1985–2008. Abstraction of medical records provided detailed treatment information, while information on risk factors was obtained during telephone interviews. Risk ratios (RRs) and 95 % confidence intervals (CIs) for CBC were obtained from multivariable conditional logistic regression models.Compared with never users of tamoxifen, the RR of CBC was lower for current users of tamoxifen  and for past users within 3 years of last use . There was no evidence of an increased risk of estrogen receptor-negative CBC associated with ever use of tamoxifen or use for 4.5 or more years. Use of chemotherapy (ever versus never use) was associated with a significantly reduced RR of developing CBC 1–4 years  and 5–9 years  after first breast cancer diagnosis. RRs of CBC associated with tamoxifen or with chemotherapy use were independent of age, family history of breast cancer, body mass index and tumor characteristics of the first breast cancer with the exception that the RR of CBC was lower for lobular histology compared with other histologies.Our findings are consistent with previous studies showing that treatment with tamoxifen or chemotherapy is associated with a lower risk of CBC although the risk reduction appears to last for a limited time period after treatment is completed.The online version of this article (doi:10.1186/s13058-016-0726-0) contains supplementary material, which is available to authorized users. Women who receive tamoxifen or chemotherapy as treatment for breast cancer are at reduced risk of developing contralateral breast cancer (CBC) according to findings from randomized trials , 2 and oData on how long the protective effect persists are sparse in relation to tamoxifen , 6, 9 anBRCA1 and BRCA2 mutation carriers [Some evidence indicates that tamoxifen may only reduce the risk of CBC for women diagnosed with an ER-positive first tumor  and thatcarriers –13. It iA better understanding of these issues is critical since CBC is a major concern for breast cancer survivors. Therefore, we analyzed data from the expanded WECARE Study, a large international population-based case-control study of CBC risk following a diagnosis of first primary breast cancer.Recruitment and data collection for the WECARE Study were conducted in two phases, herein referred to as WECARE I 2001–2004) and WECARE II (2009–2012). All participants were identified through eight population-based cancer registries: six in the USA, one in Canada and one in Denmark  were eligible for WECARE I, whereas only invasive CBCs were included in WECARE II. Cases and controls were individually matched in triplets (one case and two controls) in WECARE I and in pairs in WECARE II by age at first breast cancer (5-year strata), calendar-year of first diagnosis (4-year strata), cancer registry, and race/ethnicity. In WECARE I, for statistical purposes, cases and controls were further counter-matched on radiotherapy so that in each triplet two women had received radiotherapy and one woman had not. The reference date for cases was the CBC diagnosis date while for controls it was defined by adding the interval between the first breast cancer and CBC for the matched case to the date of unilateral breast cancer for the control. No other cancer diagnoses, except non-melanoma skin cancer, were allowed up to and including the reference date. The ‘at-risk’ period was defined as beginning at the date of first cancer diagnosis and ending at the reference date. During this period cases and controls had to reside within the same registry catchment area. Controls were ineligible if they had a prophylactic contralateral mastectomy before the reference date. All participants had to be alive at the time of contact in order to complete a telephone interview and provide a biospecimen.Information regarding treatment for the first breast cancer and any recurrences during the ‘at-risk’ period was obtained by abstracting medical records. The recorded information on chemotherapy and hormonal therapy included start and end dates of administration, reason for treatment, and type of drug. If treatment information was unavailable in the medical record, we used self-reported information .Medical records were used to obtain information on tumor characteristics  status) for the first breast cancer and the CBC. Information on tumor characteristics was also sought from the cancer registries where available. The telephone interview included questions on known breast cancer risk factors.A total of 2354 cases and 3599 controls were eligible and invited to participate in the WECARE studies. The final number of participants who completed the interview and provided a biospecimen was 1521 (65 %) cases and 2212 (62 %) controls. All participants gave written informed consent before enrollment, and the study protocols were approved by the institutional review boards at the University of Iowa (IRB-01), Fred Hutchinson Cancer Research Center, Cancer Prevention Institute of California, University of Southern California, Beckman Research Institute of the City of Hope, University of California at Irvine, Mount Sinai Hospital, Danish Cancer Society and Memorial Sloan Kettering Cancer Center, and by the Committee for the Protection of Human Subjects of the State of California and the ethical committee system in Denmark.2), histology , and ER/PR status . If either ER or PR was positive, we considered ER/PR status to be positive. To account for the counter-matching in WECARE I, we included a log-weight covariate offset term. For WECARE II participants who were matched in pairs, we assigned the value of 1 to the offset term.We used multivariable-adjusted conditional logistic regression models to estimate risk ratios (RRs) and 95 % confidence intervals (CIs) for CBC associated with systemic therapy used to treat first primary breast cancer. We estimated the RR for CBC associated with ever use of tamoxifen or chemotherapy versus never use as the reference category. We also calculated RRs associated with time since first breast cancer diagnosis , age at first breast cancer diagnosis , year of first breast cancer diagnosis , first-degree family history of breast cancer , BMI  and by time since last use at reference date (0 (current use at reference date), >0–37 months (past use), ≥37 months (past use)). The cut-point at 37 months was the median for values greater than 0 for controls who received tamoxifen.We also estimated RRs for ER-positive and ER-negative CBCs separately for ever versus never use of tamoxifen and by duration and time since last use at reference date.All analyses of associations of risk with tamoxifen treatment were repeated after restricting eligibility to women with a hormone receptor-positive first breast cancer.For chemotherapy, we examined the association between different regimens and CBC risk. A woman was classified as having received a chemotherapeutic regimen if she received the combination of the drugs for either her first primary breast cancer or a recurrence  or by duration of use . We found a decreased risk of CBC for current users of tamoxifen  and for past users who stopped tamoxifen treatment within 37 months before the reference date ; for women who stopped using tamoxifen ≥37 months before the reference date, CBC risk was close to unity . The RR for current users was lower and only statistically significant for those using tamoxifen for 54 months or longer  compared with those with shorter use . Within 37 months of ending therapy, there were no statistically significant reductions in CBC risk for women using tamoxifen for less than 54 months or for women using tamoxifen for 54 months or longer.Tamoxifen use during the ‘at-risk’ period was associated with a statistically significant decreased RR of CBC   comparing women treated with tamoxifen with those not receiving tamoxifen treatment Table . The RR P heterogeneity = 0.1–0.9) (Table P heterogeneity = 0.03).No statistically significant differences in associations with CBC risk were observed for tamoxifen use by age at first breast cancer or year at first breast cancer, family history of breast cancer, BMI or ER/PR status of first breast cancer (9) Table . TamoxifResults for women with hormone receptor-positive first tumors . We found the largest risk reductions for cyclophosphamide, methotrexate, and fluorouracil (CMF) regimens  and other regimens    , whereas no association was observed for tamoxifen only  Table . The larSensitivity analyses excluding cases diagnosed with in-situ CBC or participants diagnosed with recurrences showed no major changes in RR estimates (results not shown).In the WECARE Study, we showed that reduction in CBC risk was associated with longer duration of tamoxifen use for current users and it continued up to 3 years after ending treatment for past users. There was no increase in risk of ER-negative CBC associated with tamoxifen use for ≥54 months. A CBC risk reduction for up to 10 years after the first breast cancer diagnosis was found for women treated with chemotherapy. We found no evidence that CBC risks associated with these systemic treatments differed by age, year of diagnosis, family history of breast cancer, BMI, or ER/PR status, but the association with tamoxifen differed by histology of the first cancer.In the meta-analysis by the EBCTCG published in 1998, comparing breast cancer clinical trials with different durations of tamoxifen use, reductions in CBC risk were larger in trials of 5 years of tamoxifen use versus those of 1 or 2 years of use . A revieAn important question concerns the length of the protection against CBC after discontinuation of tamoxifen. The latest EBCTCG meta-analysis from 2011 showed a CBC risk reduction up to 5–9 years after the first breast cancer diagnosis corresponding to 0–4 years after ending treatment in trials testing 5 years of tamoxifen versus no tamoxifen  reportedAlthough the latest meta-analyses by the EBCTCG showed the preventive effect of tamoxifen on the development of CBC was limited to women with an ER-positive first breast cancer , trials BRCA1 and BRCA2 mutation carriers in respect to reducing CBC risk [Consistent with previous studies, we found that CBC risk associated with tamoxifen did not differ by age , 14 or BCBC risk  consisteLittle is known about how chemotherapy affects risk of CBC over time. Some studies failed to show risks deviating from unity during both short- and long-term time intervals after first breast cancer , 22. A sIn the 2005 EBCTCG meta-analysis, the beneficial effects of chemotherapy on CBC were restricted to younger women . We wereThe strengths of our study include the population-based study design, the large sample size of women diagnosed with CBC, and the detailed information on treatment obtained from medical records. This enabled us to adjust for other endocrine therapy including aromatase-inhibitors that have been shown to be more effective than tamoxifen in preventing CBC . HoweverIn conclusion, the expanded WECARE data suggest that treatment with tamoxifen or chemotherapy may offer protection against a CBC for a limited period following completion of treatment. Previously raised concerns that tamoxifen may increase risk of ER-negative CBC could not be substantiated.BMI, body mass index; CBC, contralateral breast cancer; CI, confidence interval; CMF, cyclophosphamide, methotrexate, and fluorouracil; ER, estrogen receptor; PR, progesterone receptor; RR, risk ratio