T3vsT1 = 0.9, 95% CI = 0.4–1.8, p trend = 0.68). In comparison with low AR/low ER women, ORs of 0.4 (95% CI = 0.1–1.2) for high AR/high ER women, 1.8 (95% CI = 0.4–7.8) for low AR/high ER women, and 0.7 (95% CI = 0.3–1.6) for high AR/low ER women were observed (p interaction = 0.21). Ki67 did not modify the association between AR expression and breast cancer risk (p interaction = 0.75). There was little evidence for an overall association between AR expression in normal breast tissue and breast cancer risk. These findings did not show that the AR association varied by Ki67 expression in normal breast tissue, though there was suggestive heterogeneity by ER expression.Sex steroid hormone signaling is critical in the development of breast cancers, although the role of the androgen receptor remains unclear. This study evaluated androgen receptor (AR) expression in normal breast tissue as a potential marker of breast cancer risk. We conducted a nested case–control study of women with benign breast disease (BBD) within the Nurses’ Health Studies. Epithelial AR expression was assessed by immunohistochemistry in normal tissue from the BBD biopsy and the percent of positive nuclei was estimated in ordinal categories of 10% for 78 breast cancer cases and 276 controls. Logistic regression models adjusting for the matching factors and BBD lesion type were used to calculate odds ratios (ORs) for the association between AR expression  and breast cancer risk. AR expression in normal breast tissue was not associated with subsequent breast cancer risk  in biopsy tissue taken from 354 women with benign breast disease, 78 of whom were later diagnosed with breast cancer. Although AR levels have been linked to outcomes in women who already have breast tumors, the study authors found no such association between AR expression and disease risk while women were still cancer-free. There was also little evidence for an interaction between AR levels and those of the estrogen receptor or the proliferation marker Ki67 on the incidence of breast cancer. However, the researchers caution, larger studies are needed to verify the results. Circulating androgens are associated with breast cancer risk among postmenopausal women, conferring roughly twofold higher risk comparing highest-to-lowest quintiles.5 Some of this apparent association may be due to the conversion of androgens to estrogens, leading to activation of estrogen signaling pathways in the breast. When circulating androgens are adjusted for circulating estradiol, these associations attenuate, but not entirely to unity, suggesting a potential effect of androgens on the breast, independent of their conversion to estrogens.7 Circulating hormones show moderate correlations with hormone levels in the breast, indicating that these may be indirect markers of the hormonal milieu in the breast.12Sex steroid hormones play a critical role in the development and progression of breast cancers.14 AR is more commonly co-expressed in estrogen receptor-positive (ER+) breast cancers (70–90%), though is still expressed in 40% of ER− cancers.17 Evidence from in vitro models suggests that the effects of AR signaling in breast cancers may depend on tumor ER expression.19 AR signaling promotes tumor growth and inhibits apoptosis in ER− breast cancers,20 while antagonizing ER signaling pathways in ER+ breast cancers.21 Epidemiologic studies have consistently found that AR expression is associated with improved breast cancer prognosis for women with ER+ breast cancer, in concordance with in vitro evidence.22 However, results from epidemiologic studies among women with ER− breast cancer are quite mixed.22 Although AR is expressed in normal breast epithelium, to date, AR expression and its potential interaction with ER expression have not been evaluated in normal breast tissue with respect to breast cancer risk. Few markers in normal tissue have been identified that predict future breast cancer risk.26 Of these, Ki67, a nuclear protein present in active phases of the cell cycle,27 has been most consistently linked to breast cancer risk.30 This association may be limited to women with low ER expression.29The androgen receptor (AR) is expressed in 60–80% of breast cancers and is an emerging prognostic and predictive marker, as well as potential therapeutic target, in breast cancer.In this analysis, we evaluated AR expression in normal breast tissue as a potential predictor of subsequent breast cancer risk in a nested case–control study within the Nurses’ Health Study and Nurses’ Health Study II cohorts and whether this association differed by ER co-expression. We further assessed potential effect modification by Ki67, given the heterogeneity previously observed for the Ki67-breast cancer association by ER expression. Finally, we explored the relationships between established breast cancer risk factors and AR expression in normal tissue.The median AR expression was 20% IQR: 10%–43%) in this population of 354 women (78 cases and 276 controls). The Spearman correlations were 0.44 between AR and ER expression and −0.09 between AR and Ki67 expression. Age-standardized distributions of the study matching factors and BBD lesion type are shown in Table 0%–43% inp trend = 0.68). Similarly, a 10% increase in AR expression was not associated with breast cancer risk  in the fully adjusted model.In logistic regression models adjusting for the matching factors, AR expression in normal breast tissue was not associated with subsequent breast cancer risk Table . After fp heterogeneity = 0.21)   Fig.  among thp value = 0.18 from age-adjusted ANCOVA). No other notable associations were observed with breast cancer risk factors.Exploratory analyses regarding the relationship between breast cancer risk factors and AR expression in normal tissue yielded few apparent associations Table . AR expr14 In a nested case–control study within the NHS and NHSII cohorts, we found no association between AR expression in normal TDLUs and the subsequent incidence of breast cancer.The androgen receptor is an emerging biomarker of interest in breast cancer. Prior studies have shown that AR may have utility as a predictive or prognostic marker, or may even be a viable therapeutic target for certain breast cancer subtypes.19 In ER− breast cancers, AR signaling can stimulate tumor growth. In contrast, in ER+ breast cancers, AR may have anti-proliferative effects—antagonizing ER signaling through the occupancy of shared or distinct-binding sites at ER target genes or through competition for transcriptional co-regulators, abrogating their interaction with ER.19 Comparatively little is known about the interactions between AR and ER signaling in normal breast epithelium, though evidence from animal models suggests that the AR may still inhibit estrogen-stimulated proliferation in normal breast tissue.33 We observed that women with high AR and high ER expression had suggestively lower risk of breast cancer, though this did not achieve statistical significance. While there were few individuals with low AR and high ER, they experience apparent higher risk of breast cancer, though again this was not statistically significant. The AR is more commonly expressed in breast epithelial cells than the ER, and while co-localization of AR and ER can occur, more often AR is expressed without ER.21 ER infrequently co-localizes with Ki67 in normal breast tissue, though this does often occur in malignant tissue.35 Prior analyses in this case–control study indicated that women with high Ki67 and low ER expression in normal TDLUs experienced higher breast cancer risk.29 Our findings indicated no heterogeneity for the association between Ki67 expression and breast cancer risk with respect to AR expression.We likewise found little evidence for an interaction between AR and ER expression in normal breast TDLUs with respect to the incidence of breast cancer. However, in vitro and population evidence support the presence of a biologic interaction between AR and ER signaling in breast cancers.36 This analysis showed no link between other reproductive factors  and concentrations of circulating androgens. A prior analysis in the NHS found that positive associations between obesity and weight change and breast cancer risk, and the inverse association between physical activity and breast cancer risk, were generally stronger for the risk of AR− tumors.37 Age at first birth and oral contraceptive use were also found to be associated with the incidence of AR− breast cancer.38 In contrast, alcohol consumption was found to be associated with the incidence of AR+ tumors.39 Apart from a non-significant suggestive positive association with age, we found no relationships between these risk factors and AR expression levels in normal breast tissue. Prior studies have evaluated the associations between breast cancer risk factors and ER and progesterone receptor (PR) expression in normal tissue.42 These studies found age, adult BMI, and alcohol consumption to be positively associated with ER expression and an inverse association with breastfeeding among parous women. Age, height, adult BMI, and BMI at age 18 were correlated with higher PR expression. While these risk factors may affect hormone receptor signaling in normal tissue, we found no relationship between these variables and AR expression, suggesting that their effects may not be through AR signaling pathways.Circulating androgens are a consistent risk factor for breast cancer among postmenopausal women. Additionally, androgen levels in plasma were shown to be correlated with several breast cancer risk factors including age, menopausal status, obesity, smoking, and alcohol consumption in a pooled analysis of 13 prospective studies.29 The limited sample size also precludes adjustment for many variables, leaving the findings susceptible to possible residual confounding. Additionally, the success rate for acquiring tissue from the BBD biopsy was low; though the women for whom we successfully obtain or do not obtain tissue have similar risk factor profiles  in the biennial questionnaires. These diagnoses are confirmed via medical record review or verbal confirmation from the nurse herself. All study protocols were approved by the institutional review board at Brigham and Women’s Hospital  and informed consent was obtained from all study participants.The Nurses’ Health Study (NHS) was established in 1976 with the enrollment of 121,700 US female registered nurses aged 30–55 years, while the Nurses’ Health Study II (NHSII) consists of 116,429 US female registered nurses aged 25–42 years at cohort baseline in 1989. Detailed descriptions of cohort procedures have been reported elsewhere.44 A median of 8 years (interquartile range [IQR] 5–12) elapsed between the BBD and breast cancer diagnoses for cases, and cases for which this time period was <6 months were excluded. Cases and controls were matched 1:4 on the age at breast cancer diagnosis (index date for controls), year of the BBD diagnosis, and years elapsed between BBD diagnosis and breast cancer diagnosis (index date).A nested case–control study was constructed within the two cohorts, wherein cases were women who reported a diagnosis of breast cancer after cohort baseline (through 1998 for NHS and 1999 for NHSII) and had previously reported a BBD diagnosis (either prior to study entry or after study baseline). Controls were women with a diagnosis of BBD who did not subsequently develop breast cancer. Details of this case–control study have been described elsewhere.45 These areas were cored (0.6 mm) and assembled into six TMAs. The TMAs were then sectioned and stained for AR protein expression  and four of six TMAs for ER protein expression . The stained TMAs were then reviewed by pathologists who estimated AR and ER expression as 0%, 1%, or in ordinal categories of 10% in the epithelial tissue in each core. The mean expression for AR and ER was then calculated across the three cores for each individual. The maximum expression and geometric mean across cores were also evaluated and yielded similar results (data not shown). Ki67 expression  was scored using Definiens Tissue Studio , an automated imaging analysis software program, which produced a 0–100% continuous estimate of expression. Women with low cellularity (<100 detected nuclei) for Ki67 expression were excluded from the analysis. Intraclass correlations were 0.66 for AR, 0.69 for ER, and 0.36 for Ki67, indicating some heterogeneity in expression across cores within individuals. Thirty-four individuals did not have evaluable AR expression in any of the three cores, leaving to a final sample size of 354 individuals (78 cases and 276 controls). Characteristics of participants selected into the nested case–control study with and without evaluable AR expression in normal tissue are shown in the supplement.For the identified cases and controls, study investigators requested H&E slides and later formalin-fixed paraffin-embedded tissue from the BBD biopsy from the diagnosing hospital. Slides were obtained for 463 cases and 1853 controls, and then within this group BBD tissue blocks were obtained for 177 cases and 719 controls. Three hundred eighty-eight participants with the following BBD lesion types were eligible for inclusion in tissue microarrays (TMAs): apocrine metaplasia, non-apocrine cysts, usual ductal hyperplasia, atypical ductal hyperplasia, and atypical lobular hyperplasia. For each of these participants, breast pathologists selected three representative areas of tissue containing normal terminal ductal lobular units (TDLUs).p value for the test of heterogeneity was then calculated using a likelihood ratio test for the AR-by-ER or AR-by-Ki67 product term. The associations between established breast cancer risk factors and AR expression  among controls were assessed using analysis of covariance (ANCOVA) adjusting for age at BBD biopsy. Nineteen controls (of 276) with 0% AR expression were reassigned to 1% (minimum detectable positive AR expression) for the ANCOVA analysis. A 0.05 level of significance was used for all statistical tests. All analyses were performed using SAS version 9.4 . As applicable, the analyses performed and reported are adherent to the REMARK recommendations for tumor marker prognostic studies.46AR expression was categorized into tertiles as defined by the distribution among the controls . As tissue from the BBD biopsy was not obtained from all members of case–control sets, unconditional logistic regression models adjusting for the study matching factors and type of BBD lesion were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for the association between AR expression in normal breast tissue and subsequent breast cancer risk. Other established breast cancer risk factors were considered as potential confounders, but were not included in final models, given their weak associations with AR expression and the limited sample size. Tests for linear trends were performed by fitting the regression models with a linear covariate taking the median AR expression level of each tertile. To evaluate the potential interaction between AR and ER or Ki67 expression, four categories were created by dichotomizing each distribution at the median level among the controls . The Supplemental Table 1


Evidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk.n = 33 790), the Breast Cancer Association Consortium  and the Genetic Investigation of ANthropometric Traits  were used for our analyses. In assessing causal relationships, four complementary MR techniques  were used to test the robustness of the results.Summary-level genotype data from 23andMe, Inc  estimated between BMI and breast size was high . All MR methods provided consistent evidence that higher genetically predicted BMI was associated with larger breast size  and lower overall breast cancer risk . No evidence of a relationship between genetically predicted breast size and breast cancer risk was found except when using the weighted median and weighted mode methods, and only with oestrogen receptor (ER)-negative risk. There was no evidence of reverse causality in any of the analyses conducted (P > 0.050).The genetic correlation (Our findings indicate a potential positive causal association between BMI and breast size and a potential negative causal association between BMI and breast cancer risk. We found no clear evidence for a direct relationship between breast size and breast cancer risk. The relationships between breast size, breast cancer risk and body mass index (BMI) were examined using summary-level genotype data from three publicly available sources.Genetic associations between these three factors were estimated using (i)genetic variants across the entire genome by linkage disequilibrium score segression, and (ii) only significantly associated variants as instrumental variables in two-sample Mendelian randomization analysis.Women with a genetic predisposition to high BMI are likely to have a larger breast size and a lower risk of breast cancer.There is no clear evidence for a direct and unmediated association between breast size and breast cancer risk. et al.,Observational studies suggest that breast size is related to breast cancer risk, although the evidence is not consistent ,https://github.com/bulik/ldsc (IJE online). Linkage disequilibrium scores provided by the software’s creators, based on the 1000 Genome Project’s European samples, were downloaded from the same website and cross-trait genetic correlation, rg, was computed from common SNPs between each pair of traits. After all preprocessing steps, there were 1 053 312 common SNPs for BMI and breast size, 992 833 common SNPs for BMI and all three breast cancer traits, and 1 116 435 common SNPs for breast size and all three breast cancer traits.LDSC, which relies on the adjustment for linkage between single nucleotide polymorphisms (SNPs), was used to estimate cross-trait genetic correlations between BMI, breast size and breast cancer risk .n = 77, explaining 2.2% of the variance in BMI)n = 7, explaining ∼1.2% of the variance in breast size)IJE online). All 77 BMI SNPs were present in the breast size and breast cancer risk datasets. All 7 breast size SNPs were present in the breast cancer risk dataset, but only 4 were present in the BMI dataset. A total of 113 breast cancer SNPs were present in the breast size dataset and 81 SNPs were present in the BMI dataset. Before performing MR analyses, the signs of the beta coefficients for each SNP across all outcome datasets were aligned to the effective alleles of the exposure datasets.Independent SNPs with genome-wide significant associations for BMI  package in R was used for pleiotropy and outlier SNPs detection for this dataset.To assess the MR assumptions, the MR-Egger test was used to detect directional pleiotropy of the genetic instruments, where a regression intercept that significantly differs from zero indicates the presence of directional pleiotropy or the InSIDE assumption (INstrument Strength Independent of Direct Effect) was violated (i.e. the associations with other traits that may affect the outcome via pathways independent of the exposure).et al.,MR was first performed using the inverse-variance weighted (IVW) method.rg) = 0.50, P = 3.89x10−43] (IJE online) but none of the other pairs of genetically predicted variables  was found by LDSC to be correlated (P > 0.050). As expected, the genetic correlations between the three types of breast cancer  were high  (IJE online).Genetically predicted BMI and genetically predicted breast size were correlated [correlation coefficient (IVW) [95% confidence interval (CI)]: 2.06 (1.80 − 2.35), P = 1.38x10−26, weighted median: 1.99 (1.64 − 2.42), P = 3.29x10−12], weighted mode  and MR-Egger  (P = 0.964) suggested an absence of strong directional pleiotropy and leave-one-out permutation analysis .A 1-SD increase in genetically predicted BMI was strongly associated with an increase in genetically predicted breast size {odds ratio , P = 9.44x10−6], ER-positive  and ER-negative  breast cancer risk  and none of the SNPs had an undue effect on the results . For the reverse direction, regression coefficients close to zero were obtained when all three risks were used as exposure, and all P-values were not significant  . One of the SNPs (rs17817449) appeared to have a stronger effect compared with the other SNPs from the leave-one-out analysis (IJE online), and this same outlier was also detected through MR-PRESSO, but removing it had a negligible effect on the results.In the IVW analysis, an increase in genetically predicted BMI was strongly associated with a decrease in overall  (weighted median: 1.32 (1.07−1.61), P = 0.008 and ORweighted mode: 1.44 (1.19−1.75), P = 0.009] , and no outliers were detected by leave-one-out permutation analysis  (IJE online).An increase in genetically predicted breast size was not significantly associated with breast cancer risk [ORctively] . The OR = 0.009] . No direanalysis . For the,weighted median: 1.16 (0.95–1.43), and ORweighted mode: 0.94 (0.73–1.23)].One SNP (rs7816345) was reported to be associated with both BMI and breast density, which may undermine the validity of the instrument for breast size.et al.,In this study, we found potential causal genetic evidence linking BMI to both breast size and breast cancer risk . Our fin,et al.et al.et al.et al. (OR = 0.65) but this difference could be due to the different datasets used as well as Guo et al.’s use of a genetic risk score as an instrumental variable instead of the beta-coefficients of each SNP that we used in our study.We also found through MR analyses a significant relationship between genetically predicted BMI and breast cancer risk. Higher genetically predicted BMI was found to be inversely associated with the risk of developing both ER-positive and ER-negative breast cancers. Some studies have suggested that BMI is more strongly associated with hormone receptor-positive cancers,,et al.IJE online). Also, MR analysis using breast cancer risk as the exposure and BMI as the outcome did not produce any significant associations. Thus, the association between BMI and breast cancer risk is mediated by only a limited number of SNPs that are significant for BMI but not for breast cancer risk.The positive association between BMI and breast cancer risk in postmenopausal women has been speculated to result from the higher tissue concentrations of oestrogen derived from the larger fat reserves of women with higher BMI.et al.Breast size is not a commonly considered breast cancer risk factor.Indeed, the relationship between breast size and breast cancer risk may be difficult to tease apart without the consideration of mammographic density, which is a strong, independent risk factor of breast cancer.It is important to note other limitations to our analysis. Firstly, due to the use of summary statistics, we were not able to stratify our analyses based on BMI or other breast cancer risk factors. This could have provided further insight into the nature of the relationship between these two traits. Secondly, the association between breast size and breast cancer risk could be driven by environmental factors that have no genetic component making it undetectable in this study. Thirdly, the seven SNPs were identified using a semi-continuous measurement of bra cup size to measure breast size.Our study is the first to address the potentially causal relationship between breast size and breast cancer using a MR approach. In summary, our results suggest that women with a genetic predisposition to high BMI are likely to have a larger breast size and a lower risk of breast cancer, but there is no clear evidence for a direct and unmediated association between breast size and breast cancer risk. Interestingly, the reverse causation of breast cancer risk to BMI, breast cancer risk to breast size and breast size to BMI were all not significant, which is in agreement with our intuition. The hypothesis that breast cancer risk is lower in women with both a larger breast size as well as a genetic predisposition for high BMI can be tested in future stratified studies.et al.This work was supported by the National Research Foundation Singapore Fellowship (NRF-NRFF2017-02) awarded to J.L. D.G.E is a NIHR Senior investigator. The body mass index association data were obtained from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, whereas the breast cancer genome-wide association meta-analyses, which generated the summary statistics used here, were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, The US National Institutes of Health , Cancer Research UK  and The European Union (HEALTH-F2-2009–223175 and H2020 633784 and 634935). All studies and funders for the breast cancer genome-wide association analyses are listed in Michailidou dyz124_Supplementary_MaterialClick here for additional data file.


BRCA1 or BRCA2. Forty-eight breast cancer patients that met the clinical criteria for BRCA testing but had received a negative BRCA1/2 result were recruited. Fifty-three genes previously implicated in hereditary cancer predisposition were captured using the BROCA Agilent cancer risk panel followed by massively parallel sequencing. Missense variants of uncertain clinical significance in CHEK2 were evaluated using an in vitro kinase assays to determine their impact on function. Pathogenic variants were identified in CHEK2, MUTYH, and RAD51B in four breast cancer patients, which represented 8.3% of the cohort. We identified three rare missense variants of uncertain significance in CHEK2 and two variants (p.Pro484Leu and p.Glu239Lys) showed markedly decreased kinase activity in vitro comparable to a known pathogenic variant. Interestingly, the local ancestry at the RAD51B locus in the carrier of p.Arg47* was predicted to be of African origin. In this cohort, 12.5% of the BRCA-negative breast cancer patients were found to carry a known pathogenic variant or a variant affecting protein activity. This study reveals an unmet clinical need of genetic testing that could benefit a significant proportion of at-risk Latinas. It also highlights the complexity of Hispanic populations as pathogenic factors may originate from any of the ancestral populations that make up their genetic backgrounds.Inherited pathogenic variants in genes that confer moderate to high risk of breast cancer may explain up to 50% of familial breast cancer. This study aimed at identifying inherited pathogenic variants in breast cancer cases from Puerto Rico that were not linked to  BRCA1 or BRCA2 are at substantially higher risk of developing breast, ovarian and other cancers1. For these women, risk reduction options include increased surveillance, chemoprevention and prophylactic surgery2. While the identification of a BRCA pathogenic variant constitutes a clear benefit for probands and their relatives4, a proportion of at-risk families do not present pathogenic variants in BRCA1 or BRCA2 and their genetic risk of breast cancer remains unexplained.Women with a pathogenic variant in PTEN, TP53, CHEK2, ATM, NBS1, RAD50, BRIP1 and PALB2, amongst others, have also been shown to confer moderate to high risk of breast cancer6. Recently, several commercial laboratories have replaced BRCA testing with extended panels of cancer susceptibility genes7. Yet, for many of the genes tested, there are no approved guidelines dictating the optimal prevention or treatment strategy should a loss of function variant be identified8.Significant progress has been made in the identification of inherited genetic factors underlying hereditary cancers. Pathogenic variants in 9. Although breast cancer (BC) incidence and mortality are lower compared to most ethnic groups in the US, the outcome and prognosis in Hispanic women are worse10. The prevalence and spectrum of BRCA variants varies significantly within Latin America and the Caribbean, with little overlap in the pathogenic variants most frequently reported in each country11. In Puerto Rico, our previous work estimated that a majority of the BRCA cases are explained by few recurrent founder mutations13. This study aimed at identifying hereditary cancer variants in BRCA-negative breast cancer cases from Puerto Rico using the BROCA panel of cancer predisposition genes14.The Hispanic/Latino population is the fastest growing ethnic minority in the US and is predicted to account for 35% by 2050BRCA testing, but had received a negative BRCA clinical test result. All study participants provided informed consent and received an orientation about genetics and hereditary cancers. Trained study personnel gathered a three-generation pedigree depicting family history of cancer. Participants whose grandparents had not been born in Puerto Rico were excluded. A complete surgery clinical pathology report was obtained for the tumor(s) of each participant. Not all tumors were evaluated by the same pathologist. A total of 97 women were interviewed, 56 (57.7%) met the inclusion criteria and agreed to participate. In addition, a sample containing a known BRCA2 c.3922G > T (p.Glu1308Ter) pathogenic variant was included as a blinded control. A cohort of 500 women from Puerto Rico that do not have a cancer diagnosis and that received a negative breast cancer screening mammography within 6 months of enrollment was used to assess frequency of variants of interests in the population of Puerto Rico.The study protocol was approved by the Institutional Review Board of Ponce Research Institute (#120829-JD) and Moffitt Cancer Center (#MCC17404) and all methods were performed in accordance with relevant guidelines and regulations. Participants were recruited between September 2012 and September 2014 from a private surgery practice located in San Juan, Puerto Rico. Evaluation of the potential study participants was conducted by a single surgeon. All women were diagnosed with breast cancer and met the NCCN  guidelines for BRCA1/2 pathogenic variants, comprehensive screening for large rearrangements was performed by multiplex ligation-dependent probe amplification (MLPA). The MLPA kits P002-D1 (BRCA1 NM_007294.3) and P045-B3 (BRCA2 NM_000059.3) and the protocol for the final assessment of allele dosage were used as described by the manufacturer 15.To ensure that all women included were true negative for http://tests.labmed.washington.edu/BROCA_versions) panel are listed in Supplementary Fig. Genomic DNA was extracted using Paxgene blood DNA Kit  and used for the preparation of paired-end libraries with 200 base pairs (bp) inserts. An oligonucleotide pool targeting exons, non-repeating introns and selected promoter regions of the genes of interest was used for enrichment (SureSelect). Following capture, samples were sequenced on an Illumina MiSeq. The total genomic region covered was 1.1 million bp (Mbp). The 53 genes of the BROCA , the Genome Analysis ToolKit (GATK)17. Sequence variants were annotated using ANNOVAR18. Additional contextual information was incorporated, including allele frequency in other studies such as 1000 Genomes , NHLBI Exome Sequence Project , and the Exome Aggregation Consortium . In silico functional impact predictions, and observed impacts were assessed from databases like ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) (release 9/2016), the Catalog Of Somatic Mutations In Cancer (COSMIC) (v68), RegulomeDB19, and FunSeq20. Analyses were performed using custom bash, Perl, R scripts, and VarSifter21. Variants distribution plots were realized using Lollipops (https://zenodo.org/badge/latestdoi/20224/pbnjay/lollipops). We obtained an average of 2.7 million reads per sample, 99% aligning to the human genome and an average depth of coverage of 83.9  with the Burrows-Wheeler Aligner (BWA)BRCA1 and BRCA222 and Insight for Lynch Syndrome genes23) were not further investigated (n = 17). For the remaining variants, those that had a frequency >0.5% in any of the gnomAD24 populations  were filtered from the analysis unless they had been reported as likely pathogenic or pathogenic on ClinVar25.Finally, the remaining variants were classified based on the ACMG criteria26. The frequency of likely pathogenic/pathogenic variants and VUS identified in NCCN clinically actionable gene was assessed in a cohort of 500 women that do not have cancer history by multiplex PCR using a Sequenom analyzer. In Silico predictions of functional significance was obtained for each non-synonymous variant using the following algorithms: fathmm27, MutationAssessor28, PolyPhen-229 and SIFT30.Each non-synonymous coding variant was assessed individually for classification into the following categories: (1) likely benign or benign, (2) likely pathogenic or pathogenic, (3) variant of unknown significance (VUS). This classification did not distinguish variants of high penetrance from those of moderate penetrance sometimes refered to as ‘risk factor’ in public databases. First, 16 variants that have been classified as benign or likely benign by expert panels  (primers available upon request). We also constructed two known pathogenic variants, p.His143Tyr and p.Ile157Thr, to serve as negative controls. Constructs were confirmed by Sanger sequencing.To determine the impact of Human embryonic kidney 293FT cells were transfected with empty pCMV2-FLAG vectors, or with expression vectors containing wild-type CHK2 or variants using Fugene HD . After ~48 h cells were exposed to 6 Gy of ionizing radiation (IR) and lysates were collected after 1 h in RIPA buffer . Levels of total and phosphorylated CHK2 in lysates were analyzed by immunoblotting on a 10% SDS-PAGE gel. The following antibodies were used: anti-phospho-Serine 516 CHK2 , anti-phospho-Threonine 68 CHK2 , anti-CHK2 (EMD Millipore) and anti-β-actin (Santa Cruz Biotechnology).2, 10 mM MnCl2, 1 mM PMSF, 40 μM cold ATP, and 13 μCi [γ-32P] ATP) and bacterially expressed GST-CDC25C peptide  as an exogenous substrate. Kinase reactions were incubated for 30 min at 30 °C. Samples were resolved on a 12% SDS-PAGE gel.For immunoprecipitation and kinase assays cell lysates were incubated at 4 °C for 2 h with anti-FLAG M2 affinity gel. Immunoprecipitates were washed three times with RIPA buffer and used for immunoblotting with anti-phospho-CHK2 and anti-CHK2. For kinase assay, the beads were incubated with Kinase Buffer , patients with variants in any other gene in the panel. Group IV , patients with no pathogenic variant or VUS found in the coding sequences : Group I n = 5; 10.2%), patients harboring at least one coding variant classified as likely pathogenic or pathogenic (L/Pathogenic) in any gene. Group II , patients with VUS in breast cancer susceptibility genes classified as “clinically actionable” by NCCN0.2%, patP = 0.8, Fig. To assess the prior probability of being carrier, each proband was grouped according to age of onset and/or family history of early onset breast cancer (at or before age 45). There was no significant difference in the distribution of carrier status after grouping probands by age of onset and/or family history of early onset breast cancer (BRCA2 p.Glu1308Ter carrier (blind control), which was properly identified, four probands in 48 (8.3%) were found to carry a pathogenic variant in a non-BRCA breast cancer gene  was diagnosed with breast cancer at the age of 50 and had a brother with early onset colorectal cancer. A truncating variant, p.Arg47* was identified in RAD51B predicted to result in a protein lacking ~90% of the coding region. This patient (PRI-136 III-1) was diagnosed with an invasive ductal carcinoma at age 33 and family history included early onset breast and colorectal cancer. Pedigrees representing family histories of patients carrying known pathogenic variants are depicted in Fig. The patient harboring a pathogenic variant in 25 and/or using ACMG26 criteria. The highest number of VUS was observed in ATM (n = 6)  Table , a clini25 or in the gnomAD24 databases. Despite a low observed prevalence in gnomAD, BLM p.Arg1407Lys and CDH1 p.Tyr523Cys were each present in two unrelated individuals in this cohort. Variants ATM p.Val2937Ala and BLM p.Ser854Asn, have been observed in the gnomAD Latino population exclusively . For this individual, the prior risk of being a BRCA carrier was estimated at 14.7% using the BRCAPRO model (data not shown).In 27.1% of the cohort (n = 13), no known pathogenic variant or coding VUS were identified. Among those, PRI-185 stands out for having strong family history of cancer including early onset breast cancer, ovarian cancer and colorectal cancer , and the other was predicted to be motif breaking for HDAC2 (3 probands). An MSH6 intronic variant was predicted to be motif breaking for ETS1, GABPA, and EGR1 (3 probands). A variant in the upstream promoter region of STK11 was predicted to be motif breaking for SP1 (1 proband). The promoter region of ATM harbored a variant predicted to be motif breaking for BCL11A and BCL3 (1 proband). Finally, a PALB2 promoter region variant is predicted to be motif breaking for MAX, MYC, and USF1 (1 proband).To determine whether our patients harbored potentially functional variants in addition to protein alteration, we examined variants with the two highest functional score using two annotation tools: RegulomeDB  and FunSeq . Of the six variants most likely to be functional by both methods, two were near CHEK2 locus, both carriers of the p.Ile157Thr pathogenic variant were homozygous for European ancestry , 14.9% (SD 12.2) and 10.8% (SD 7.4) for European, African and Native American ancestry, respectively , which represents over 8% of our cohort. In ClinVar, the p.Ile157Thr variant (VCV000005591.3) currently has conflicting interpretations with most submissions representing Likely pathogenic (8) or Pathogenic (17/20). The Ile157 residue is central in the FHA-kinase domain interface and functionally analyses of this variant by several independent groups indicates that the substitution generates a partially defective protein40. The p.Ile157Thr variant may confer a modest increase in unselected and familial BC risk (OR ~ 1.5) and a high risk (OR > 4) for lobular breast cancer, although risk estimates are not available for Puerto Rican women41.We identified four carriers with pathogenic or likely pathogenic 38, respectively, were shown to have marked decreases in kinase activity, and may constitute pathogenic variants. Although not central to its stabilization, the p.Glu239Lys is also located at the FHA-Kinase dimerization interface36. A change from a negatively to a positively charged amino acid might have an impact on CHK2 dimerization. Pro484 faces away from the dimerization axes but replacing proline may be reflected in a less rigid positioning of the alpha helix adjacent to the C-terminal end of the kinase domain36. Of note, although p.Arg519Leu, located in the NLS region, only displayed mild effects, our assay did not evaluate its cellular distribution. It is currently a variant of uncertain significance.Two missense VUS (p.Glu239Lys and p.Pro484Leu), located in the N- and C-terminal lobes of the kinase domainCHEK2 variants across ethnic groups have been observed in the literature42. The cancer spectrum in families of carriers of pathogenic or likely pathogenic CHEK2 variants based on functional assays, was consistent with the literature including breast, early onset breast, prostate, and colorectal cancers. Remarkably, two out of four families reported multiple cases of second primary or bilateral breast cancers , consistent with published meta-analysis reporting the association of CHEK2 with second primary breast cancers43. Potentially pathogenic CHEK2 variants were overrepresented in this cohort, suggesting that CHEK2 variants may affect the Puerto Rico population disproportionately. We have previously described a founder effect for a recurrent BRCA2 variant in Puerto Rico (p.Glu1308Ter)13, a phenomenon that is likely contributing to increased variant frequencies in this population.Significant differences in the prevalence of MUTYH, with a family history of early onset colorectal cancer consistent with the reported cancer risk for this gene. MUTYH-associated polyposis is inherited as an autosomal recessive trait44, with bi-allelic carriers demonstrating up to 28-fold increase in colorectal cancer risk45. Mono-allelic carriers have modest increases in risk of colorectal cancer, which was shown to differ in magnitude based on the variant47 and family history of colorectal cancer47. There is also evidence supporting a role of MUTYH in extra colonic tumor risk48, including emerging associations with the risk of BC, as two missense variants in MUTYH were shown to confer a significant increase in breast cancer risk in Sephardic Jews, a population in which the MUTYH mutation prevalence was high49. As of now, the data supporting the association between monoallelic MUTYH pathogenic variants and BC risk remains sporadic and additional evidence will be required before management guidelines are modified for this gene.One patient was heterozygous for a known pathogenic variant in RAD51B truncating variant p.Arg47* was observed in a family characterized by early onset breast cancer and colorectal cancer. Although a segregation analysis would be required to assess the carrier status of relatives with colorectal cancer, to our knowledge, there has been no report of the association of RAD51B variant with the familial risk of colorectal cancer. In a French study, RAD51B likely deleterious variants were observed in both breast-ovarian and breast-only cancer families50. GWAS have consistently identified the 14q24.1 genomic interval locus as a breast cancer susceptibility locus, which can be broken down into 2 signals mapping within or close to RAD51B. The first has been shown to be associated with triple negative phenotype and mammographic density51, and the second with male breast cancer risk52. When combined with GWAS evidence, our results suggest that RAD51B may be a candidate that warrants reconsideration for inclusion in breast cancer susceptibility gene panels. The local ancestry at the RAD51B locus in the carrier of p.Arg47* was predicted to be of African origin, which highlights the complexity of Hispanic populations in studying genetic basis to disease as pathogenic factor may originate from any of the ancestral populations that make up their genetic backgrounds. The identification of pathogenic variants in genes not commonly included in hereditary cancer testing is a reminder that negative results, especially in the context of strong family history, should be interpreted with care.A BRCA1/2 genes, previous studies have reported higher rates of novel mutations or VUS in historically understudied populations55. In the clinic, VUS are associated with additional challenges. In U.S community-based practices, clinicians reported less confidence in interpreting and counseling multigene panel testing for cancer risk assessment when compared to single-gene testing56. In a survey among members of the National Society of Genetic Counselors, only 63.2% felt their patients understood the meaning of a VUS result57. For counselees, VUS results are less understood58 and have been shown to be associated with psychological distress59. The BROCA panel was developed to provide comprehensive testing for hereditary cancers, and includes several genes that have not been associated with BC risk. While standardized panel simplifies genetic testing, it also results in a higher probability of identifying VUS, which remain challenging in the clinic.In the current study, over 46% of the study participants were found to carry a non-synonymous coding VUS. In the 61. To date, genome-wide association studies (GWAS) have identified over 100 loci associated with breast cancer risk62, accounting for up to 14% of the familial risk attributed to common variants63. Although the risk associated with individual GWAS loci is not elevated enough to inform clinical decisions, polygenic risk scores were proposed as risk stratification tools in population screening programs and targeted prevention67. It is possible that high-risk families for which no potential causative variant is identified share an excess of low penetrance variants.In 42.9% of the cohort, no potential causative variant was identified. Familial aggregation and twin studies have shown that even in non-hereditary breast cancer cases, a significant proportion of risk is inheritedBRCA1/2 negative clinical test result, patients of higher socio-economic status with better access to health care are likely to be overrepresented in this cohort. While the clinic where recruitment took place serves patients from all over the island, its location in the San Juan metropolitan area probably favored the recruitment of patients from the metropolitan area. The European contribution to the global genetic ancestry of the cohort was estimated at 74.3%, which is higher than the previously reported average of 63.7% for the Island in a census based population68. European ancestry in Puerto Rico has been associated with higher socio-economic status68. Consequently, having access to BRCA genetic testing, which was an inclusion criterion in this study, is likely to be correlated with a higher socio-economic status and might explain this enrichment in European ancestry.Although this is the first study addressing specifically hereditary cancer risks in an entirely Puerto Rican population, the sample size was limited. In addition, because the inclusion criteria required a 69. This is especially concerning, as it has been documented that rarer variants are more likely to show geographic clustering. In this study, we provide the first description of the pathogenic variants underlying BC genetic risk in Puerto Rican Hispanic women who tested negative for a BRCA mutation. Over 12% of the study participants had a known pathogenic variant or a variant that was predicted pathogenic by functional assay. For a large proportion of the genes included in hereditary cancer gene panels, clinical guidelines for management of risk have yet to be established pending the availability of accurate estimates of their respective cancer spectrum and the magnitude of associated risk.Our understanding of the genetic factors driving disease risk is biased towards populations of European descentSupplementary tables and figures


Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity. Taking individual risk factors beyond age into account should enable the classification of women into groups at varying risk of breast cancer. Personalised risk-based screening going beyond the current ‘one-size fits all' recommendation may increase the effectiveness and benefit-harm balance of breast cancer screening. Individualised risk prediction models for breast cancer are a key element to develop risk-based screening approaches since they are designed to quantify the risk that can predict whether an individual woman would develop breast cancer in a defined period.7 However, organised screening programmes do not use these models routinely. One reason for not including these models in screening context is the high uncertainty with regards to its applicability in screening settings. Also, the emergence of new risk prediction factors such as the expression of single nucleotide polymorphisms (SNPs) needs to be appropriately summarised before recommending one of the models into screening practice.A number of risk prediction models that include classical risk factors are commonly used in clinical contexts.Like any other source of information, risk prediction models have limitations that should be evaluated before using them. A rigorous risk of bias assessment of the existing individualised risk models is needed to clarify the overall quality and applicability of each model. Therefore, the aim of this systematic review is to update the existing evidence, conduct a critical appraisal and risk of bias assessment and summarise the results of the individualised risk models which are used to estimate the risk of breast cancer in women in the general population.8 and adhering to the PRISMA statement reporting recommendations.9 A predetermined review protocol was registered (CRD42018089842) in the PROSPERO database (date of registration 1 March 2018). The Patient, Intervention, Comparison, Outcomes (PICO) question of this systematic review is the following: Should individualised breast cancer risk prediction models vs. no risk prediction models be used to develop risk-based screening approaches for women in the general population?We performed a systematic review of the literature following the standard Cochrane Collaboration methodsWe retrieved relevant literature by using a combination of controlled vocabulary and keyword search terms in the following databases: (i) Medline (accessed through PubMed); (ii) The Cochrane Library; and (iii) EMBASE (accessed through Ovid). Terms related to breast cancer recurrence were excluded in order to avoid retrieving citations out of the scope of this systematic review. We adapted the search algorithms to the requirements of each database and used validated filters to retrieve systematic reviews and primary studies as needed. We reviewed references of included studies that could potentially fulfil our eligibility criteria. The detailed search strategy is reported in Supplementary table We searched primary studies of individualised breast cancer risk models searching each database from its inception up to February 2018.Eligible studies were those published in English that reported a model to estimate the individualised risk of breast cancer in women in the general population. We included models that assessed more than one risk factor and reported the quantitative characteristics of the risk prediction model. If multiple publications were based on the same individualised risk model, the most extensive report of the model in terms of risk factors reported was chosen. We excluded external validation studies that replicated previous models without adding any additional information such as a new design for collecting the inputs data, modifications on the risk factors or the risk model method.Articles identified from the search were loaded into EndNote X7.7.1 for Windows  and duplicates were removed.One reviewer screened the search results based on title and abstract, and a second reviewer performed a quality check of the study screening by reviewing 20% of the references. Two reviewers independently confirmed eligibility based on the full text of the relevant articles. In case of disagreement between researchers, the inclusion of studies was determined by consensus. We reported the result of this process with a PRISMA flowchart Fig. .Fig. 1PRWe used a predefined form to extract the following information from included studies: author, publication date, country, study design, the name of the model if available, sample characteristics, sample size, type of breast cancer, the method of analysis, and validation of the model. Data abstraction was conducted by one reviewer and checked by another.10 to assess the relevance and credibility of each risk prediction study and the following sources of limitations: (i) internal and external validation; (ii) bias due to the study design for risk estimates; (iii) limitations in data inputs; (iv) appropriateness of the model analysis; (v) reporting bias; (vi) interpretation bias; and (vii) conflict of interest. The risk of bias for each domain was rated as low, high or unclear. For systematic reviews we used the AMSTAR 2 critical appraisal tool.11Two reviewers carried out the assessment of the risk of bias independently and final quality assessment was based on consensus. We used the ISPOR-AMCP-NPC QuestionnaireWe evaluated the model validation by assessing both the discriminative power and the calibration accuracy estimated for the women in the general population. When available in the included publication, we extracted the area under the receiver operating characteristic curve (AUROC), the net reclassification index (NRI) and the expected observed (E/O) ratio. The NRI was not included in the tables because it was only reported in 2 out of 24 articles. The characteristics of the included models and the risk prediction outcomes reported preclude the possibility to pool data across studies. Therefore, a narrative synthesis has been conducted. Key study characteristics, validation and accuracy of individual risk models, and methodological quality are described in tables and summarised in a narrative manner. Results are presented according to the original model that they reported.7 which we used as a source of primary studies. In addition, two studies were included after a manual inspection of papers’ references.13 After the full text was checked, 24 studies35 met the inclusion criteria and were considered in the evidence synthesis. Details about study inclusion with reasons for exclusion are described in the flow-chart  because it provides information of both models and presents its results separately. A brief summary of the 24 included studies is presented in Table 22 After this first publication, eight studies were identified that were based on BRCAT model but modified the data collection design, assessed additional risk factors or changed the statistical method. In addition to the five risk factors proposed in 1989, other variables such as body mass index (BMI), weight, hormone replacement therapy (HRT), alcohol consumption, physical activity, diet, breast density, atypical hyperplasia, breast inflammatory disease, parity, a polygenic risk score or hormones information have been included in updated versions , the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, the International Breast Cancer Intervention Study (IBIS), and other original models. The study by Zhang et al.23 Similarly, the AUROC reported by Boyle et al.16 and Matsuno et al.25 were 0.60 and 0.61, although these authors added BMI, HRT, alcohol, physical activity and diet, and ethnicity into the model. Zhang et al.13 with the new variables reach an AUROC of 0.65 and Tice et al.30 reported in 2005 a higher AUROC value of 0.68 which was obtained just adding breast density to the original five risk factors , and ethnicity into the model in 2008 and obtained a value of 0.66 for the AUROC.19 The addition of oestradiol levels to the model was tested by Rosner et al. who obtained a 0.65 AUROC value in 2008.28 Finally the addition of a polygenic risk score, mammographic density and endogenous hormones by Zhang et al.13 reached a 0.68 AUROC value ,38 and it updates the results of a previous systematic review published by Anothaisintawee et al. in 2012.7 In contrast to these reviews, however, our aim was to provide innovative information regarding the quality of the identified prediction models. Thus, we have identified and rigorously analysed the strengths and limitations of 24 individualised models in order to adjust our conclusions to the quality of the evidence.In this systematic review, we found that the number of individualised breast cancer risk prediction models has increased steadily over the past three decades. This finding is in agreement with the narrative overview published by Cintolo-Gonzalez et al. in 2017,7 we found that in contrast to the BCRAT and Rosner & Colditz models that were the most frequently cited models up to 20107 the BCSC model has concentrated the attention of several authors during the last five years, although its discriminatory accuracy has not dramatically improved. Second, none of the models in the review of Anothaisintawee et al.7 included genetic information as a risk factor. By contrast, we have identified four models including genetic information: the IBIS model33 that includes genetic phenotype in their updated version, the BCSC model that includes a polygenetic score in both 201512 and 201629 publications, as well as the article by Zhang et al. that added a polygenic risk score to both the BCRAT and the Rosner & Colditz models.13We have identified two new trends with regards to the use and development of the models, which are the increased use of the BCSC model and the inclusion of common genetic variation in the prediction models. As compared to the information published in the review of Anothaisintawee et al.,13 also reported an improvement in the net reclassification index (NRI) of the BCRAT, and Rosner & Colditz models, as well as in the BCSC model, respectively.Most of the included studies reported the AUROC to determine the probability that a randomly chosen woman with disease would be correctly categorised as higher risk compared to a randomly chosen woman without disease. The discriminatory accuracy estimate does not express whether the model is more or less accurate in predicting the risk of specific individuals but measures the capacity of the model to determine which women are at higher/lower risk for developing breast cancer. Thus, both calibration accuracy and discriminatory accuracy should be assessed. Contrary to what is expected, we found that authors reported the E/O ratio only in less than half of the included studies. In addition to the AUROC value, the studies of Zhang et al. and Vachon et al.39 Thus, the verdict on risk models should not be based solely on these estimators. Instead, they need to be prospectively evaluated in clinical trials. In fact, there are currently two very large randomised trials assessing risk-based screening strategies. Both of them are using individualised models. Both the IBIS and the BCSC models are being tested in the European trial MyPeBS .40 Also, the BCSC model is being tested in the US WISDOM trial (Women Informed to Screen Depending On Measures of risk).41Overall, the information provided by the AUROC and the E/O ratio was consistent suggesting that the included models have moderate discriminatory accuracy and calibration accuracy when applied to the women in the general population. Nevertheless, it must be taken into account that despite the great importance of validation in terms of AUROC and E/O ratio, the presence of low values of AUROC or clearly different from 1 values of the E/O ratio does not mean that these models are useless. On the contrary, models are clinically useful even with moderate AUROC since they can reclassify individuals at the extremes of risk.42 These differences, the nature of breast cancer itself and its low incidence may condition a low discriminatory accuracy of a model. In other words, in the general population, there is a low probability of having breast cancer (even in the highest risk group). This low probability may mean that the discriminatory power of a breast cancer risk model won’t be as high as a risk model targeted to other common diseases such as cardiovascular events, for instance. Another potential limitation in the applicability in the screening context is the completeness and the number of included risk factors, which ranged from five to 18. Nevertheless, some potentially relevant risk factors such as genetic markers have been only included in few models. Recent studies44 have shown that adding genetic information as a risk factor can increase the discriminative accuracy of the different models which opens the line for further evaluation. An evaluation that should first assess the calibration of these models in prospective cohort studies.The distribution of risk factors in such different populations may affect the applicability of the models to different contexts. The fact that different subtypes of breast cancer may have different genetic markers is widely accepted.2 The presence of some mammographic features in these screening mammograms may be related to the risk of developing breast cancer, as has been recently pointed out by some authors.45 Only one of the 24 models identified in this systematic review included microcalcifications and masses found at mammography as risk factors in the model.21 Time-changing variables such as radiological variables may not be as stable as personal history. However, in a screening context, this information is especially relevant because it is easily available from previous screening examinations.Overall, women are usually screened using mammography. Particularly in Europe, most programmes invite women for screening every 2 years.We found variability in the design of the studies that were used to obtain the cancer risk estimates. Notably, the study design used in the BCSC model was a cohort, which is a robust epidemiology design that allows developing and validating prediction models. Another frequently used design was the case-control study, nested or not. Contrary to the cohort study, time-changing variables may not be well obtained in case-control studies.46 The Rosner-Colditz model has also been validated in several studies, one of the most complete validations being the one performed in 2013 by the authors themselves.37 On the other hand, we found that although the Eriksson et al.19 model reports the highest AUC (0.71), this model has not been externally validated, which increases the uncertainty about its applicability.Regarding the external validation, the models showed some limitations given that few of them were further evaluated in different contexts. As far as we know, there are numerous scientific publications reporting external model validation in different settings and countries. These studies may help to understand the performance of a model in a specific context, but this issue was out of the scope of our review and, therefore, we have not included external validation studies. As an example of the relevance of these studies, we can inform that the BCRAT model has more than 50 articles informing the external validation of these models in different countries.Also, there were limitations in data inputs, mostly due to the fact that in several models the information was provided by self-reported questionnaires that may affect the accuracy of the results. Finally, there is a limitation when comparing the AUROC or E/O ratio across the models given that there is great heterogeneity amongst them. The models were targeted to different populations, included different sets of risk factors, and often used different methodologies. We have taken into account all these variations and presented the results by model categories.47 and BRACAPRO48 were not included in this review because they are aimed at high risk women and not useful for women in the general population in the screening context. Full-text screening and data abstraction process were performed by two researchers, which increase the quality of the review process. Moreover, as far as we know, this is the first review assessing the risk of bias of the identified risk prediction models.This systematic review was limited to studies published in English and did not involve an active search for grey literature, which is literature that is not formally published in sources such as books or journal articles. Therefore, some models may not have been identified. However, since we have conducted a comprehensive literature search in Medline, EMBASE and The Cochrane Library, we estimate that the loss of information due to the study selection criteria is low. Some key genetically oriented models, such as BOADICEA21 was identified. Currently, it is still a challenge to recommend any of the models as the standard for predicting individual risk in screening context. However, the models have been updated by adding new variables, such as common genetic variation or radiologic variables and have shown improvements in their quality as well as in their discriminative accuracy. These new variables need further evaluation to confirm its promising impact in the prediction capacity to propose personalised strategies for breast cancer screening.The development of individualised breast cancer risk prediction models has increased over the last three decades, but the improvements in both the discriminatory power and calibration accuracy are still limited. Despite the time that has passed since the first model was published and a large number of available publications, only one model addressed to women attending a population-based screening programmeSupplementary data


DNA methylation has been implicated in breast cancer aetiology, but little is known about whether reproductive history and DNA methylation interact to influence carcinogenesis. This study examined modification of the association between global DNA methylation and breast cancer risk by reproductive characteristics. A population-based case-control study assessed reproductive history in an interviewer-administered questionnaire. Global DNA methylation was measured from white blood cell DNA using luminometric methylation assay (LUMA) and pyrosequencing assay (long interspersed elements-1 (LINE-1). We estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) among 1 070 breast cancer cases and 1 110 population-based controls. Effect modification was assessed on additive and multiplicative scales. LUMA methylation was associated with elevated breast cancer risk across all strata (comparing the highest to the lowest quartile), but estimates were higher among women with age at menarche ≤12 years  compared to >12 years . We observed a 2-fold increase in the LUMA methylation-breast cancer association among women with age at first birth >23 years  versus ≤23 years . No modification was evident for parity or lactation. Age at menarche and age at first birth may be modifiers of the association between global DNA methylation and breast cancer risk. LINE-1 m∑T)]*100., 28. MetThe reproductive characteristics of interest for our study were age at menarche, parity, age at first birth, and lactation. These variables were collected as part of the main 100-minute interviewer-administered questionnaire, which included a reproductive calendar to enhance recall . For theUsing unconditional logistic regression we first estimated odds ratios (ORs) and 95% confidence intervals (CI) for the associations between reproductive characteristics and global methylation levels, among control women only , given previous evidence that women with breast cancer (compared to women without breast cancer) are likely to have lower global methylation levels . Then, uWe estimated the odds of breast cancer within quartiles of LUMA and LINE-1 global methylation markers and within strata of each of the four reproductive characteristics. For associations stratified by age at menarche and parity, models included all women (n = 2 180), but for age at first birth or lactation, models included parous women only (n = 1 916). Quartiles (Q) of LUMA and LINE-1 methylation were categorized based on distribution of the percent methylation among controls. Given that LUMA, a global measurement of promoter methylation, was positively associated with overall breast cancer risk in our study population (9), we used the lowest quartile as the referent category. In contrast, LINE-1 hypomethylation is hypothesized to be a marker of decreased genomic integrity, we therefore used the highest quartile of LINE-1 as the referent category.a priori p-value <0.05). Additive interactions were evaluated using a common referent and the relative excess risk due to interaction (RERI) , [S2 Table].As shown in  the association between LINE-1 methylation and breast cancer risk (comparing Q1 vs. Q4) did not appear to differ between those with age at menarche ≤12 years of age  and those who reported an age at menarche >12 years .As shown in Table 2). Similarly, parity did not appear to modify the association between LINE-1 methylation and breast cancer risk (Table 3).The association between LUMA methylation and breast cancer did not appear to differ substantially between strata of parity. When comparing the highest to the lowest quartile, we observed similar ORs between LUMA methylation and breast cancer risk among nulliparous  and parous  women . By comparison, among women reporting early age at first birth (≤23 years) the association with breast cancer among women in the highest LUMA methylation quartile was less pronounced and included the null value . This effect modification by age at first birth on the LUMA methylation-breast cancer association was evident on the multiplicative (pinteraction = 0.02) and additive scales . L. L,[35].,. To our ,. In cont, which maquintile 5 vs.1 = 95%CI = 1.83–3.16) comparing quintile 5 vs. 1,[Contrary to our findings among control women, data from the EPIC-Italy sub-cohort showed that for each yearly increase in age at menarche, the odds of having genome-wide methylation (assessed by LUMA) below median level was increased by 32%  [ 5 vs. 1, and stil 5 vs. 1,. While t 5 vs. 1,. Our datThe divergence of the epigenome as a function of age due to stochastic changes in methylation  mayOur study has many strengths, namely the availability of global methylation assessments in a well-characterized population-based case-control study with a relatively large sample size. The study included detailed exposure assessment, particularly for reproductive history. Questionnaires were administered by trained interviewers using validated methods to mitigate recall error, however,Using resources from a large population-based case-control study, we observed that high methylation levels, assessed using the LUMA platform, were differentially associated with breast cancer risk among women with an earlier age at menarche and a later age at first birth. This study provides etiologic insight into how age-related reproductive factors may influence breast cancer risk through its interaction with the DNA methylome. Our findings may also help in the identification of an early biomarker among women who are at an increased risk of breast cancer based on their reproductive history.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Premalignant breast lesions pose variable risks for transformation, raising the question who should receive treatment to counteract the potential progression to breast cancer. Because the secreted metastasis mediator Osteopontin (OPN) is a marker for breast cancer aggressiveness, its presence in these lesions may reflect progression risk.By immunohistochemistry, we analyse the association of Osteopontin variant expression in healthy breasts, hyperplasias, papillomas, and carcinomas in situ from 434 women to assess a) staining for OPN exon 4 (present in OPN-a and OPN-b) or OPN-c in low-risk to high-risk lesions b) correlations between staining and progression  or survival.The markers correlate with risk, and they are prognostic for ensuing invasive disease and survival. About 10% of OPN-c pathology score 0–1 (intensity), vs. 40% of score 3 experience cancer over 5 years. More than 90% of women, who progress, had pathology scores of 2–3 for OPN-c intensity at the time of initial diagnosis. When combining OPN-c and OPN exon 4 staining, all of the low intensity patients are alive after 5 years, whereas women in the high category have a close to 30% chance to die within 5 years. Of patients who succumb, close to 80% had a high combined score at the time of initial diagnosis.The combined information of OPN splice variant immunohistochemistry can provide a foundation for very reliable prognostication and has the potential to aid decision making in the treatment of early breast lesions. It is difficult for the individual patient to make that choice because there are no predictors for her specific progression risk. A molecular diagnostic that informs the patient whether she is at high or low risk for developing breast cancer can facilitate the decision on follow-up treatment. Distinguishing high-risk patients from low-risk patients will improve the prognosis of the former group (through early decisive intervention) and spare unnecessary treatment for the latter group (through watchful waiting).The progress achieved in imaging and detection over recent years has generated a relatively new dilemma in breast disease: Which patients with premalignant lesions should receive treatment to prevent the future development of breast cancer? While such changes are present in about 5% of disease-free women, their clinical significance is uncertain as not all cases progress.4), papillomatosis or lobular carcinoma in situ (LCIS) with moderate risk for transformation. Non-invasive, but potentially precancerous lesions are called ductal carcinoma in situ (DCIS). DCIS is characterised by the proliferation of transformed epithelial cells within ducts, which are surrounded by an intact basement membrane. There is a 30–50% risk that DCIS (stage 0), if not treated, will progress to locally invasive breast cancer and then to metastatic breast carcinoma (stage III). The acquisition of invasiveness is a critical step in these early breast carcinomas. It is associated with the aberrant expression and splicing of specific tumour progression genes that allow the cells to penetrate the basement membrane.5 While there is a substantial need in breast cancer progression to identify biomarkers for the sequence: hyperplasia → atypia/papilloma → DCIS → DCIS with microinvasion → invasive ductal cancer (IDC), or alternatively from atypia via LCIS to invasive lobular cancer (ILC), current breast histopathology does not allow the reliable diagnosis of this invasive potential.Normal histology or usual ductal hyperplasia put a patient at low risk for developing breast cancer. Early stages of breast transformation develop from hyperplasia to atypia , atypical ductal hyperplasia (ADH)7 However, the gene product is subject to alternative splicing selectively in cancer, which deletes exon 4 (to generate Osteopontin-c) or exon 5 (to generate Osteopontin-b) from the unspliced form . The variants have distinct pathophysiological functions in cancer progression and convey distinct information on the disease. We have previously investigated the predictive capabilities of splice variants for treatment responses8 and their prognostic potential for cancerous lesions.10 A prior meta-analysis confirmed pan-Osteopontin to be correlated with premalignant progression in breast and other transformations.6 Here we analyse the prognostic value of OPN splice variants in mammary tissue at the premalignant stage. The variant forms are distinguishable by antibodies to exon 4, recognising OPN-a and OPN-b, or to the splice junction of OPN-c respectively.Biomarkers are important for guiding the diagnosis and management of growths in the breast. Two broad groups of biomarkers comprise prognostic markers and predictive markers. Prognostic markers allow forecasts regarding the natural course of the disease. They differentiate between patients likely to have a good vs. a poor outcome. By contrast, predictive markers provide upfront information regarding how likely a patient is to benefit from a specific treatment, and hence may guide the choice from available therapies. Two of the most critical questions in breast cancer, for which there is a paucity of suitable biomarkers, comprise the prediction of treatment responses and the prognostication which premalignant breast lesions will form cancer. The cytokine Osteopontin  has been extensively studied as a metastasis gene. It constitutes the most abundantly secreted phospho-protein in breast and other cancers and supports invasive behaviour. As such, it is a biomarker for breast cancer aggressiveness and for breast cancer prognosis . In older studies, pan-Osteopontin  was measured.This study investigated biopsies from a total of 434 women with premalignant breast lesions Table ; compris11 For each antibody, the tissues were scored according to intensity  and percent positivity , separately for nuclei and cytoplasm. In addition to analysing the indicators in their original scale, we dichotomised the immunohistochemical biomarkers into low (0–1) or high (2–3). We have previously found this method to strengthen the power of the analysis.8 All microscopic slides were independently evaluated by two pathologists, and in the rare cases of discrepant initial scores, a final score was agreed on after discussion.For each antibody, a formalin-fixed and paraffin-embedded biopsy specimen from premalignant lesions were cut on a microtome in 5 μm slices. The antibodies used in this study, after blocking in 2% donkey serum, were anti-hOPN-c IgY , and LF161 (Larry Fisher). The IgY antibody recognises the Osteopontin-c splice junction and detects the molecule in immunohistochemistry. It was diluted 1:500 to 1:700. The polyclonal rabbit antibody LF161 for staining selectively exon 4 (present in Osteopontin-a and -b) was used at 1:1000. The antibodies and their use in immunohistochemistry have been thoroughly validated.2 test was applied. The primary methods for addressing survival time (duration) and prognosis (ensuing invasive disease or death) was Kaplan Meier for univariate analysis. A multivariate analysis of those factors with a p-value of less than 0.05 were then applied to a Cox proportional hazard model. The hazard ratio (HR) measures the hazard between two individuals, whose value of the independent variable differ by one unit (if continuous) or moving from one class to another class .Statistical analyses were conducted using MedCalc version 14.8.1. The pathology scores assess staining intensity and percent positivity. The predictors were each categorical or dichotomised (pathology scores 0 and 1 = low vs. 2 and 3 = high). A second analysis included the risk group. For evaluating differences in biomarkers among the risk groups  a χFor biomarker development, we devised ROC curves using all parameters (intensity and percent positivity of the immunohistochemistry stains plus risk groups). To model outcome . We used the formulaX indicating the parameters measured  and β being the coefficient for the regression . The log odds are a linear function of the covariates.with 12). The 198 patients with DCIS  have an elevated risk to develop breast cancer. The risk level of radial scar is not fully characterised; 9 patients with this diagnosis were included in the overall evaluation without assignment to risk groups  and 60 had usual ductal hyperplasia. These two groups are considered to be at low risk for progression. Atypia/ADH (74 cases), papilloma/papillomatosis intraductalis 19 cases) and LCIS (20 cases) comprise an intermediate progression risk  Fig. . The mar10). Among the risk groups, expectedly, DCIS was associated with the highest probability of developing breast cancer compared to PI, ADH, and LCIS . OPN-a/b intensity had a p-value of 0.0220 and a hazard ratio of 1.4456 (95% confidence limits 1.0564–1.9783). By contrast, the values for risk were p-value 0.7185, hazard ratio 0.9472 (95% confidence limits 0.7064–1.2702). This suggests that the OPN variant forms are biomarkers for progression hazard, also for lesions that are conventionally categorised as low risk. The markers may be of particular benefit in assessing the need for treatment in non-DCIS premalignant lesions.Follow-up information had 214 patients with non-recurrence over various observation periods  and 55 patients (20%) experiencing breast cancer over 3–5 years (48 patients had insufficient follow-up duration or died from other causes and were excluded). The data identified OPN-c intensity scores 2–3 as stronger predictors for progression than intensity scores 0–1 for all types of lesions analysed  reflected them as prognostic. The pathology scores were higher for OPN exon 4 as well as for OPN-c in patients who succumbed to breast cancer compared to those who over at least 5 years did not. When combining OPN-c and OPN exon 4 staining intensity on a scale of low (pathology scores for both markers 0–1), intermediate (one marker 0–1 the other 2–3) and high (both markers 2–3), the prognostic accuracy improved such that all of the low patients were alive after 5 years, whereas women in the high category had a 30% chance to die within 5 years . Close to 80% of patients who succumbed had a high score at the time of initial diagnosis Fig. . In the We analysed patients who incurred breast cancer within 5 years in comparison to those who were free of relapse for at least 5 years following the initial diagnosis. More than 90% of women, who experienced breast cancer had had pathology scores of 2–3 for OPN-c intensity at the time of initial diagnosis. About 2.5% of women free of OPN-c (intensity pathology score 0), and 7.5% of OPN-c pathology score 1 progress over 5 years. This risk increases to 24% at pathology score 2 and 40% at pathology score 3. However, OPN exon 4 was less informative than OPN-c , so that combining the two markers yielded modest improvement over OPN-c intensity alone Fig. .According to ROC curves, a logistic regression algorithm that applies the pathology scores as categorical variables and the dichotomised risk group (low or medium vs. high) achieves better sensitivity and specificity for the prognostication of death from breast cancer Fig. , as wellAll DCIS cases underwent surgical resection (16 patients had mastectomies). A fraction of them was further treated with tamoxifen (12), radiation 39) or both (41). While the sizes of the subgroups preclude conclusive assessments due to lack of statistical power, the trends suggest that the prognostic value of OPN-c/exon 4 is insignificantly affected by ensuing treatment, and that within each treatment group  the biomarker may be able to distinguish high vs. low risk for invasive disease . While OPN-a and OPN-c may synergise in tumour progression,15 The early stages of breast transformation (atypias) are difficult to differentiate from benign growths (hyperplasias) on one end of the spectrum and invasive carcinomas in situ (CIS) on the other end. Furthermore, the inspection of tumour margins to assess invasiveness is unreliable and it requires step sections through the entire biopsy material. Microinvasion is typically identified at surgical biopsy, as core needle tissue cannot enable this distinction. Hence, there is an intense need for reliable biomarkers. We have analysed biopsies, and there is a possibility that an invasive component was present, but cryptic to histologic analysis at that time . The independent variable OPN-c/exon 4 enables the assessment of risk regardless of the type of premalignant lesion present. Because its staining intensity is indicative of invasive potential, it allows histopathologic evaluation even when the margins cannot be inspected. The prognostic biomarker is not dependent on the two most common constraints, histologic type and margins of the lesion. Its value lies in being able to examine invasive potential, regardless whether an invasive component has been missed in the biopsy or invasion has not yet occurred.Available diagnostic techniques in breast cancer prevention involve a biopsy, where samples of tissue are taken to confirm or eliminate the presence of transformed cells by histopathological examination. While this procedure is a standard at present, it lacks indicators for the detection of invasive potential. Most biopsies are obtained with a core needle, and sampling or interpretation error may understate the disease identified.17 Estrogen receptor (ER) and progesterone receptor (PR) facilitate decisions on therapy, but are weak prognostic measures.18 HER2 over-expression is associated with poor prognosis, but HER2-based mechanisms underlie only about one third of breast cancers.While research has recognised a large number of biomolecules to be deregulated or defective in breast cancer, relatively few of them are commonly used in histological diagnosis. Specifically, markers that predict invasiveness have not been firmly established, and they have been sorely absent from utilisation in premalignant lesions. Among the accepted molecular indicators, the U.S. FDA-approved and ASCO-recommended tumour markers CA15.3, CA27.29, and CEA are useful only for monitoring the therapy of advanced breast cancer or its relapse. These serum markers still lack the adequate sensitivity (below 25%) and specificity (below 70%) to be applicable in diagnosing early stage breast carcinoma in a large population.19 no such tests have been developed for precancerous lesions.Genetic signatures have been used to assess risk in more advanced lesions. The oncotype Dx relapse score involves 21 genes. It has prognostic utility for relapse and survival in tamoxifen-treated, node-negative, ERα-positive cases. It permits the identification of a subgroup with sufficiently low residual risk to safely omit chemotherapy. It may also be informative in low nodal positivity, identifying patients who can benefit from anthracycline-based chemotherapy. The MammaPrint signature comprises 70 genes that identify risk for relapsing within 5 years. It is applicable to node-negative, ER-negative patients. High risk women benefit significantly from the addition of chemotherapy to endocrine treatment. Although subject to active research,20 the value of indicators can be improved by using the presence of molecules that are essential for invasion through tissue barriers, which constitutes a critical transformation step. Published reports include the polycomb group transcriptional repressor EZH2, which is elevated in invasive breast carcinoma compared with normal breast epithelia.21 VEGF correlates with uPA in the node-positive population, and patients with high VEGF levels display poor outcome, with an increased risk for the node-positive subset.22 Neither of these potential molecular indicators has been developed for routine diagnostic use. Further research will indicate whether combining OPN splice variants with other candidate markers can further improve their prognostic potential in premalignant breast disease.While the proliferation marker Ki-67 has shown some prognostic potential for risk assessment in premalignant breast disease,Supplement


Estrogens play a significant role in breast cancer development and are not only produced endogenously, but are also mimicked by estrogen-like compounds from environmental exposures. We evaluated associations between estrogenic (E) activity, demographic factors and breast cancer risk factors in Non-Latina Black (NLB), Non-Latina White (NLW), and Latina women. We examined the association between E activity and Indigenous American (IA) ancestry in Latina women. Total E activity was measured with a bioassay in plasma samples of 503 women who served as controls in the San Francisco Bay Area Breast Cancer Study. In the univariate model that included all women with race/ethnicity as the independent predictor, Latinas had 13% lower E activity (p = 0.239) and NLBs had 35% higher activity (p = 0.04) compared to NLWs. In the multivariable model that adjusted for demographic factors, Latinas continued to show lower E activity levels , but the difference between NLBs and NLWs was no longer statistically significant (p = 0.431). An inverse association was observed between E activity and IA ancestry among Latina women  consistent with our previously reported association between IA ancestry and breast cancer risk. These findings suggest that endogenous estrogens and exogenous estrogen-like compounds that act on the estrogen receptor and modulate E activity may partially explain racial/ethnic differences in breast cancer risk. Epidemiological studies have consistently reported that endogenous sex hormones play a critical role in the etiology of numerous diseases including breast cancer –4. InterChanges in lifestyle and demographic factors can influence endogenous estrogen levels through changes in adiposity. Across racial/ethnic groups, multiple studies have consistently identified a positive association between body mass index (BMI) and estrogen levels , 12]. I. I12]. IVarious methods are available to quantify estrogen receptor (ER) function and activation. Luciferase assays have been widely used and have proven useful in predicting breast cancer risk , 19]. G. G19]. GIn a previous pilot study among foreign-born and U.S.-born Mexican women who participated as controls in a population-based case-control study of breast cancer, we found that plasma estrogenic (E) activity was associated with genetic ancestry and years of U.S. residence , suggestParticipants were selected from the control group of the San Francisco Bay Area Breast Cancer Study (SFBCS), a population-based case-control study in Latina, NLB, and NLW women . ControlIn the present study, we measured E activity in plasma samples of 503 women  from the control group of SFBCS. Eight percent of the participants were premenopausal and equally distributed among the different racial/ethnic groups. To prevent exposure misclassification of exogenous estrogen use, we excluded women who were using hormone therapy at the time of blood draw. Participants who had one or more missing covariates were excluded from the analyses .All study participants provided written informed consent. The study was approved by the Institutional Review Boards at the University of California, San Francisco and the Cancer Prevention Institute of California.Plasma hormone measurements were performed at the Reproductive Endocrine Research laboratory at the University of Southern California under the direction of one of the contributing authors (FZS). Methods are described elsewhere . BrieflyWe utilized a receptor-mediated Chemical-Activated Luciferase gene eXpression (CALUX) assay for the assessment of total E activity profiles in human plasma, which captures levels of both endogenous and exogenous estrogenic compounds. Methods followed procedures as previously described , 21]. B. B21]. Bβ-1]*100. Statistical significance was set at p<0.05. All statistical analyses were performed using the program Stata [All hormone values were natural log (ln) transformed to approximate normal distribution. Differences in means or proportions for all analyzed variables between racial/ethnic groups were assessed using two-sided t-tests and Fisher’s exact tests, respectively. Linear regression was used to analyze the relationship between ln-transformed and plate adjusted E activity (dependent variable) and race/ethnicity (main predictor). Plate adjusted values were obtained by first estimating average plate effects using linear regression and then subtracting the average plate effect from each individual value. Percent change in RLUs per unit change of predictor variables was calculated using the formula [eam Stata .2), neighborhood socioeconomic status (SES)  geocoded to a Census 2000 block group, alcohol intake during the calendar year before selection into the parent study , and E activity . Selection of these variables was based on factors we previously hypothesized could affect E activity at time of blood draw [The present study assessed differences in E activity by race/ethnicity and tested associations between E activity and various factors that were hypothesized to affect E activity levels . Multivariable regression models included several independent variables such as race/ethnicity , age at blood draw , height , body mass index (BMI) at blood draw . We ran multivariable models adjusting for reproductive variables, but as we had previously hypothesized, they did not have an effect on E activity at blood draw, which for most women occurred many years after the individual’s last pregnancy. Therefore, final models do not adjust for reproductive variables. Additionally, because the average age at blood draw for all three racial/ethnic groups was over the age of 60 years, which is about 10 years after the average age of menopause in U.S. women, final models did not adjust for age at menopause.Descriptive characteristics by racial/ethnic groups are presented in In the univariate model , LatinasRestricting the analysis to Latina women with information on genetic ancestry (N = 276), we found that in the univariate model higher IA ancestry was associated with lower E activity . This fiOur study shows that there are significant differences in E activity in women across three racial/ethnic groups, partly due to differences in BMI (in the case of NLWs vs. NLBs) and potential exposure to exogenous estrogen-like compounds (NLWs vs. Latinas). Results also suggest that higher IA ancestry in Latina women is associated with lower levels of E activity, which is consistent with the observation that Latina women with high IA ancestry have lower risk of developing breast cancer.There was an association between BMI and E activity in all racial/ethnic groups. In NLB women, higher E activity was mostly attributed to BMI. After inclusion of the BMI variable in the multivariable model, the level of E activity among NLBs was no longer statistically different from NLWs. The attenuation of the positive association between E activity and BMI after adjusting for endogenous estrogen levels in the regression model is consistent with previous observations –25. In oAnother lifestyle factor that showed association with E activity was alcohol intake. Alcohol intake is associated with risk of several cancers including breast cancer. However, studies looking at the effects of alcohol consumption on circulating levels of endogenous estrogen have produced inconsistent results. Some studies reported that increased alcohol consumption is associated with higher circulating estrogens , while oThe association between IA ancestry and E activity among Latinas even after adjusting for endogenous estrogens suggests that in this group, the E activity could be related to environmental exposures such as exogenous chemicals or dietary constituents. Soy-based and vegetable-derived foods containing several phytoestrogens are common in the diet of Latino populations . BecauseAn important strength of the present study is the diverse study population and the larger sample set of Latina women included in the analysis of IA ancestry (n = 276) compared to our previous pilot study that included only 90 Mexican women . Using tOur study also has some limitations. Although we were able to assess total E activity in plasma samples and account for endogenous estrogens, this approach does not allow us to determine the specific estrogen-like compounds that are acting on the ER. However, we report total E activity, which encompasses both endogenous and exogenous estrogens and is a comprehensive representation of estrogenic exposure. Additionally, our results are based on blood samples from a single time point, which may not represent the fluctuations of estrogens over time. Yet, the endogenous estrogen levels in postmenopausal women are relatively stable over time  and flucGiven the central role of the activation of the ER in the etiology of breast cancer, a better understanding of the receptor’s interaction with the receptor ligands, whether endogenous or exogenous, is imperative. Additionally, the discovery of specific compounds that are modulating the receptor and are present at different levels in different populations, would lead to changes in exposure to these compounds and ultimately, to changes in breast cancer risk.S1 Fig(PDF)Click here for additional data file.S1 Dataset(XLS)Click here for additional data file.