These findings provide estimates of cancer risk based on BRCA1 and BRCA2 mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mutation location in risk assessment.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Background Previous small studies found that BRCA1 and BRCA2 breast tumors differ in their pathology. Analysis of larger datasets of mutation carriers should allow further tumor characterization. Methods We used data from 4,325 BRCA1 and 2,568 BRCA2 mutation carriers to analyze the pathology of invasive breast, ovarian and contralateral breast cancers. Results There was strong evidence that the proportion of estrogen receptor (ER)-negative breast tumors decreased with age at diagnosis among BRCA1 (p-trend=1.2×10−5) but increased with age at diagnosis among BRCA2 carriers (p-trend=6.8×10−6). The proportion of triple negative tumors decreased with age at diagnosis in BRCA1 carriers but increased with age at diagnosis of BRCA2 carriers. In both BRCA1 and BRCA2 carriers, ER-negative tumors were of higher histological grade than ER-positive tumors (Grade 3 vs. Grade 1, p=1.2×10−13 for BRCA1 and p=0.001 for BRCA2). ER and progesterone receptor (PR) expression were independently associated with mutation carrier status (ER-positive odds ratio (OR) for BRCA2=9.4, 95%CI:7.0-12.6 and PR-positive OR=1.7, 95%CI:1.3-2.3, under joint analysis). Lobular tumors were more likely to be BRCA2-related (OR for BRCA2=3.3, 95%CI:2.4-4.4, p=4.4×10−14), and medullary tumors BRCA1-related (OR for BRCA2=0.25, 95%CI:0.18-0.35, p=2.3×10−15). ER-status of the first breast cancer was predictive of ER-status of asynchronous contralateral breast cancer (p=0.0004 for BRCA1; p=0.002 for BRCA2). There were no significant differences in ovarian cancer morphology between BRCA1 and BRCA2 carriers (serous:67%; mucinous:1%; endometriod:12%; clear-cell:2%). Conclusions/Impact Pathology characteristics of BRCA1 and BRCA2 tumors may be useful for improving risk prediction algorithms and informing clinical strategies for screening and prophylaxis.