Association analysis identifies 65 new breast cancer risk loci, predicts target genes for known risk loci and demonstrates a strong overlap with somatic driver genes in breast tumours. Genome-wide association studies for breast cancer have identified common genetic variation that influences susceptibility to this disease, but much of the genetic risk remains unexplained. Doug Easton and colleagues report a genome-wide association study for breast cancer in more than 122,000 cases and 105,000 controls. The authors genotyped a subset of these cases using OncoArray, a new, custom genome-wide single-nucleotide polymorphism (SNP) array for cancer genomics. Overall, they identify 65 loci newly associated with breast cancer susceptibility, and estimate that, together with 107 previously identified breast cancer susceptibility loci, these explain about 18 per cent of the familial relative risk of breast cancer. Polygenic risk scores may be used in risk prediction models and may improve early detection and targeted prevention of the disease. Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry1. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10−8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

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.

To the Editor: We read the article by Antoniou et al. (August 7 issue)¹ because we like to know the penetrance of breast-cancer genes before we recommend surgery. After reading the article, we are unsure of what to tell patients. The simplest message would be that the risk up to 70 years of age is 35%, but this is based on an analysis of patients who received a diagnosis of breast cancer from 1930 to the present. The relevant risk value is that for a woman without breast cancer who is found to have a PALB2 mutation today. Most patients . . .

Background: Previously, small studies have 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 histologic 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%; endometrioid: 12%; clear-cell: 2%). Conclusions/Impact: Pathologic characteristics of BRCA1 and BRCA2 tumors may be useful for improving risk-prediction algorithms and informing clinical strategies for screening and prophylaxis. Cancer Epidemiol Biomarkers Prev; 21(1); 134–47. ©2011 AACR.