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.

Purpose: According to current guidelines, molecular tests predicting the outcome of breast cancer patients can be used to assist in making treatment decisions after consideration of conventional markers. We developed and validated a gene expression signature predicting the likelihood of distant recurrence in patients with estrogen receptor (ER)–positive, HER2-negative breast cancer treated with adjuvant endocrine therapy. Experimental Design: RNA levels assessed by quantitative reverse transcriptase PCR in formalin-fixed, paraffin-embedded tumor tissue were used to calculate a risk score (Endopredict, EP) consisting of eight cancer-related and three reference genes. EP was combined with nodal status and tumor size into a comprehensive risk score, EPclin. Both prespecified risk scores including cutoff values to determine a risk group for each patient (low and high) were validated independently in patients from two large randomized phase III trials [Austrian Breast and Colorectal Cancer Study Group (ABCSG)-6: n = 378, ABCSG-8: n = 1,324]. Results: In both validation cohorts, continuous EP was an independent predictor of distant recurrence in multivariate analysis (ABCSG-6: P = 0.010, ABCSG-8: P < 0.001). Combining Adjuvant!Online, quantitative ER, Ki67, and treatment with EP yielded a prognostic power significantly superior to the clinicopathologic factors alone [c-indices: 0.764 vs. 0.750, P = 0.024 (ABCSG-6) and 0.726 vs. 0.701, P = 0.003 (ABCSG-8)]. EPclin had c-indices of 0.788 and 0.732 and resulted in 10-year distant recurrence rates of 4% and 4% in EPclin low-risk and 28% and 22% in EPclin high-risk patients in ABCSG-6 (P < 0.001) and ABCSG-8 (P < 0.001), respectively. Conclusions: The multigene EP risk score provided additional prognostic information to the risk of distant recurrence of breast cancer patients, independent from clinicopathologic parameters. The EPclin score outperformed all conventional clinicopathologic risk factors. Clin Cancer Res; 17(18); 6012–20. ©2011 AACR.