Polygenic risk scores for prediction of breast cancer and breast cancer subtypes

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRS) could improve screening and prevention strategies. Our aim was to develop PRS, optimized for prediction of estrogen receptor (ER) specific disease, from the largest available genome wide association dataset, and to empirically validate the PRS in prospective studies. The development dataset comprised 94,075 cases and 75,017 controls 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 step-wise regression or lasso penalized regression. The best performing PRS were validated in an independent test set comprising 11,428 cases and 18,323 controls from ten prospective studies, and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRS (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 PRS 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.