Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank
Peh Joo Ho, Elaine Hsuen Lim, Mikael Hartman, Fuh Yong Wong, Jingmei Li
Abstract
Purpose The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. Methods We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of LoF in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. Results In total, 147,399 were considered at high risk for developing BC within the next two years by at least one of the four risk prediction tools examined (Gail2-year>0.5%: 47%, PRS2-year>0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only one risk predictor. The overlap between individuals flagged as high-risk due to genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. Conclusion Risk-based BC screening may require a multi-pronged approach that includes PRS, predisposition genes, FH, and other recognised risk factors.