Overlap of high-risk individuals across family history, genetic & non-genetic breast cancer risk models: Analysis of 180,398 women from European & Asian ancestries

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ABSTRACT Background Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk ≥1·66% by the Gail model [GAIL binary ]; first-degree family history of breast cancer [FH binary ]; 5-year absolute risk ≥1·66% by a 313-variants polygenic risk score [PRS binary ]; and carriers of pathogenic variants in breast cancer predisposition genes [PTV binary ]. Findings The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeans vs controls : AUC PRS =0·635 [0·632-0·638] vs AUC Gail =0·492 [0·489-0·495]; Asians vs controls : AUC PRS =0·564 [0·556-0·573] vs AUC Gail =0·506 [0·497-0·514]). PRS binary and GAIL binary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRS binary and PTV binary was minimal (<0·65% Europeans, <0·15% Asians) compared to the proportion at high risk using PTV binary alone (Europeans: 4·6%, Asians: 4·4%) and PRS binary alone (Europeans: 13·9%, Asians: 8·5%). PRS binary and FH binary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation The incomplete overlap between high-risk individuals identified by PRS binary , GAIL binary , FH binary, and PTV binary highlights the need for a comprehensive approach to breast cancer risk prediction. SIGNIFICANCE This study shows that different ways of predicting breast cancer risk do not always flag the same people, suggesting that combining multiple risk factors could improve early detection and screening.

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