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

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 [GAILbinary]; first-degree family history of breast cancer [FHbinary]; 5-year absolute risk >=1.66% by a 313-variants polygenic risk score [PRSbinary]; and carriers of pathogenic variants in breast cancer predisposition genes [PTVbinary]. Findings The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeansvs controls: AUCPRS=0.635 [0.632-0.638] vs AUCGail=0.492 [0.489-0.495]; Asiansvs controls: AUCPRS=0.564 [0.556-0.573] vs AUCGail=0.506 [0.497-0.514]). PRSbinary and GAILbinary 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 PRSbinary and PTVbinary was minimal (<0.65% Europeans, <0.15% Asians) compared to the proportion at high risk using PTVbinary alone (Europeans: 4.6%, Asians: 4.4%) and PRSbinary alone (Europeans: 13.9%, Asians: 8.5%). PRSbinary and FHbinary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation The incomplete overlap between high-risk individuals identified by PRSbinary, GAILbinary, FHbinary, and PTVbinary 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.Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.