DOI 10.1101/2025.02.27.25323002
Overlap of high-risk individuals across family history, genetic & non-genetic breast cancer risk models: Analysis of 180,398 women from European & Asian ancestries.
Аннотация
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.
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