Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold.Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated.Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p< 0.001) and Gail (p< 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive:p< 0.001; DCIS:p= 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p< 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential.Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.
背景：已知乳腺癌多基因风险评分（PRS）与传统风险模型（如盖尔模型[Gail]）主要提供相互独立的信息，但其重叠程度如何因种族、年龄、疾病类型（浸润性乳腺癌、导管原位癌）和风险阈值而异尚不明确。 方法：在一项回顾性病例对照研究中，我们评估了180,398名女性（其中161,849名为欧洲裔，18,549名为亚洲裔）的风险预测效能。通过逻辑回归模型计算比值比（OR），并估算受试者工作特征曲线下面积（AUC）。 结果：在欧洲裔女性中，针对浸润性疾病的PRS在较年轻（<50岁）女性中（OR=2.51，AUC=0.622）显示出比≥50岁女性（OR=2.06，AUC=0.653）更强的关联性。PRS在亚洲裔女性中的预测效能较低（OR范围=1.62–1.64，AUC=0.551–0.600）。盖尔模型在各组中的预测效能一般，在年轻亚洲裔女性中表现较差（OR=0.94–0.99，AUC=0.523–0.533）。在欧洲裔人群中，PRS（p<0.001）和盖尔模型（p<0.001）均存在年龄交互作用，而在亚洲裔人群中，仅盖尔模型观察到年龄交互作用（浸润性癌：p<0.001；导管原位癌：p=0.002）。在亚洲裔人群中，PRS识别出的高风险个体多于盖尔模型，尤其在≥50岁人群中，而盖尔模型在欧洲裔人群中识别更多。在高风险阈值下，PRS、盖尔模型和家族史之间的重叠有限。通过比较经验性与模型基础的ROC曲线进行校准分析，显示PRS和盖尔模型均存在偏离（p<0.001），表明校准不足。在欧洲裔人群中，家族史和既往活检史是盖尔模型区分度的主要驱动因素；在年轻亚洲裔人群中，首次活产年龄具有重要影响。 结论：PRS在传统工具基础上为风险分层增加了价值，特别是在年轻女性和亚洲裔人群中。
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