MyOme Inc, Menlo Park, CA.
Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY.
JCO Precis Oncol. 2023 Feb;7:e2200447. doi: 10.1200/PO.22.00447.
To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups.
We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic.
The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone.
Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.
开发和验证一种跨种族综合风险评分（caIRS），该评分将跨种族多基因风险评分（caPRS）与乳腺癌（BC）风险的临床估算器相结合。我们假设 caIRS 比不同种族群体的临床危险因素更能预测 BC 风险。
我们使用具有纵向随访的多种回顾性队列数据来开发 caPRS 并将其与 Tyrer-Cuzick（T-C）临床模型相结合。我们在包括超过 130,000 名女性的两个验证队列中测试了 caIRS 与 BC 风险之间的关联。我们比较了 caIRS 和 T-C 对 5 年和剩余终身 BC 风险的模型区分度，并评估了 caIRS 将如何影响诊所的筛查。
在两个验证队列中，所有测试人群的 caIRS 均优于 T-C 单独使用，并且在 T-C 之外对风险预测有显著贡献。接受者操作特征曲线下的面积从 0.57 提高到 0.65，每个标准差的优势比从 1.35（95%CI，1.27 至 1.43）增加到 1.79（95%CI，1.70 至 1.88）在验证队列 1 中观察到类似的改善，在验证队列 2 中也观察到类似的改善。我们观察到在两个验证队列中，黑人和非裔美国女性使用 caIRS 时阳性预测值的最大增益，约为两倍，与 T-C 相当的阴性预测值。在包括 caIRS 和 T-C 的多变量、年龄调整后的逻辑回归模型中，caIRS 仍然具有统计学意义，表明 caIRS 提供了 T-C 单独无法提供的信息。
将 caPRS 添加到 T-C 模型中可以改善多种族女性的 BC 风险分层，这可能对筛查建议和预防产生影响。
