Purpose

Breast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown.

Methods

The Canadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known.

Results

Generally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer.

Conclusion

PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

中文翻译：

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多基因风险评分改善女性乳腺癌遗传风险人群估计的潜力

目的

乳腺癌风险通常使用家族史 (FH) 和罕见的高/中外显率致病变异 (PV) 进行评估，特别是BRCA1/2，以及最近的PALB2、CHEK2和ATM。除了这些 PV 之外，现在还可以使用日益具有预测性的多基因风险评分 (PRS)。然而，这三种不同的遗传风险指标用于风险分层的人群水平的比较预测能力尚不清楚。

方法

使用新型遗传混合模型 (GMM) 估算了加拿大遗传性乳腺癌风险分布。根据经验观察的人口统计模式，合成了具有现实代表性的女性样本，其中包括适当相关的家族史、罕见 PV 的遗传、PRS 以及未知多基因型的残余风险。使用 BOADICEA 风险算法模拟 10 年绝对乳腺癌发病率，并与遗传风险进行比较，就好像整体多基因（包括其测量的 PRS 成分）和 PV 风险完全已知。

结果

一般来说，PRS 对于识别高危女性的预测能力最强，而家族史的预测能力最弱。只有 PRS 才能识别出患乳腺癌风险较低的女性。

结论

PRS 信息将是实现全人群乳腺癌筛查有效风险分层的最重要进步。