Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, i.e., penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (e.g., penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

翻译：多基因面板检测能够以较低成本快速检测多种癌症易感基因，从而使此类检测覆盖更广泛的人群。因此，更多携带不同癌症易感基因致病性胚系突变的患者得以被识别。这既为患者提供了制定风险降低管理策略的重要机遇，也产生了对此类患者进行遗传咨询的迫切需求。咨询的核心依据是特定基因突变相关癌症的年龄特异性风险精准估计，即外显率评估。我们提出一种基于贝叶斯分层随机效应模型的元分析方法，通过整合报告不同类型风险测量指标（如外显率、相对风险、比值比）的研究，同时考虑相关不确定性，从而获得外显率估计值。在通过马尔可夫链蒙特卡洛算法估计参数后验分布后，我们计算外显率及其可信区间。我们通过基于两个中度风险乳腺癌易感基因ATM和PALB2风险研究报告的模拟实验，对所提方法进行验证并与现有方法比较。结果表明，本方法在可信区间覆盖概率和估计均方误差方面显著优于现有方法。最后，我们将该方法应用于ATM基因致病突变携带者乳腺癌外显率的实际估算。