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：多基因组合检测能够以较低成本快速检测多种癌症易感基因，从而使更广泛的人群获得此类检测机会。因此，越来越多的携带多种癌症易感基因致病性胚系突变的患者被识别出来。这为针对这些患者制定合适的降低风险管理策略提供了重大机遇，也产生了紧迫需求。遗传咨询依赖于与特定基因突变相关的多种癌症年龄别风险准确估算（即外显率估计）。我们提出一种基于贝叶斯分层随机效应模型的荟萃分析方法，通过整合报告不同风险测量类型（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，我们估计外显率及其可信区间。我们通过基于报告两种中度风险乳腺癌易感基因ATM和PALB2风险的研究模拟，研究提出的方法并与现有方法进行比较。我们的方法在可信区间覆盖概率和估计均方误差方面表现出显著优越性。最后，我们应用该方法估计ATM基因致病性突变携带者的乳腺癌外显率。