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：多基因面板检测可快速且低成本地检测多种癌症易感基因，使此类检测更广泛地覆盖人群。因此，越来越多携带不同癌症易感基因致病性胚系突变的患者被识别。这既提供了重大机遇，也产生了迫切需求——需要对这些患者进行适当的风险降低管理策略咨询。咨询的关键在于准确评估与特定基因突变相关的各类癌症年龄特异性风险，即渗透率估计。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指标（如渗透率、相对风险、比值比）的研究，并考虑相关不确定性，从而获得渗透率估计值。通过马尔可夫链蒙特卡洛算法估计参数后验分布后，我们计算渗透率及其可信区间。我们基于两个中度乳腺癌易感基因ATM和PALB2的风险研究报告进行模拟研究，检验所提方法并与现有方法比较。结果表明，所提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估计ATM基因致病突变携带者的乳腺癌渗透率。