Title: Bayesian Meta-Analysis of Penetrance for Cancer Risk Show Chinese title

Title: 癌症风险易感性贝叶斯元分析

Abstract: 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. Show Chinese abstract

Abstract: 多基因面板检测允许快速且以较低成本检测多种癌症易感基因，使此类检测对更广泛的人群可及。 因此，越来越多携带各种癌症易感基因致病性种系突变的患者被识别出来。 这创造了一个巨大机遇，同时也迫切需要就适当的降低风险管理策略对这些患者进行咨询。 咨询的关键在于准确估计与特定基因突变相关的各种癌症的年龄特异性发病风险，即外显率估计。 我们提出了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指标（例如，外显率、相对风险、比值比）的研究，同时考虑相关不确定性来获得外显率估计。 在通过马尔可夫链蒙特卡洛算法估计参数后验分布后，我们估计外显率和可信区间。 我们通过基于报告两种中等风险乳腺癌易感基因ATM和PALB2风险的研究的模拟来检验所提出的方法，并与现有方法进行比较。 在可信区间覆盖概率和估计的均方误差方面，我们提出的方法明显优于现有方法。 最后，我们将该方法应用于估计ATM基因致病性突变携带者的乳腺癌外显率。