Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management. Using principles of Mendelian genetics, Bayesian probability theory, and variant-specific knowledge, Mendelian models derive the probability of carrying a pathogenic variant and developing cancer in the future, based on family history. Existing Mendelian models are widely employed, but are generally limited to specific genes and syndromes. However, the upsurge of multi-gene panel germline testing has spurred the discovery of many new gene-cancer associations that are not presently accounted for in these models. We have developed PanelPRO, a flexible, efficient Mendelian risk prediction framework that can incorporate an arbitrary number of genes and cancers, overcoming the computational challenges that arise because of the increased model complexity. We implement an eleven-gene, eleven-cancer model, the largest Mendelian model created thus far, based on this framework. Using simulations and a clinical cohort with germline panel testing data, we evaluate model performance, validate the reverse-compatibility of our approach with existing Mendelian models, and illustrate its usage. Our implementation is freely available for research use in the PanelPRO R package.
翻译:利用门德尔式的遗传学原则、巴耶斯概率理论和各种特定知识,门德尔式模型根据家庭历史,得出携带病原体变异的概率,并在今后发展癌症。现有的门德尔式模型被广泛采用,但一般限于特定基因和综合症。然而,多基因小组生殖细胞测试的猛增刺激了许多新的基因-癌症协会的发现,而这些协会目前没有在这些模型中进行核算。我们开发了一个灵活、高效的门德尔式风险预测框架,其中可以包括任意数量的基因和癌症,克服由于模型复杂性增加而出现的计算挑战。我们实施了11个基因模型,11个癌症模型,这是迄今在这种框架基础上创建的最大的门德尔式模型。我们利用模拟和带有生殖细胞小组测试数据的临床组群,评估模型性能,验证我们的方法与现有的门德尔式模型的反相容性,并展示其应用情况。