Statistical methods for Mendelian models with multiple genes and cancers

Abstract

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 multigene 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 11‐gene, 11‐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.

Combining Breast Cancer Risk Prediction Models

“…Ensemble learning can be used to combine an arbitrary number of models of any type. Penetrance modification is a way to incorporate risk factors beyond family history into complex Mendelian models, including the recently developed multi-gene and multi-syndrome model PanelPro [15,62], which can incorporate an arbitrary number of genes and cancer types and can potentially provide greater clinical benefit than single-syndrome models such as BRCAPRO.…”