Statistical methods for Mendelian models with multiple genes and cancers

TL;DR

This paper introduces PanelPRO, a flexible Mendelian risk prediction framework capable of modeling multiple genes and cancers simultaneously, addressing limitations of existing models and validated with simulations and clinical data.

Contribution

Developed PanelPRO, the largest Mendelian model to date, capable of incorporating numerous genes and cancers, overcoming computational challenges of complex genetic risk modeling.

Findings

Validated model performance with simulations and clinical data

Demonstrated reverse-compatibility with existing models

Provided an open-source R package for research use

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 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.