Evaluating a Mendelian Risk Prediction Model That Aggregates Across Genes and Cancers

Abstract

Using principles of Mendelian genetics, probability theory, and mutation-specific knowledge, Mendelian risk prediction models identify those at high risk of carrying a heritable cancer susceptibility variant and assess future risk of cancer. Our previously-validated Fam3PRO model is a generalizable and computationally efficient Mendelian risk prediction framework that incorporates an arbitrary number of gene-cancer associations. In practice, from a model training perspective, there may be uncertainty in estimating the population-level model parameters necessary for rare gene-cancer associations. From a clinical perspective, it may be infeasible to obtain a detailed patient family history for many cancers. Motivated by the context of pre-screening for germline testing of a broad hereditary cancer gene panel, we propose a Mendelian model that aggregates information across genes and cancers, reducing patient burden and bypassing the need for robust parameter estimation for rare genes and syndromes. We evaluated this aggregate model through simulations and applied it to two independent clinical cohorts. We show that when the clinical goal is to assess patient risk of carrying a pathogenic variant for any cancer susceptibility gene, the aggregate model can give results comparable to a Mendelian model that considers many genes and cancers individually, while greatly simplifying model assumptions and user input.