  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.
