Abstract
Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models focus on a few specific syndromes; however, recent evidence from multi-gene panel testing shows that many syndromes are overlapping, motivating the development of models that incorporate family history on several cancers and predict mutations for a comprehensive panel of genes.We present PanelPRO, a new, open-source R package providing a fast, flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. It includes a customizable database with default parameter values estimated from published studies and allows users to select any combinations of genes and cancers for their models, including well-established single syndrome BayesMendel models (BRCAPRO and MMRPRO). This leads to more accurate risk predictions and ultimately has a high impact on prevention strategies for cancer and clinical decision making. The package is available for download for research purposes at https://projects.iq.harvard.edu/bayesmendel/panelpro.
Highlights
In the last decade, DNA sequencing has changed dramatically
Tests have become faster and more affordable, leading to discovery of a growing number of germline pathogenic variants associated with increased cancer risk
Genes, and their interactions, we introduce PanelPRO, an R package which aims to e ciently and exibly scale to the demands of germline panel testing
Summary
DNA sequencing has changed dramatically. Tests have become faster and more affordable, leading to discovery of a growing number of germline pathogenic variants associated with increased cancer risk. Evidence is accruing that gene mutations, which were typically believed to be only associated with one or two types of hereditary cancers, may increase the risk for a wider range of syndromes. These advancements have changed the genetic counseling landscape by introducing a need to consider a wider set of individual genes and cancers to accurately assess overall risks. We expect that users of BayesMendel will migrate to this generalized and customizable enhancement, and that PanelPRO will lead to new users interested in broader cross syndrome modeling in the current landscape for cancer clinical risk assessment.
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