Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO
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.
This manuscript introduces PanelPRO, an innovative multi-gene multi-cancer Mendelian model. Software for this model, including the model parameter database, is available to download for research use; https://projects.iq.harvard.edu/bayesmendel/panelpro
Article and author information
National Institutes of Health (5T32CA009337)
- Jane W Liang
- Theodore Huang
National Institutes of Health (2T32CA009001)
- Theodore Huang
National Institutes of Health (4P30CA006516)
- Giovanni Parmigani
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Goutham Narla, University of Michigan, United States
- Received: March 24, 2021
- Accepted: August 16, 2021
- Accepted Manuscript published: August 18, 2021 (version 1)
- Version of Record published: September 28, 2021 (version 2)
- Version of Record updated: November 15, 2022 (version 3)
© 2021, Lee et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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