Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO

  1. Gavin Lee
  2. Jane W Liang
  3. Qing Zhang
  4. Theodore Huang
  5. Christine Choirat
  6. Giovanni Parmigani
  7. Danielle Braun  Is a corresponding author
  1. ETH Zürich and EPFL, Switzerland
  2. Harvard T.H. Chan School of Public Health, United States
  3. Broad Institute of MIT and Harvard, United States

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.

Data availability

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

Author details

  1. Gavin Lee

    Swiss Data Science Center, ETH Zürich and EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2659-1163
  2. Jane W Liang

    Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Qing Zhang

    Getz Laboratory, Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Theodore Huang

    Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Christine Choirat

    Swiss Data Science Center, ETH Zürich and EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  6. Giovanni Parmigani

    Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Danielle Braun

    Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
    For correspondence
    bmendel@jimmy.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5177-8598

Funding

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.

Reviewing Editor

  1. Goutham Narla, University of Michigan, United States

Version history

  1. Received: March 24, 2021
  2. Accepted: August 16, 2021
  3. Accepted Manuscript published: August 18, 2021 (version 1)
  4. Version of Record published: September 28, 2021 (version 2)
  5. Version of Record updated: November 15, 2022 (version 3)

Copyright

© 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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  1. Gavin Lee
  2. Jane W Liang
  3. Qing Zhang
  4. Theodore Huang
  5. Christine Choirat
  6. Giovanni Parmigani
  7. Danielle Braun
(2021)
Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO
eLife 10:e68699.
https://doi.org/10.7554/eLife.68699

Share this article

https://doi.org/10.7554/eLife.68699

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