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.

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.

Metrics

  • 1,650
    views
  • 168
    downloads
  • 10
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Cancer Biology
    Han V Han, Richard Efem ... Richard Z Lin
    Research Article

    Most human pancreatic ductal adenocarcinoma (PDAC) are not infiltrated with cytotoxic T cells and are highly resistant to immunotherapy. Over 90% of PDAC have oncogenic KRAS mutations, and phosphoinositide 3-kinases (PI3Ks) are direct effectors of KRAS. Our previous study demonstrated that ablation of Pik3ca in KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cells induced host T cells to infiltrate and completely eliminate the tumors in a syngeneic orthotopic implantation mouse model. Now, we show that implantation of Pik3ca−/− KPC (named αKO) cancer cells induces clonal enrichment of cytotoxic T cells infiltrating the pancreatic tumors. To identify potential molecules that can regulate the activity of these anti-tumor T cells, we conducted an in vivo genome-wide gene-deletion screen using αKO cells implanted in the mouse pancreas. The result shows that deletion of propionyl-CoA carboxylase subunit B gene (Pccb) in αKO cells (named p-αKO) leads to immune evasion, tumor progression, and death of host mice. Surprisingly, p-αKO tumors are still infiltrated with clonally enriched CD8+ T cells but they are inactive against tumor cells. However, blockade of PD-L1/PD1 interaction reactivated these clonally enriched T cells infiltrating p-αKO tumors, leading to slower tumor progression and improve survival of host mice. These results indicate that Pccb can modulate the activity of cytotoxic T cells infiltrating some pancreatic cancers and this understanding may lead to improvement in immunotherapy for this difficult-to-treat cancer.

    1. Cancer Biology
    2. Immunology and Inflammation
    Almudena Mendez-Perez, Andres M Acosta-Moreno ... Esteban Veiga
    Short Report

    In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.