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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 sy...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478415/ https://www.ncbi.nlm.nih.gov/pubmed/34406119 http://dx.doi.org/10.7554/eLife.68699 |
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author | Lee, Gavin Liang, Jane W Zhang, Qing Huang, Theodore Choirat, Christine Parmigiani, Giovanni Braun, Danielle |
author_facet | Lee, Gavin Liang, Jane W Zhang, Qing Huang, Theodore Choirat, Christine Parmigiani, Giovanni Braun, Danielle |
author_sort | Lee, Gavin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8478415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-84784152021-09-30 Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO Lee, Gavin Liang, Jane W Zhang, Qing Huang, Theodore Choirat, Christine Parmigiani, Giovanni Braun, Danielle eLife Cancer Biology 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. eLife Sciences Publications, Ltd 2021-08-18 /pmc/articles/PMC8478415/ /pubmed/34406119 http://dx.doi.org/10.7554/eLife.68699 Text en © 2021, Lee et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Cancer Biology Lee, Gavin Liang, Jane W Zhang, Qing Huang, Theodore Choirat, Christine Parmigiani, Giovanni Braun, Danielle Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title | Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title_full | Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title_fullStr | Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title_full_unstemmed | Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title_short | Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO |
title_sort | multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel r package panelpro |
topic | Cancer Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478415/ https://www.ncbi.nlm.nih.gov/pubmed/34406119 http://dx.doi.org/10.7554/eLife.68699 |
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