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PRODIGY: personalized prioritization of driver genes
MOTIVATION: Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primar...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703777/ https://www.ncbi.nlm.nih.gov/pubmed/31681944 http://dx.doi.org/10.1093/bioinformatics/btz815 |
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author | Dinstag, Gal Shamir, Ron |
author_facet | Dinstag, Gal Shamir, Ron |
author_sort | Dinstag, Gal |
collection | PubMed |
description | MOTIVATION: Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification but patient-specific driver gene identification remains a challenge. METHODS: We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein–protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize-collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. RESULTS: In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further toward personalized medicine and treatment. AVAILABILITY AND IMPLEMENTATION: The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7703777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77037772020-12-07 PRODIGY: personalized prioritization of driver genes Dinstag, Gal Shamir, Ron Bioinformatics Original Papers MOTIVATION: Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification but patient-specific driver gene identification remains a challenge. METHODS: We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein–protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize-collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. RESULTS: In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further toward personalized medicine and treatment. AVAILABILITY AND IMPLEMENTATION: The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-03-15 2019-11-04 /pmc/articles/PMC7703777/ /pubmed/31681944 http://dx.doi.org/10.1093/bioinformatics/btz815 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Dinstag, Gal Shamir, Ron PRODIGY: personalized prioritization of driver genes |
title | PRODIGY: personalized prioritization of driver genes |
title_full | PRODIGY: personalized prioritization of driver genes |
title_fullStr | PRODIGY: personalized prioritization of driver genes |
title_full_unstemmed | PRODIGY: personalized prioritization of driver genes |
title_short | PRODIGY: personalized prioritization of driver genes |
title_sort | prodigy: personalized prioritization of driver genes |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703777/ https://www.ncbi.nlm.nih.gov/pubmed/31681944 http://dx.doi.org/10.1093/bioinformatics/btz815 |
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