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The mutational landscape of phosphorylation signaling in cancer

Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies....

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Autores principales: Reimand, Jüri, Wagih, Omar, Bader, Gary D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3788619/
https://www.ncbi.nlm.nih.gov/pubmed/24089029
http://dx.doi.org/10.1038/srep02651
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author Reimand, Jüri
Wagih, Omar
Bader, Gary D.
author_facet Reimand, Jüri
Wagih, Omar
Bader, Gary D.
author_sort Reimand, Jüri
collection PubMed
description Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction.
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spelling pubmed-37886192013-10-18 The mutational landscape of phosphorylation signaling in cancer Reimand, Jüri Wagih, Omar Bader, Gary D. Sci Rep Article Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction. Nature Publishing Group 2013-10-02 /pmc/articles/PMC3788619/ /pubmed/24089029 http://dx.doi.org/10.1038/srep02651 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/3.0/ This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/
spellingShingle Article
Reimand, Jüri
Wagih, Omar
Bader, Gary D.
The mutational landscape of phosphorylation signaling in cancer
title The mutational landscape of phosphorylation signaling in cancer
title_full The mutational landscape of phosphorylation signaling in cancer
title_fullStr The mutational landscape of phosphorylation signaling in cancer
title_full_unstemmed The mutational landscape of phosphorylation signaling in cancer
title_short The mutational landscape of phosphorylation signaling in cancer
title_sort mutational landscape of phosphorylation signaling in cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3788619/
https://www.ncbi.nlm.nih.gov/pubmed/24089029
http://dx.doi.org/10.1038/srep02651
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