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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....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2013
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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. |
format | Online Article Text |
id | pubmed-3788619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
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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