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Pathway and network analysis of more than 2500 whole cancer genomes
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as par...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002574/ https://www.ncbi.nlm.nih.gov/pubmed/32024854 http://dx.doi.org/10.1038/s41467-020-14367-0 |
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author | Reyna, Matthew A. Haan, David Paczkowska, Marta Verbeke, Lieven P. C. Vazquez, Miguel Kahraman, Abdullah Pulido-Tamayo, Sergio Barenboim, Jonathan Wadi, Lina Dhingra, Priyanka Shrestha, Raunak Getz, Gad Lawrence, Michael S. Pedersen, Jakob Skou Rubin, Mark A. Wheeler, David A. Brunak, Søren Izarzugaza, Jose M. G. Khurana, Ekta Marchal, Kathleen von Mering, Christian Sahinalp, S. Cenk Valencia, Alfonso Reimand, Jüri Stuart, Joshua M. Raphael, Benjamin J. |
author_facet | Reyna, Matthew A. Haan, David Paczkowska, Marta Verbeke, Lieven P. C. Vazquez, Miguel Kahraman, Abdullah Pulido-Tamayo, Sergio Barenboim, Jonathan Wadi, Lina Dhingra, Priyanka Shrestha, Raunak Getz, Gad Lawrence, Michael S. Pedersen, Jakob Skou Rubin, Mark A. Wheeler, David A. Brunak, Søren Izarzugaza, Jose M. G. Khurana, Ekta Marchal, Kathleen von Mering, Christian Sahinalp, S. Cenk Valencia, Alfonso Reimand, Jüri Stuart, Joshua M. Raphael, Benjamin J. |
author_sort | Reyna, Matthew A. |
collection | PubMed |
description | The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments. |
format | Online Article Text |
id | pubmed-7002574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70025742020-02-07 Pathway and network analysis of more than 2500 whole cancer genomes Reyna, Matthew A. Haan, David Paczkowska, Marta Verbeke, Lieven P. C. Vazquez, Miguel Kahraman, Abdullah Pulido-Tamayo, Sergio Barenboim, Jonathan Wadi, Lina Dhingra, Priyanka Shrestha, Raunak Getz, Gad Lawrence, Michael S. Pedersen, Jakob Skou Rubin, Mark A. Wheeler, David A. Brunak, Søren Izarzugaza, Jose M. G. Khurana, Ekta Marchal, Kathleen von Mering, Christian Sahinalp, S. Cenk Valencia, Alfonso Reimand, Jüri Stuart, Joshua M. Raphael, Benjamin J. Nat Commun Article The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments. Nature Publishing Group UK 2020-02-05 /pmc/articles/PMC7002574/ /pubmed/32024854 http://dx.doi.org/10.1038/s41467-020-14367-0 Text en © The Author(s) 2020, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Reyna, Matthew A. Haan, David Paczkowska, Marta Verbeke, Lieven P. C. Vazquez, Miguel Kahraman, Abdullah Pulido-Tamayo, Sergio Barenboim, Jonathan Wadi, Lina Dhingra, Priyanka Shrestha, Raunak Getz, Gad Lawrence, Michael S. Pedersen, Jakob Skou Rubin, Mark A. Wheeler, David A. Brunak, Søren Izarzugaza, Jose M. G. Khurana, Ekta Marchal, Kathleen von Mering, Christian Sahinalp, S. Cenk Valencia, Alfonso Reimand, Jüri Stuart, Joshua M. Raphael, Benjamin J. Pathway and network analysis of more than 2500 whole cancer genomes |
title | Pathway and network analysis of more than 2500 whole cancer genomes |
title_full | Pathway and network analysis of more than 2500 whole cancer genomes |
title_fullStr | Pathway and network analysis of more than 2500 whole cancer genomes |
title_full_unstemmed | Pathway and network analysis of more than 2500 whole cancer genomes |
title_short | Pathway and network analysis of more than 2500 whole cancer genomes |
title_sort | pathway and network analysis of more than 2500 whole cancer genomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002574/ https://www.ncbi.nlm.nih.gov/pubmed/32024854 http://dx.doi.org/10.1038/s41467-020-14367-0 |
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