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Simultaneous Identification of Multiple Driver Pathways in Cancer
Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identif...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662702/ https://www.ncbi.nlm.nih.gov/pubmed/23717195 http://dx.doi.org/10.1371/journal.pcbi.1003054 |
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author | Leiserson, Mark D. M. Blokh, Dima Sharan, Roded Raphael, Benjamin J. |
author_facet | Leiserson, Mark D. M. Blokh, Dima Sharan, Roded Raphael, Benjamin J. |
author_sort | Leiserson, Mark D. M. |
collection | PubMed |
description | Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software. |
format | Online Article Text |
id | pubmed-3662702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36627022013-05-28 Simultaneous Identification of Multiple Driver Pathways in Cancer Leiserson, Mark D. M. Blokh, Dima Sharan, Roded Raphael, Benjamin J. PLoS Comput Biol Research Article Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software. Public Library of Science 2013-05-23 /pmc/articles/PMC3662702/ /pubmed/23717195 http://dx.doi.org/10.1371/journal.pcbi.1003054 Text en © 2013 Leiserson et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Leiserson, Mark D. M. Blokh, Dima Sharan, Roded Raphael, Benjamin J. Simultaneous Identification of Multiple Driver Pathways in Cancer |
title | Simultaneous Identification of Multiple Driver Pathways in Cancer |
title_full | Simultaneous Identification of Multiple Driver Pathways in Cancer |
title_fullStr | Simultaneous Identification of Multiple Driver Pathways in Cancer |
title_full_unstemmed | Simultaneous Identification of Multiple Driver Pathways in Cancer |
title_short | Simultaneous Identification of Multiple Driver Pathways in Cancer |
title_sort | simultaneous identification of multiple driver pathways in cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662702/ https://www.ncbi.nlm.nih.gov/pubmed/23717195 http://dx.doi.org/10.1371/journal.pcbi.1003054 |
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