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...

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Autores principales:	Mark D M Leiserson, Dima Blokh, Roded Sharan, Benjamin J Raphael
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/18f6e798e1c848eeb0f54886920e4b89
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id	oai:doaj.org-article:18f6e798e1c848eeb0f54886920e4b89
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spelling	oai:doaj.org-article:18f6e798e1c848eeb0f54886920e4b892021-11-18T05:52:08ZSimultaneous identification of multiple driver pathways in cancer.1553-734X1553-735810.1371/journal.pcbi.1003054https://doaj.org/article/18f6e798e1c848eeb0f54886920e4b892013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23717195/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Distinguishing 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.Mark D M LeisersonDima BlokhRoded SharanBenjamin J RaphaelPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 5, p e1003054 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Mark D M Leiserson Dima Blokh Roded Sharan Benjamin J Raphael Simultaneous identification of multiple driver pathways in cancer.
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	article
author	Mark D M Leiserson Dima Blokh Roded Sharan Benjamin J Raphael
author_facet	Mark D M Leiserson Dima Blokh Roded Sharan Benjamin J Raphael
author_sort	Mark D M Leiserson
title	Simultaneous identification of multiple driver pathways in cancer.
title_short	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_sort	simultaneous identification of multiple driver pathways in cancer.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/18f6e798e1c848eeb0f54886920e4b89
work_keys_str_mv	AT markdmleiserson simultaneousidentificationofmultipledriverpathwaysincancer AT dimablokh simultaneousidentificationofmultipledriverpathwaysincancer AT rodedsharan simultaneousidentificationofmultipledriverpathwaysincancer AT benjaminjraphael simultaneousidentificationofmultipledriverpathwaysincancer
_version_	1718424713500295168

Simultaneous identification of multiple driver pathways in cancer.

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