Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.

A well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly...

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Main Authors: David Tamborero, Nuria Lopez-Bigas, Abel Gonzalez-Perez
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23408991/pdf/?tool=EBI
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spelling doaj-4acf915e62d54827a7558fb8a2cb02fe2021-03-03T23:44:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0182e5548910.1371/journal.pone.0055489Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.David TamboreroNuria Lopez-BigasAbel Gonzalez-PerezA well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly differs between tumors depending on the copy number status. A drawback of this approach is that it may fail to detect low-recurrent drivers. Furthermore, this analysis does not provide information about expression changes for each gene as compared to the whole data set and does not take into consideration the expression of normal samples. Here we describe a novel method (Oncodrive-CIS) aimed at ranking genes according to the expression impact caused by the CNAs. The rationale of Oncodrive-CIS is based on the hypothesis that genes involved in cancer due to copy number changes are more biased towards misregulation than are bystanders. Moreover, to gain insight into the expression changes caused by gene dosage, the expression of samples with CNAs is compared to that of tumor samples with diploid genotype and also to that of normal samples. Oncodrive-CIS demonstrated better performance in detecting putative associations between copy-number and expression in simulated data sets as compared to other methods aimed to this purpose, and picked up genes likely to be related with tumorigenesis when applied to real cancer samples. In summary, Oncodrive-CIS provides a statistical framework to evaluate the in cis effect of CNAs that may be useful to elucidate the role of these aberrations in driving oncogenesis. An implementation of this method and the corresponding user guide are freely available at http://bg.upf.edu/oncodrivecis.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23408991/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author David Tamborero
Nuria Lopez-Bigas
Abel Gonzalez-Perez
spellingShingle David Tamborero
Nuria Lopez-Bigas
Abel Gonzalez-Perez
Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
PLoS ONE
author_facet David Tamborero
Nuria Lopez-Bigas
Abel Gonzalez-Perez
author_sort David Tamborero
title Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
title_short Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
title_full Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
title_fullStr Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
title_full_unstemmed Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
title_sort oncodrive-cis: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description A well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly differs between tumors depending on the copy number status. A drawback of this approach is that it may fail to detect low-recurrent drivers. Furthermore, this analysis does not provide information about expression changes for each gene as compared to the whole data set and does not take into consideration the expression of normal samples. Here we describe a novel method (Oncodrive-CIS) aimed at ranking genes according to the expression impact caused by the CNAs. The rationale of Oncodrive-CIS is based on the hypothesis that genes involved in cancer due to copy number changes are more biased towards misregulation than are bystanders. Moreover, to gain insight into the expression changes caused by gene dosage, the expression of samples with CNAs is compared to that of tumor samples with diploid genotype and also to that of normal samples. Oncodrive-CIS demonstrated better performance in detecting putative associations between copy-number and expression in simulated data sets as compared to other methods aimed to this purpose, and picked up genes likely to be related with tumorigenesis when applied to real cancer samples. In summary, Oncodrive-CIS provides a statistical framework to evaluate the in cis effect of CNAs that may be useful to elucidate the role of these aberrations in driving oncogenesis. An implementation of this method and the corresponding user guide are freely available at http://bg.upf.edu/oncodrivecis.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23408991/pdf/?tool=EBI
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AT abelgonzalezperez oncodrivecisamethodtoreveallikelydrivergenesbasedontheimpactoftheircopynumberchangesonexpression
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