iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes

Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver var...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Chengliang, Guo, Yunfei, Yang, Hui, He, Zeyu, Liu, Xiaoming, Wang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5180414/
https://www.ncbi.nlm.nih.gov/pubmed/28007024
http://dx.doi.org/10.1186/s13073-016-0390-0
_version_ 1782485527464771584
author Dong, Chengliang
Guo, Yunfei
Yang, Hui
He, Zeyu
Liu, Xiaoming
Wang, Kai
author_facet Dong, Chengliang
Guo, Yunfei
Yang, Hui
He, Zeyu
Liu, Xiaoming
Wang, Kai
author_sort Dong, Chengliang
collection PubMed
description Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher’s exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0390-0) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5180414
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-51804142016-12-28 iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes Dong, Chengliang Guo, Yunfei Yang, Hui He, Zeyu Liu, Xiaoming Wang, Kai Genome Med Method Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher’s exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0390-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-12-22 /pmc/articles/PMC5180414/ /pubmed/28007024 http://dx.doi.org/10.1186/s13073-016-0390-0 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Dong, Chengliang
Guo, Yunfei
Yang, Hui
He, Zeyu
Liu, Xiaoming
Wang, Kai
iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title_full iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title_fullStr iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title_full_unstemmed iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title_short iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
title_sort icages: integrated cancer genome score for comprehensively prioritizing driver genes in personal cancer genomes
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5180414/
https://www.ncbi.nlm.nih.gov/pubmed/28007024
http://dx.doi.org/10.1186/s13073-016-0390-0
work_keys_str_mv AT dongchengliang icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes
AT guoyunfei icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes
AT yanghui icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes
AT hezeyu icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes
AT liuxiaoming icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes
AT wangkai icagesintegratedcancergenomescoreforcomprehensivelyprioritizingdrivergenesinpersonalcancergenomes

Ejemplares similares