domingo, 26 de marzo de 2017

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes. - PubMed - NCBI

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes. - PubMed - NCBI



 2016 Dec 22;8(1):135. doi: 10.1186/s13073-016-0390-0.

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

Dong C1,2Guo Y1,2Yang H1,3He Z4Liu X5,6Wang K7,8.

Abstract

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 .

KEYWORDS:

Cancer genomics; Machine learning; Precision medicine; Precision oncology; TCGA

PMID:
 
28007024
 
PMCID:
 
PMC5180414
 
DOI:
 
10.1186/s13073-016-0390-0

[Indexed for MEDLINE] 
Free PMC Article

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