Format

Send to

Choose Destination
Genome Med. 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 C1,2, Guo Y1,2, Yang H1,3, He Z4, Liu X5,6, Wang K7,8.

Author information

1
Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, 90089, USA.
2
Biostatistics Graduate Program, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
3
Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA.
4
Department of Computer Science, New York University, New York, NY, 10012, USA.
5
Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
6
Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
7
Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, 90089, USA. kw2701@cumc.columbia.edu.
8
Institute for Genomic Medicine, Columbia University, 630 W. 168th St, Room 11-451, New York, NY, 10032, USA. kw2701@cumc.columbia.edu.

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

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center