ICAGES: Integrated CAncer GEnome Score for Comprehensively Prioritizing Driver Genes in Personal Cancer Genomes

Overview

Journal Genome Med

Publisher Biomed Central

Specialty Genetics

Date 2016 Dec 24

PMID 28007024

Citations 32

Authors

Chengliang Dong

Yunfei Guo

Hui Yang

Zeyu He

Xiaoming Liu

Kai Wang

Affiliations

Soon will be listed here.

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 .

Citing Articles

SMG: self-supervised masked graph learning for cancer gene identification.

Cui Y, Wang Z, Wang X, Zhang Y, Zhang Y, Pan T Brief Bioinform. 2023; 24(6).

PMID: 37950905 PMC: 10639095. DOI: 10.1093/bib/bbad406.

Identifying cancer driver genes in individual tumours.

Gillman R, Field M, Schmitz U, Karamatic R, Hebbard L Comput Struct Biotechnol J. 2023; 21:5028-5038.

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A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.

Labani M, Beheshti A, Argha A, Alinejad-Rokny H Int J Mol Sci. 2023; 24(3).

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PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology.

Ulgen E, Ozisik O, Sezerman O Bioinformatics. 2023; 39(1).

PMID: 36689556 PMC: 9869653. DOI: 10.1093/bioinformatics/btad022.

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer.

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