Discovery of co-occurring driver pathways in cancer.

Abstract

BACKGROUND:

It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.

RESULTS:

In this study, an exact mathematical programming method was proposed to de novo identify co-occurring mutated driver pathways (CoMDP) in carcinogenesis without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis.

CONCLUSIONS:

The present method can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.

• Authors: Junhua Zhang, Ling-Yun Wu, Xiang-Sun Zhang, Shihua Zhang
• Journal: BMC bioinformatics (BMC Bioinformatics) Vol. 15 Pg. 271 (Aug 09 2014) ISSN: 1471-2105 [Electronic] England
• PMID: 25106096 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Algorithms
• Carcinogenesis (genetics)
• Disease Progression
• Humans
• Mutation
• Neoplasms (genetics, pathology)
• Signal Transduction (genetics)
• Software
• Systems Biology (methods)