Discovery of co-occurring driver pathways in cancer.

Junhua Zhang,Xiang-Sun Zhang,Ling-Yun Wu,Shihua Zhang

doi:10.1186/1471-2105-15-271

Abstract

BackgroundIt 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.ResultsIn 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-271) contains supplementary material, which is available to authorized users.

Highlights

It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis
co-occurring mutated driver pathways (CoMDP) usually degenerates to find one gene set which corresponds to the optimal solution of binary linear programming (BLP)
These two sets constitute one of the embedded gene sets which can be found using the BLP method, so they can be viewed as the same driver gene set as obtained using BLP directly

Summary

Introduction

It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. To improve the diagnosis and treatment of cancer patients, several large-scale cancer genomics projects (e.g., the Cancer Genome Atlas (TCGA) [1], and International Cancer Genome Consortium (ICGC) [2]) have been performed in recent years Analyzing these high-throughput data provides valuable opportunities to understand the formation and progression of cancer [3,4]. Mutations of the genes in one pathway usually exhibit mutual exclusivity, i.e., a single mutation is usually enough to disturb one pathway [12,13] These rules have been frequently used to detect driver pathways and modules [14,15,16]. Ciriello et al proposed MEMo (Mutual Exclusivity Modules) to detect oncogenic network modules within a constructed network using gene mutation information and a human reference network (including protein interactions and signal transduction pathways) [14]

Methods

Results

Conclusion