Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.

Bo Gao,Juntao Liu,Guojun Li,Yue Zhao,Yang Li,Lushan Wang,Zhengchang Su

doi:10.1002/advs.201801384

Abstract

Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample‐specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software is available at: https://sourceforge.net/projects/cancer‐pathway/files/.

Highlights

IntroductionThe existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples
CoMEt suffers from high computational complexity of the proposed metric evaluating the balance of mutual exclusivity, and limits its applications. To overcome this limitation and to further improve the accuracy of driver module identification, we have developed UniCovEx
To evaluate the performance of our method, we compared it with two state-of-the-art methods CovEx[27] and HotNet2[29] on 3106 samples for 12 cancer types (Table S1, Supporting Information) using 1571 cancer genes documented in the NCG 5.0 repository[37] and three proteinprotein interaction (PPI) networks, HINT+HI2012, iRefIndex, and Multinet

Summary

Introduction

The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. Y. Zhao IAM MADIS NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190, China known pathways or networks. Zhao IAM MADIS NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190, China known pathways or networks These methods can be limited by the currently incomplete known pathways and interaction networks in databases.[12,13]

Results

Discussion

Conclusion