Identifying Cancer Specific Driver Modules Using a Network-Based Method

Feng Li,Peizhuo Wang,Lin Gao,Yuxuan Hu

doi:10.3390/molecules23051114

Abstract

Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git.

Highlights

Cancer is considered as a complex disease driven by genome alterations that include gene mutations, copy number alterations, and so on [1,2]
We propose a network-based method to detect cancer specific driver modules (CSDM), which can catch the specificity of a certain cancer type to other cancer types at the pathway level
We investigate the overlaps between the specific driver modules of every two different cancer types

Summary

Introduction

Cancer is considered as a complex disease driven by genome alterations that include gene mutations, copy number alterations, and so on [1,2]. Tokheim et al [12] propose a machine learning-based method for driver gene prediction, and establish an evaluation framework to compare the performance of eight prediction methods They show that driver genes predicted by each of the eight methods vary widely, and most current methods do not fully consider the mutational heterogeneity [12]. Some other methods prioritize driver genes based on mutation data and functional networks [14,15] or matrix factorization framework [16,17,18,19,20] These methods do not consider the complicated mutational heterogeneity among patients [3,21,22,23]. Since the genes with driver mutations always work together in cellular signaling and regulatory pathways [21,24], detecting driver pathways, driver modules or driver gene sets, with genes possessing driver mutations, can consider this complicated mutational heterogeneity and provide an understanding of carcinogenesis at the pathway level

Methods

Results

Conclusion