QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

Yahya Bokhari,Tomasz Arodz,Areej Alhareeri

doi:10.1186/s12859-020-3449-2

Abstract

BackgroundCancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors – passenger mutations – dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations.ResultsWe propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples.ConclusionsEvaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https://github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.

Highlights

Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers
An analysis of large number of cancer samples gathered in the Cancer Genome Atlas (TCGA) [10] shows that the total number of mutations present in a tumor tissue from a single patient can range from 10 to more than 100, and only about 2 to 6 among them are driver mutations [11]
We evaluated QuaDMutNetEx using its default parameters that have been selected experimentally: the maximum size of the gene set is ν = 50; k = 1, indicating equal preference for optimizing coverage and excess coverage; C = 2.5; the network parameter was set to α = 0.3; the number of iterations was set to T = 10, 000

Summary

Introduction

Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Mutation rate tends to increase in cancer cells [8], it can differ significantly even among subclones within the tumor [9] Most of these new random mutations do not contribute to the progression of the disease. An analysis of large number of cancer samples gathered in the Cancer Genome Atlas (TCGA) [10] shows that the total number of mutations present in a tumor tissue from a single patient can range from 10 to more than 100, and only about 2 to 6 among them are driver mutations [11]. The main challenge in this task is discovering new driver genes while avoiding false positives stemming from the abundance of passenger mutations

Methods

Results

Discussion

Conclusion