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

Abstract

Background: 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. 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. Results: We 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. Conclusions: Evaluation 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/QuaDMutNetEx under the GNU GPLv3 license.

Identification and Segregation of Genes with Improved Recurrent Neural Network Trained with Optimal Gene Level and Mutation Level Features

“…In 2020, Bokhari et al [8] proposed "QuaDMutNetEx, a new cancer driver genes discovery approach". The suggested algorithm discovers genetically similar groupings of lowfrequency genetic variants, along with many genetic makeups that aren't even detected if network connection data also isn't taken into account, according to QuaDMutNetEx's evaluation on four separate tumor sample datasets.…”

“…SNVs, penetrations or omissions (Indels), and morphological variations are among the mutations that cause cancer [1] [2] [3] [4]. Identifying driver genes whose mutation cause cancer can aid in the understanding of cancer's process that will aid throughout the development of innovative therapeutic strategies [5] [6] [7] [8]. Massive volumes of cancer genomic data have indeed been disclosed as a result of developments in nextgeneration sequencing techniques, that makes finding driver genes easier [9][10] [11][12] [13].…”

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

“…Hence, some pathways containing rare mutations may be ignored [12]. Prior knowledge-based methods regard genes with high mutation rates and their less-frequently mutated neighbors as drivers, and attempt to detect them from known gene or protein-level pathways or networks [13], such as MEXCOwalk [12], HotNet [14], IDM-SPS [15] and HotNet2 [16]. However, biological networks are still associated with noise and incomplete.…”

“…The intuition of combining these two kinds of methods, i.e., taking advantage of fundamental features of a driver pathway and gene relationships in biological networks, has germinated. In 2020, Yahya et al [13] presented method QuaDMut-NetEx, which is extended from their QuaDMutEx method by incorporating the connectivity of genes in the identification model. Experimental results indicate that method QuaDMutNetEx can identify some driver genes with low mutation rate compared with method QuaDMutEx.…”

“…Among the above mentioned identification methods, some parameters need to be preset to adjust the weight of different omics data, such as methods iMCMC [8], MOGA [9], QuaDMutEX [10], PGA-MWS [11], and QuaDMutNetEx [13]. This may limit their usability and scalability, for a large number of experiments are usually required to ascertain these parameters before applying them.…”