An Effective Graph Clustering Method to Identify Cancer Driver Modules

Abstract

Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.

Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

“…The STRING database 2 was used to analyze protein–protein interactions (PPI) among ferroptosis modulators. In this study, we propose an MCL cluster to identify cancer driver modules that combine mutex, functional similarity, and connectivity in the PPI network ( Zhang et al, 2020 ). Pearson correlation analysis was used to reveal the associations among different regulators in different modules.…”

Definition of a New Metric With Mutual Exclusivity and Coverage for Identifying Cancer Driver Modules

“…With the development of high-throughput sequencing technology, massive amounts of DNA sequencing data have been released, making it feasible to comprehensively analyze cancer-related somatic mutation data from the data level. In this process, the key challenge is to identify driver gene sets with biologically similar functions, that is, driver pathways or modules, which are often disturbed in cancer cells and lead to products of tumorigenic properties [1,6,7].…”