Identification of Cancer Driver Modules Based on Graph Clustering from Multiomics Data.

Abstract

A major challenge in cancer genomics is to identify cancer driver genes and modules. Most existing methods to identify cancer driver modules (iCDM) identify groups of genes whose somatic mutational patterns exhibit either mutual exclusivity or high coverage of patient samples, without considering other biological information from multiomics data sets. Here we integrate mutual exclusivity, coverage, and protein-protein interaction information to construct an edge-weighted network, and present a graph clustering approach based on symmetric non-negative matrix factorization to iCDM. iCDM was tested on pan-cancer data and the results were compared with those from several advanced computational methods. Our approach outperformed other methods in recovering known cancer driver modules, and the identified driver modules showed high accuracy in classifying normal and tumor samples.

• Authors: Wei Zhang, Shu-Lin Wang, Yue Liu
• Journal: Journal of computational biology : a journal of computational molecular cell biology (J Comput Biol) Vol. 28 Issue 10 Pg. 1007-1020 (Oct 2021) ISSN: 1557-8666 [Electronic] United States
• PMID: 34529511 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor

Topics

• Algorithms
• Biomarkers, Tumor (genetics)
• Cluster Analysis
• Computational Biology (methods)
• Databases, Genetic
• Gene Regulatory Networks
• Genetic Predisposition to Disease
• Humans
• Neoplasms (genetics)
• Protein Interaction Mapping