Characterizing mutation–expression network relationships in multiple cancers. (August 2016)

• Record Type: Journal Article
• Authors: Ghazanfar, Shila; Yang, Jean Yee Hwa
• Is Part Of: Computational biology and chemistry. Volume 63(2016)
• Page Start: 73
• Page End: 82
• Publication Date: 2016-08
• Subjects: CGC Cancer Gene Census -- DE differential expression -- FDR false discovery rate -- GEO Gene Expression Omnibus -- HGNC HUGO Gene Nomenclature Committee -- KEGG Kyoto Encyclopedia of Genes and Genomes -- NCG Network of Cancer Genes -- NMF non-negative matrix factorization -- PPI Protein–protein interaction -- UPPI union protein–protein interaction -- TCGA The Cancer Genome Atlas -- WGCNA Weighted Gene Correlation Network Analysis -- WGS whole genome sequencing -- WES whole exome sequencing; Mutation -- Gene expression -- Protein–protein interaction network -- Pan-cancer; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.02.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Background: Data made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics. Results: Using somatic mutation and gene expression information across 19 cancers, we generated mutation–expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma ( p < 0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load. Conclusions: This study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation–expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.