A multi-omics approach for identifying important pathways and genes in human cancer

• Published in: BMC bioinformatics Vol. 19; no. 1; p. 479
• Main Authors: Frost, H Robert, Amos, Christopher I
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.12.2018; BioMed Central; BMC
• Subjects: Biochemistry; Biology; Cancer; Cancer genetics; Cancer genomics; Cell fate; Cell survival; Copy number; Copy number variations; Data analysis; Data processing; Development and progression; Disruption; Driver mutations; Gene expression; Gene mutation; Gene set testing; Genes; Genetic aspects; Genomes; Genomics; High-throughput screening (Biochemical assaying); Lung cancer; Mathematical models; Methodology; Mutation; Pathway analysis; Regression analysis; Researchers; Statistical analysis; Statistical models; Target recognition; Therapeutic applications; Topology; Tumors; Pathway analysis; Gene set testing; Driver mutations; Cancer genomics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2476-8

Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding how pathway disruption is driven by somatic alterations is thus essential for an accurate characterization of cancer biology and identification of therapeutic targets. Unfortunately, current cancer pathway analysis methods fail to fully model the relationship between somatic alterations and pathway activity. To address these limitations, we developed a multi-omics method for identifying biologically important pathways and genes in human cancer. Our approach combines single-sample pathway analysis with multi-stage, lasso-penalized regression to find pathways whose gene expression can be explained largely in terms of gene-level somatic alterations in the tumor. Importantly, this method can analyze case-only data sets, does not require information regarding pathway topology and supports personalized pathway analysis using just somatic alteration data for a limited number of cancer-associated genes. The practical effectiveness of this technique is illustrated through an analysis of data from The Cancer Genome Atlas using gene sets from the Molecular Signatures Database. Novel insights into the pathophysiology of human cancer can be obtained from statistical models that predict expression-based pathway activity in terms of non-silent somatic mutations and copy number variation. These models enable the identification of biologically important pathways and genes and support personalized pathway analysis in cases where gene expression data is unavailable.