title: Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level.
abstract: Driver gene selection is crucial to understand the heterogeneous system of cancer. To identity cancer driver genes, various statistical strategies have been proposed, especially the L1-type regularization methods have drawn a large amount of attention. However, the statistical approaches have been developed purely from algorithmic and statistical point, and the existing studies have applied the statistical approaches to genomic data analysis without consideration of biological knowledge. We consider a statistical strategy incorporating biological knowledge to identify cancer driver gene. The alterations of copy number have been considered to driver cancer pathogenesis processes, and the region of strong interaction of copy number alterations and expression levels was known as a tumor-related symptom. We incorporate the influence of copy number alterations on expression levels to cancer driver gene-selection processes. To quantify the dependence of copy number alterations on expression levels, we consider [Formula: see text] and [Formula: see text] effects of copy number alterations on expression levels of genes, and incorporate the symptom of tumor pathogenesis to gene-selection procedures. We then proposed an interaction-based feature-selection strategy based on the adaptive L1-type regularization and random lasso procedures. The proposed method imposes a large amount of penalty on genes corresponding to a low dependency of the two features, thus the coefficients of the genes are estimated to be small or exactly 0. It implies that the proposed method can provide biologically relevant results in cancer driver gene selection. Monte Carlo simulations and analysis of the Cancer Genome Atlas (TCGA) data show that the proposed strategy is effective for high-dimensional genomic data analysis. Furthermore, the proposed method provides reliable and biologically relevant results for cancer driver gene selection in TCGA data analysis.
authors.affiliation: 1 Faculty of Global and Science Studies, Yamaguchi University , Yamaguchi Prefecture, Japan .
authors.forename: Heewon
authors.initials: H
authors.lastname: Park
authors.affiliation: 2 Health Intelligence Center, Institute of Medical Science, University of Tokyo , Tokyo, Japan .
authors.forename: Atsushi
authors.initials: A
authors.lastname: Niida
authors.affiliation: 2 Health Intelligence Center, Institute of Medical Science, University of Tokyo , Tokyo, Japan .
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.affiliation: 3 Human Genome Center, Institute of Medical Science, University of Tokyo , Tokyo, Japan .
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D002471:Cell Transformation, Neoplastic / Q000235:genetics / Q000378:metabolism; D056915:DNA Copy Number Variations; D018628:Gene Dosage; D053263:Gene Regulatory Networks; D015894:Genome, Human; D006801:Humans; D009010:Monte Carlo Method; D009857:Oncogenes
keywords: copy number-driven expression levels; driver gene selection; interaction-based features selection; random lasso