title: Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes.
abstract: Protein tertiary structure determines molecular function, interaction, and stability of the protein, therefore distribution of mutation in the tertiary structure can facilitate the identification of new driver genes in cancer. To analyze mutation distribution in protein tertiary structures, we applied a novel three dimensional permutation test to the mutation positions. We analyzed somatic mutation datasets of 21 types of cancers obtained from exome sequencing conducted by the TCGA project. Of the 3,622 genes that had ≥3 mutations in the regions with tertiary structure data, 106 genes showed significant skew in mutation distribution. Known tumor suppressors and oncogenes were significantly enriched in these identified cancer gene sets. Physical distances between mutations in known oncogenes were significantly smaller than those of tumor suppressors. Twenty-three genes were detected in multiple cancers. Candidate genes with significant skew of the 3D mutation distribution included kinases (MAPK1, EPHA5, ERBB3, and ERBB4), an apoptosis related gene (APP), an RNA splicing factor (SF1), a miRNA processing factor (DICER1), an E3 ubiquitin ligase (CUL1) and transcription factors (KLF5 and EEF1B2). Our study suggests that systematic analysis of mutation distribution in the tertiary protein structure can help identify cancer driver genes.
authors.affiliation: Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Japan.|Department of Drug Discovery Medicine, Graduate School of Medicine, Kyoto University, Japan.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Fujimoto
authors.affiliation: Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Japan.|Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Japan.
authors.forename: Yukinori
authors.initials: Y
authors.lastname: Okada
authors.affiliation: Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Japan.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Boroevich
authors.affiliation: Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Japan.
authors.forename: Tatsuhiko
authors.initials: T
authors.lastname: Tsunoda
authors.affiliation: Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Japan.
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Taniguchi
authors.affiliation: Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Japan.
authors.forename: Hidewaki
authors.initials: H
authors.lastname: Nakagawa
mesh_terms: D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D059472:Exome; D053263:Gene Regulatory Networks; D006801:Humans; D008958:Models, Molecular; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D015513:Oncogene Proteins / Q000737:chemistry* / Q000235:genetics; D055550:Protein Stability; D017434:Protein Structure, Tertiary; D025521:Tumor Suppressor Proteins / Q000737:chemistry* / Q000235:genetics; D000073359:Exome Sequencing
chemical_list: D015513:Oncogene Proteins; D025521:Tumor Suppressor Proteins