title: Pathway-driven discovery of rare mutational impact on cancer.
abstract: Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene itself. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; the philosophy of our method is iteratively combining rare mutations until no more mutations can be added under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutational events. Breast cancer patients with somatic mutation and mRNA expression were analyzed by our approach. Our approach is shown to sensitively capture mutations that change pathway level mRNA expression, concurrently discovering important mutations previously reported in breast cancer such as TP53, PIK3CA, and RB1. In addition, out of 15,819 genes considered in breast cancer, our approach identified mutational events of 32 genes showing pathway level mRNA expression differences.
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul 151-742, Republic of Korea ; Samsung Genome Institute, Samsung Medical Center, Irwon-ro 81, Seoul 136-710, Republic of Korea.
authors.forename: TaeJin
authors.identifier: 0000-0001-5165-2744
authors.initials: T
authors.lastname: Ahn
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul 151-742, Republic of Korea ; Department of Statistics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul 151-742, Republic of Korea.
authors.forename: Taesung
authors.identifier: 0000-0002-8294-590X
authors.initials: T
authors.lastname: Park
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D058534:Class I Phosphatidylinositol 3-Kinases; D019295:Computational Biology; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009154:Mutation; D019869:Phosphatidylinositol 3-Kinases / Q000096:biosynthesis; D012333:RNA, Messenger / Q000096:biosynthesis*; D016160:Retinoblastoma Protein / Q000096:biosynthesis; D015398:Signal Transduction / Q000235:genetics; D016159:Tumor Suppressor Protein p53 / Q000096:biosynthesis
chemical_list: D012333:RNA, Messenger; D016160:Retinoblastoma Protein; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; D019869:Phosphatidylinositol 3-Kinases; D058534:Class I Phosphatidylinositol 3-Kinases; C484760:PIK3CA protein, human