title: Hotspot mutations delineating diverse mutational signatures and biological utilities across cancer types.
abstract: An important step towards personalizing cancer treatment is to integrate heterogeneous evidences to catalog mutational hotspots that are biologically and therapeutically relevant and thus represent where targeted therapy would likely be beneficial. However, existing methods do not sufficiently delineate varying functionality of individual mutations within the same genes.
  
  
  
  We observed a large discordancy of mutation rates across different mutation subtypes and tumor types, and nominated 702 hotspot mutations in 549 genes in the Catalog of Somatic Mutations in Cancer (COSMIC) by considering context specific mutation characteristics such as genes, cancer types, mutation rates, mutation subtypes and sequence contexts. We observed that hotspot mutations were highly prevalent in Non CpG-island C/G transition and transversion sequence contexts in 10 tumor types, and specific insertion hotspot mutations were enriched in breast cancer and deletion hotspot mutations in colorectal cancer. We found that the hotspot mutations nominated by our approach were significantly more conserved than non-hotspot mutations in the corresponding cancer genes. We also examined the biological significance and pharmacogenomics properties of these hotspot mutations using data in the Cancer Genome Atlas (TCGA) and the Cancer Cell-Line Encyclopedia (CCLE), and found that 53 hotspot mutations are independently associated with diverse functional evidences in 1) mRNA and protein expression, 2) pathway activity, or 3) drug sensitivity and 82 were highly enriched in specific tumor types. We highlighted the distinct functional indications of hotspot mutations under different contexts and nominated novel hotspot mutations such as MAP3K4 A1199 deletion, NR1H2 Q175 insertion, and GATA3 P409 insertion as potential biomarkers or drug targets.
  
  
  
  We identified a set of hotspot mutations across 17 tumor types by considering the background mutation rate variations among genes, tumor subtypes, mutation subtypes, and sequence contexts. We illustrated the common and distinct mutational signatures of hotspot mutations among different tumor types and investigated their variable functional relevance under different contexts, which could potentially serve as a resource for explicitly selecting targets for diagnosis, drug development, and patient management.
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Biostatistics, Bioinformatics and Systems Biology Program, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
authors.forename: Tenghui
authors.initials: T
authors.lastname: Chen
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
authors.forename: Zixing
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
authors.forename: Wanding
authors.initials: W
authors.lastname: Zhou
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
authors.forename: Zechen
authors.initials: Z
authors.lastname: Chong
authors.affiliation: Department of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
authors.forename: Funda
authors.initials: F
authors.lastname: Meric-Bernstam
authors.affiliation: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Biostatistics, Bioinformatics and Systems Biology Program, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. KChen3@mdanderson.org.|Biostatistics, Bioinformatics and Systems Biology Program, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA. KChen3@mdanderson.org.
authors.forename: Ken
authors.initials: K
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D009154:Mutation; D059645:Mutation Rate; D009369:Neoplasms / Q000235:genetics*; D057285:Precision Medicine; D017422:Sequence Analysis, DNA / Q000379:methods