title: Using common variants to indicate cancer genes.
abstract: The catalogue of tumour-specific somatic mutations (SMs) is growing rapidly owing to the advent of next-generation sequencing. Identifying those mutations responsible for the development and progression of the disease, so-called driver mutations, will increase our understanding of carcinogenesis and provide candidates for targeted therapeutics. The phenotypic consequence(s) of driver mutations cause them to be selected for within the tumour environment, such that many approaches aimed at distinguishing drivers are based on finding significantly somatically mutated genes. Currently, these methods are designed to analyse, or be specifically applied to, nonsynonymous mutations: those that alter an encoded protein. However, growing evidence suggests the involvement of noncoding transcripts in carcinogenesis, mutations in which may also be disease-driving. We wished to test the hypothesis that common DNA variation rates within humans can be used as a baseline from which to score the rate of SMs, irrespective of coding capacity. We preliminarily tested this by applying it to a dataset of 159,498 SMs and using the results to rank genes. This resulted in significant enrichment of known cancer genes, indicating that the approach has merit. As additional data from cancer sequencing studies are made publicly available, this approach can be refined and applied to specific cancer subtypes. We named this preliminary version of our approach PRISMAD (polymorphism rates indicate somatic mutations as drivers) and have made it publicly accessible, with scripts, via a link at www.precancer.leeds.ac.uk/software-and-datasets.
authors.affiliation: Leeds Institute of Cancer and Pathology, University of Leeds, St James's University Hospital, Leeds, United Kingdom.
authors.forename: Lucy F
authors.initials: LF
authors.lastname: Stead
authors.forename: Helene
authors.initials: H
authors.lastname: Thygesen
authors.forename: David R
authors.initials: DR
authors.lastname: Westhead
authors.forename: Pamela
authors.initials: P
authors.lastname: Rabbitts
mesh_terms: D004252:DNA Mutational Analysis; D005787:Gene Frequency; D052138:Genes, Neoplasm; D055106:Genome-Wide Association Study / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D035683:MicroRNAs / Q000235:genetics; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D011110:Polymorphism, Genetic; D012984:Software
chemical_list: C561679:MIRN99 microRNA, human; D035683:MicroRNAs
keywords: cancer driver genes; next-generation sequencing; somatic mutation