title: Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate.
abstract: Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5'UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance.
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Malene
authors.identifier: 0000-0001-9722-0461
authors.initials: M
authors.lastname: Juul
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Johanna
authors.initials: J
authors.lastname: Bertl
authors.affiliation: Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark.
authors.forename: Qianyun
authors.initials: Q
authors.lastname: Guo
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Morten Muhlig
authors.initials: MM
authors.lastname: Nielsen
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Michał
authors.initials: M
authors.lastname: Świtnicki
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Henrik
authors.initials: H
authors.lastname: Hornshøj
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Tobias
authors.initials: T
authors.lastname: Madsen
authors.affiliation: Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark.
authors.forename: Asger
authors.initials: A
authors.lastname: Hobolth
authors.affiliation: Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.|Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark.
authors.forename: Jakob Skou
authors.identifier: 0000-0002-7236-4001
authors.initials: JS
authors.lastname: Pedersen
mesh_terms: D056808:Biostatistics / Q000379:methods; D063646:Carcinogenesis; D020869:Gene Expression Profiling; D006801:Humans; D009154:Mutation; D059645:Mutation Rate; D009369:Neoplasms / Q000473:pathology* / Q000503:physiopathology*; D016019:Survival Analysis
keywords: cancer; cancer biology; computational biology; driver detection; human; mutational processes; non-coding mutations; systems biology