Non-coding Cancer Driver Candidates Identified with a Sample- and Position-specific Model of the Somatic Mutation Rate

Overview

Journal Elife

Specialty Biology

Date 2017 Apr 1

PMID 28362259

Citations 23

Authors

Malene Juul

Johanna Bertl

Qianyun Guo

Morten Muhlig Nielsen

Michal Switnicki

Henrik Hornshoj

Tobias Madsen

Asger Hobolth

Jakob Skou Pedersen

Affiliations

Soon will be listed here.

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 (), where 5'UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene () 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.

Citing Articles

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Elevated incidence of somatic mutations at prevalent genetic sites.

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Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis.

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