Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate
- Aarhus University Hospital, Denmark
- Aarhus University, Denmark
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 associate with altered expression or decreased patient survival across an independent pan-cancer sample set (n=5,454). 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.
Article and author information
Author details
Jakob Skou Pedersen
Funding
Medical Sciences (Sapere Aude Grant,#12-126439)
- Jakob Skou Pedersen
The Danish Council for Strategic Research (#10-092320/DSF)
- Jakob Skou Pedersen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2017, Juul et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate
eLife 6:e21778.
https://doi.org/10.7554/eLife.21778
