Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate

  1. Malene Juul  Is a corresponding author
  2. Johanna Bertl
  3. Qianyun Guo
  4. Morten Muhlig Nielsen
  5. Michał Świtnicki
  6. Henrik Hornshøj
  7. Tobias Madsen
  8. Asger Hobolth
  9. Jakob Skou Pedersen  Is a corresponding author
  1. Aarhus University Hospital, Denmark
  2. 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

  1. Malene Juul

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    For correspondence
    malene.juul.rasmussen@clin.au.dk
    Competing interests
    The authors declare that no competing interests exist.
  2. Johanna Bertl

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  3. Qianyun Guo

    Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  4. Morten Muhlig Nielsen

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  5. Michał Świtnicki

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  6. Henrik Hornshøj

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  7. Tobias Madsen

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  8. Asger Hobolth

    Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  9. Jakob Skou Pedersen

    Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    For correspondence
    jakob.skou@clin.au.dk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7236-4001

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.

Reviewing Editor

  1. Gilean McVean, Oxford University, United Kingdom

Publication history

  1. Received: September 24, 2016
  2. Accepted: March 14, 2017
  3. Accepted Manuscript published: March 31, 2017 (version 1)
  4. Accepted Manuscript updated: April 7, 2017 (version 2)
  5. Version of Record published: May 22, 2017 (version 3)

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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  1. Malene Juul
  2. Johanna Bertl
  3. Qianyun Guo
  4. Morten Muhlig Nielsen
  5. Michał Świtnicki
  6. Henrik Hornshøj
  7. Tobias Madsen
  8. Asger Hobolth
  9. Jakob Skou Pedersen
(2017)
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

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