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

Malene Juul Rasmussen; Johanna Bertl; Qianyun Guo; Morten Muhlig Nielsen; Michał Świtnicki; Henrik Hornshøj; Tobias Madsen; Asger Hobolth; Jakob Skou Pedersen

doi:10.7554/eLife.21778

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

Malene Juul Rasmussen, Johanna Bertl, Qianyun Guo, Morten Muhlig Nielsen, Michał Świtnicki, Henrik Hornshøj, Tobias Madsen, Asger Hobolth, Jakob Skou Pedersen

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

31 Citations (Scopus)

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.

Original language	English
Article number	e21778
Journal	eLife
Volume	6
ISSN	2050-084X
DOIs	https://doi.org/10.7554/eLife.21778
Publication status	Published - 31 Mar 2017

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Journal Article

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10.7554/eLife.21778Licence: CC BY

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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{\textquoteright}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.",

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Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate. / Rasmussen, Malene Juul; Bertl, Johanna; Guo, Qianyun et al.
In: eLife, Vol. 6, e21778, 31.03.2017.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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T1 - Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate

AU - Rasmussen, Malene Juul

AU - Bertl, Johanna

AU - Guo, Qianyun

AU - Nielsen, Morten Muhlig

AU - Świtnicki, Michał

AU - Hornshøj, Henrik

AU - Madsen, Tobias

AU - Hobolth, Asger

AU - Pedersen, Jakob Skou

PY - 2017/3/31

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N2 - 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.

AB - 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.

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

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