SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations

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Abstract

Background

Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development. Several approaches have been developed to identify highly mutated regions, but these do not take into account the specific genomic context, and thus likelihood of mutation, of CREs.

Results

Here, we present SMuRF (Significantly Mutated Region Finder), a user-friendly command-line tool to identify these significantly mutated regions from user-defined genomic intervals and SNVs. We demonstrate this using publicly available datasets in which SMuRF identifies 72 significantly mutated CREs in liver cancer, including known mutated gene promoters as well as previously unreported regions.

Conclusions

SMuRF is a helpful tool to allow the simple identification of significantly mutated regulatory elements. It is open-source and freely available on GitHub ( https://github.com/LupienLab/SMURF).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2501-y) contains supplementary material, which is available to authorized users.

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Most cited references 5

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Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.

Akihiro Fujimoto, Mayuko Furuta, Yasushi Totoki … (2016)

Liver cancer, which is most often associated with virus infection, is prevalent worldwide, and its underlying etiology and genomic structure are heterogeneous. Here we provide a whole-genome landscape of somatic alterations in 300 liver cancers from Japanese individuals. Our comprehensive analysis identified point mutations, structural variations (STVs), and virus integrations, in noncoding and coding regions. We discovered mutational signatures related to liver carcinogenesis and recurrently mutated coding and noncoding regions, such as long intergenic noncoding RNA genes (NEAT1 and MALAT1), promoters, CTCF-binding sites, and regulatory regions. STV analysis found a significant association with replication timing and identified known (CDKN2A, CCND1, APC, and TERT) and new (ASH1L, NCOR1, and MACROD2) cancer-related genes that were recurrently affected by STVs, leading to altered expression. These results emphasize the value of whole-genome sequencing analysis in discovering cancer driver mutations and understanding comprehensive molecular profiles of liver cancer, especially with regard to STVs and noncoding mutations.

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Genome-wide analysis of non-coding regulatory mutations in cancer

Nils Weinhold, Anders Jacobsen, Nikolaus Schultz … (2014)

Cancer primarily develops due to somatic alterations in the genome. Advances in sequencing have enabled large-scale sequencing studies across many tumor types, emphasizing discovery of alterations in protein-coding genes. However, the protein-coding exome comprises less than 2% of the human genome. Here, we analyze complete genome sequences of 863 human tumors from The Cancer Genome Atlas and other sources to systematically identify non-coding regions that are recurrently mutated in cancer. We utilize novel frequency and sequence-based approaches to comprehensively scan the genome for non-coding mutations with potential regulatory impact. We identified recurrent mutations in regulatory elements upstream of PLEKHS1, WDR74, and SDHD, as well as previously identified mutations in the TERT promoter. SDHD promoter mutations are frequent in melanoma and associated with reduced gene expression and poor patient prognosis. The non-protein-coding cancer genome remains widely unexplored and our findings represent a step towards targeting the entire genome for clinical purposes.

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Cell-of-origin chromatin organization shapes the mutational landscape of cancer

Paz Polak, Rosa Karlic, Amnon Koren … (2015)

Cancer is a disease potentiated by mutations in somatic cells. Cancer mutations are not distributed uniformly along the genome. Instead, different genomic regions vary by up to 5-fold in the local density of somatic mutations 1 , posing a fundamental problem for statistical methods of cancer genomics. Epigenomic organization has been proposed as a major determinant of the cancer mutational landscape 1-5 . However, both somatic mutagenesis and epigenomic features are highly cell-type-specific 6,7 . We investigated the distribution of mutations in multiple samples of diverse cancer types and compared them to cell-type-specific epigenomic features. Here, we show that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes. Overwhelmingly, the best predictors of local somatic mutation density are epigenomic features derived from the most likely cell type of origin of the corresponding malignancy. Moreover, we find that cell-of-origin chromatin features are much stronger determinants of cancer mutation profiles than chromatin features of cognate cancer cell lines. We show further that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome. Thus, DNA sequence of a cancer genome encompasses a wealth of information about the identity and epigenomic features of its cell of origin.

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Author and article information

Contributors

Paul Guilhamon: paul.guilhamon@sickkids.ca

Mathieu Lupien:

ORCID: http://orcid.org/0000-0003-0929-9478

Mathieu.Lupien@uhnresearch.ca

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 26 November 2018

Publication date PMC-release: 26 November 2018

Publication date Collection: 2018

Volume: 19

Electronic Location Identifier: 454

Affiliations

[1 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Princess Margaret Cancer Centre, The MaRS Center, , University Health Network, ; 101 College Street, Toronto, ON M5G 1L7 Canada

[2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Medical Biophysics, , University of Toronto, ; Toronto, ON Canada

[3 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Ontario Institute for Cancer Research, ; Toronto, ON Canada

Author information

Mathieu Lupien http://orcid.org/0000-0003-0929-9478

Article

Publisher ID: 2501

DOI: 10.1186/s12859-018-2501-y

PMC ID: 6258448

PubMed ID: 30477433

SO-VID: 6c3000ee-ac8c-47b5-a5d3-3b2ca0c1e445

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 29 June 2018

Date accepted : 16 November 2018

Funding

Funded by: Stand Up To Cancer (CDN)

Award ID: SU2C-AACR-DT-19-15

Award Recipient : Mathieu Lupien

Funded by: FundRef http://dx.doi.org/10.13039/501100000109, Prostate Cancer Canada;

Award ID: RS2014-04

Award Recipient : Mathieu Lupien

Funded by: FundRef http://dx.doi.org/10.13039/501100000027, Institute of Cancer Research;

Award ID: MFE 338954

Award Recipient : Paul Guilhamon

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: cis-regulatory elements,mutations,cancer,enrichment,transcriptional regulation

Data availability:

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: cis-regulatory elements, mutations, cancer, enrichment, transcriptional regulation

SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations

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Abstract

Background

Results

Conclusions

Electronic supplementary material

Related collections

Computational epistasis

Most cited references 5

Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.

Genome-wide analysis of non-coding regulatory mutations in cancer

Cell-of-origin chromatin organization shapes the mutational landscape of cancer

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