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    Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

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    Authors
    Islam, S. M. A.
    Díaz-Gay, M.
    Wu, Y.
    Barnes, M.
    Vangara, R.
    Bergstrom, E. N.
    He, Y.
    Vella, M.
    Wang, J.
    Teague, J. W.
    Clapham, P.
    Moody, S.
    Senkin, S.
    Li, Y. R.
    Riva, L.
    Zhang, T.
    Gruber, Andreas J
    Steele, C. D.
    Otlu, B.
    Khandekar, A.
    Abbasi, A.
    Humphreys, L.
    Syulyukina, N.
    Brady, S. W.
    Alexandrov, B. S.
    Pillay, N.
    Zhang, J.
    Adams, D. J.
    Martincorena, I.
    Wedge, David C
    Landi, M. T.
    Brennan, P.
    Stratton, M. R.
    Rozen, S. G.
    Alexandrov, L. B.
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    Affiliation
    Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
    Issue Date
    2022
    
    Metadata
    Show full item record
    Abstract
    Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.
    Citation
    Islam SMA, Díaz-Gay M, Wu Y, Barnes M, Vangara R, Bergstrom EN, et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell genomics. 2022 Nov 9;2(11):None. PubMed PMID: 36388765. Pubmed Central PMCID: PMC9646490. Epub 2022/11/18. eng.
    Journal
    Cell Genomics
    URI
    http://hdl.handle.net/10541/625856
    DOI
    10.1016/j.xgen.2022.100179
    PubMed ID
    36388765
    Additional Links
    https://dx.doi.org/10.1016/j.xgen.2022.100179
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.xgen.2022.100179
    Scopus Count
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    All Paterson Institute for Cancer Research

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