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Analysis of Mutational Signatures Using the mutSignatures R Library

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Urothelial Carcinoma

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2684))

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Abstract

Accumulation of somatic mutations is a hallmark of cancer. Defects in DNA metabolism and DNA repair and exposure to mutagens may result in characteristic nonrandom profiles of DNA mutations, also known as mutational signatures. Resolving mutational signatures can help identifying genetic instability processes active in human cancer samples, and there is an expectation that this information might be exploited in the future for drug discovery and personalized treatment.

Here we show how to analyze bladder cancer mutation data using mutSignatures, an open-source R-based computational framework aimed at investigating DNA mutational signatures. We illustrate the typical steps of a mutational signature analysis. We start by importing and pre-processing mutation data from a list of Variant Call Format (VCF) files. Next, we show how to perform de novo mutational signature extraction and how to determine activity of previously resolved mutational signatures, including Catalogue of Somatic Mutations In Cancer (COSMIC) signatures. Finally, we provide insights into parameter selection, algorithm tuning, and data visualization.

Overall, the chapter guides the reader through all steps of a mutational signature analysis using R and mutSignatures, a software that may help gathering insights into genetic instability and cancer biology.

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References

  1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674

    Article  CAS  PubMed  Google Scholar 

  2. Stratton MR, Campbell PJ, Futreal PA (2009) The cancer genome. Nature 458:719–724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brash DE (2015) UV Signature Mutations. Photochem Photobiol 91:15–26

    Article  CAS  PubMed  Google Scholar 

  4. Langenbucher A, Bowen D, Sakhtemani R et al (2021) An extended APOBEC3A mutation signature in cancer. Nat Commun 12:1602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nik-Zainal S, Alexandrov LB, Wedge DC et al (2012) Mutational processes molding the genomes of 21 breast cancers. Cell 149:979–993

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791

    Article  CAS  PubMed  Google Scholar 

  7. Fantini D, Seiler R, Meeks JJ (2019) Molecular footprints of muscle-invasive bladder cancer in smoking and nonsmoking patients. Urol Oncol Semin Orig Investig 37:818–825

    CAS  Google Scholar 

  8. Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500:415–421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fantini D, Vidimar V, Yu Y et al (2020) MutSignatures: an R package for extraction and analysis of cancer mutational signatures. Sci Rep 10:18217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fantini D, Glaser AP, Rimar KJ et al (2018) A carcinogen-induced mouse model recapitulates the molecular alterations of human muscle invasive bladder cancer. Oncogene 37:1911–1925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. R Core Team (2020). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/. Accessed 9 Sep 2022

  12. Robertson AG, Kim J, Al-Ahmadie H et al (2017) Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171:540–556.e25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. BSgenome.Hsapiens.UCSC.hg19. In: Bioconductor. http://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg19/. Accessed 9 Sep 2022

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Acknowledgements

This work was supported by NIH grant BX003692 and from the John P. Hanson Foundation for Cancer Research at the Robert H. Lurie Comprehensive Cancer Center of Northwestern University. The authors would like to thank Yanni Yu and Khyati Ashwin Meghani for their assistance in preparing this work.

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Correspondence to Joshua J. Meeks .

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Fantini, D., Meeks, J.J. (2023). Analysis of Mutational Signatures Using the mutSignatures R Library. In: Hoffmann, M.J., Gaisa, N.T., Nawroth, R., Ecke, T.H. (eds) Urothelial Carcinoma. Methods in Molecular Biology, vol 2684. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3291-8_3

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  • DOI: https://doi.org/10.1007/978-1-0716-3291-8_3

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3290-1

  • Online ISBN: 978-1-0716-3291-8

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