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De novo mutational signature discovery in tumor genomes using SparseSignatures

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-s...

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Autores principales: Lal, Avantika, Liu, Keli, Tibshirani, Robert, Sidow, Arend, Ramazzotti, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270462/
https://www.ncbi.nlm.nih.gov/pubmed/34181655
http://dx.doi.org/10.1371/journal.pcbi.1009119
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author Lal, Avantika
Liu, Keli
Tibshirani, Robert
Sidow, Arend
Ramazzotti, Daniele
author_facet Lal, Avantika
Liu, Keli
Tibshirani, Robert
Sidow, Arend
Ramazzotti, Daniele
author_sort Lal, Avantika
collection PubMed
description Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features.
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spelling pubmed-82704622021-07-21 De novo mutational signature discovery in tumor genomes using SparseSignatures Lal, Avantika Liu, Keli Tibshirani, Robert Sidow, Arend Ramazzotti, Daniele PLoS Comput Biol Research Article Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features. Public Library of Science 2021-06-28 /pmc/articles/PMC8270462/ /pubmed/34181655 http://dx.doi.org/10.1371/journal.pcbi.1009119 Text en © 2021 Lal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lal, Avantika
Liu, Keli
Tibshirani, Robert
Sidow, Arend
Ramazzotti, Daniele
De novo mutational signature discovery in tumor genomes using SparseSignatures
title De novo mutational signature discovery in tumor genomes using SparseSignatures
title_full De novo mutational signature discovery in tumor genomes using SparseSignatures
title_fullStr De novo mutational signature discovery in tumor genomes using SparseSignatures
title_full_unstemmed De novo mutational signature discovery in tumor genomes using SparseSignatures
title_short De novo mutational signature discovery in tumor genomes using SparseSignatures
title_sort de novo mutational signature discovery in tumor genomes using sparsesignatures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270462/
https://www.ncbi.nlm.nih.gov/pubmed/34181655
http://dx.doi.org/10.1371/journal.pcbi.1009119
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