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mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies

Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational s...

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Autores principales: Rustad, Even H., Nadeu, Ferran, Angelopoulos, Nicos, Ziccheddu, Bachisio, Bolli, Niccolò, Puente, Xose S., Campo, Elias, Landgren, Ola, Maura, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007623/
https://www.ncbi.nlm.nih.gov/pubmed/33782531
http://dx.doi.org/10.1038/s42003-021-01938-0
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author Rustad, Even H.
Nadeu, Ferran
Angelopoulos, Nicos
Ziccheddu, Bachisio
Bolli, Niccolò
Puente, Xose S.
Campo, Elias
Landgren, Ola
Maura, Francesco
author_facet Rustad, Even H.
Nadeu, Ferran
Angelopoulos, Nicos
Ziccheddu, Bachisio
Bolli, Niccolò
Puente, Xose S.
Campo, Elias
Landgren, Ola
Maura, Francesco
author_sort Rustad, Even H.
collection PubMed
description Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic.
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spelling pubmed-80076232021-04-16 mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies Rustad, Even H. Nadeu, Ferran Angelopoulos, Nicos Ziccheddu, Bachisio Bolli, Niccolò Puente, Xose S. Campo, Elias Landgren, Ola Maura, Francesco Commun Biol Article Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic. Nature Publishing Group UK 2021-03-29 /pmc/articles/PMC8007623/ /pubmed/33782531 http://dx.doi.org/10.1038/s42003-021-01938-0 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Rustad, Even H.
Nadeu, Ferran
Angelopoulos, Nicos
Ziccheddu, Bachisio
Bolli, Niccolò
Puente, Xose S.
Campo, Elias
Landgren, Ola
Maura, Francesco
mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title_full mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title_fullStr mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title_full_unstemmed mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title_short mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
title_sort mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007623/
https://www.ncbi.nlm.nih.gov/pubmed/33782531
http://dx.doi.org/10.1038/s42003-021-01938-0
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