A mixture model for signature discovery from sparse mutation data

Abstract Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. T...

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Autores principales:	Itay Sason, Yuexi Chen, Mark D.M. Leiserson, Roded Sharan
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Mutational signatures Probabilistic modeling Gene panel sequencing Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/1eee8e0e684b4c5797b96c3ffaa77b66
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id	oai:doaj.org-article:1eee8e0e684b4c5797b96c3ffaa77b66
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spelling	oai:doaj.org-article:1eee8e0e684b4c5797b96c3ffaa77b662021-11-08T10:57:56ZA mixture model for signature discovery from sparse mutation data10.1186/s13073-021-00988-71756-994Xhttps://doaj.org/article/1eee8e0e684b4c5797b96c3ffaa77b662021-11-01T00:00:00Zhttps://doi.org/10.1186/s13073-021-00988-7https://doaj.org/toc/1756-994XAbstract Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .Itay SasonYuexi ChenMark D.M. LeisersonRoded SharanBMCarticleMutational signaturesProbabilistic modelingGene panel sequencingMedicineRGeneticsQH426-470ENGenome Medicine, Vol 13, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mutational signatures Probabilistic modeling Gene panel sequencing Medicine R Genetics QH426-470
spellingShingle	Mutational signatures Probabilistic modeling Gene panel sequencing Medicine R Genetics QH426-470 Itay Sason Yuexi Chen Mark D.M. Leiserson Roded Sharan A mixture model for signature discovery from sparse mutation data
description	Abstract Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .
format	article
author	Itay Sason Yuexi Chen Mark D.M. Leiserson Roded Sharan
author_facet	Itay Sason Yuexi Chen Mark D.M. Leiserson Roded Sharan
author_sort	Itay Sason
title	A mixture model for signature discovery from sparse mutation data
title_short	A mixture model for signature discovery from sparse mutation data
title_full	A mixture model for signature discovery from sparse mutation data
title_fullStr	A mixture model for signature discovery from sparse mutation data
title_full_unstemmed	A mixture model for signature discovery from sparse mutation data
title_sort	mixture model for signature discovery from sparse mutation data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/1eee8e0e684b4c5797b96c3ffaa77b66
work_keys_str_mv	AT itaysason amixturemodelforsignaturediscoveryfromsparsemutationdata AT yuexichen amixturemodelforsignaturediscoveryfromsparsemutationdata AT markdmleiserson amixturemodelforsignaturediscoveryfromsparsemutationdata AT rodedsharan amixturemodelforsignaturediscoveryfromsparsemutationdata AT itaysason mixturemodelforsignaturediscoveryfromsparsemutationdata AT yuexichen mixturemodelforsignaturediscoveryfromsparsemutationdata AT markdmleiserson mixturemodelforsignaturediscoveryfromsparsemutationdata AT rodedsharan mixturemodelforsignaturediscoveryfromsparsemutationdata
_version_	1718442404859609088

A mixture model for signature discovery from sparse mutation data

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