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dc.contributor.authorRubanova, Yulia
dc.contributor.authorShi, Ruian
dc.contributor.authorHarrigan, Caitlin F
dc.contributor.authorLi, Roujia
dc.contributor.authorWintersinger, Jeff
dc.contributor.authorSahin, Nil
dc.contributor.authorDeshwar, Amit
dc.contributor.authorPCAWG Evolution and Heterogeneity Working Group
dc.contributor.authorMorris, Quaid
dc.contributor.authorPCAWG Consortium
dc.date.accessioned2021-02-04T16:24:57Z
dc.date.available2021-02-04T16:24:57Z
dc.date.issued2020-02-05
dc.date.submitted2018-11-22
dc.identifier.issn2041-1723
dc.identifier.others41467-020-14352-7
dc.identifier.other14352
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317152
dc.description.abstractThe type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArticle
dc.subject/631/67/68
dc.subject/631/114/2397
dc.subjectarticle
dc.titleReconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.
dc.typeArticle
dc.date.updated2021-02-04T16:24:56Z
prism.issueIdentifier1
prism.publicationNameNat Commun
prism.volume11
dc.identifier.doi10.17863/CAM.64263
dcterms.dateAccepted2019-12-23
rioxxterms.versionofrecord10.1038/s41467-020-14352-7
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidHarrigan, Caitlin F [0000-0002-9243-9648]
dc.contributor.orcidMorris, Quaid [0000-0002-2760-6999]
dc.identifier.eissn2041-1723
cam.issuedOnline2020-02-05


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)