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dc.contributor.authorCmero, Mareken
dc.contributor.authorYuan, Keen
dc.contributor.authorOng, Cheng Soonen
dc.contributor.authorSchröder, Janen
dc.contributor.authorPCAWG Evolution and Heterogeneity Working Group,en
dc.contributor.authorCorcoran, Niall Men
dc.contributor.authorPapenfuss, Tonyen
dc.contributor.authorHovens, Christopher Men
dc.contributor.authorMarkowetz, Florianen
dc.contributor.authorMacintyre, Geoffen
dc.contributor.authorPCAWG Consortium,en
dc.date.accessioned2019-12-19T00:31:02Z
dc.date.available2019-12-19T00:31:02Z
dc.date.issued2020-02-05en
dc.identifier.issn2041-1723
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/300096
dc.description.abstractWe present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.
dc.format.mediumElectronicen
dc.languageengen
dc.publisherSpringer Nature
dc.rightsAll rights reserved
dc.rights.uri
dc.subjectPCAWG Evolution and Heterogeneity Working Groupen
dc.subjectPCAWG Consortiumen
dc.subjectHumansen
dc.subjectNeoplasmsen
dc.subjectLiver Neoplasmsen
dc.subjectPancreatic Neoplasmsen
dc.subjectOvarian Neoplasmsen
dc.subjectProstatic Neoplasmsen
dc.subjectSensitivity and Specificityen
dc.subjectComputational Biologyen
dc.subjectGene Frequencyen
dc.subjectGenome, Humanen
dc.subjectAlgorithmsen
dc.subjectComputer Simulationen
dc.subjectFemaleen
dc.subjectMaleen
dc.subjectDNA Copy Number Variationsen
dc.subjectWhole Genome Sequencingen
dc.titleInferring structural variant cancer cell fraction.en
dc.typeArticle
prism.issueIdentifier1en
prism.publicationDate2020en
prism.publicationNameNature communicationsen
prism.startingPage730
prism.volume11en
dc.identifier.doi10.17863/CAM.47170
dcterms.dateAccepted2019-12-16en
rioxxterms.versionofrecord10.1038/s41467-020-14351-8en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-02-05en
dc.contributor.orcidCmero, Marek [0000-0001-7783-5530]
dc.contributor.orcidYuan, Ke [0000-0002-2318-1460]
dc.contributor.orcidOng, Cheng Soon [0000-0002-2302-9733]
dc.contributor.orcidPapenfuss, Tony [0000-0002-1102-8506]
dc.contributor.orcidHovens, Christopher M [0000-0002-0610-1289]
dc.contributor.orcidMarkowetz, Florian [0000-0002-2784-5308]
dc.contributor.orcidMacintyre, Geoffrey [0000-0003-3906-467X]
dc.identifier.eissn2041-1723
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idCancer Research UK (15973)
pubs.funder-project-idCancer Research UK (C14303_do not transfer)
pubs.funder-project-idCancer Research UK (CRUK-A15973)
cam.orpheus.counter2*
rioxxterms.freetoread.startdate2022-12-18


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