Inferring structural variant cancer cell fraction

dc.contributor.authorCmero, Marek
dc.contributor.authorYuan, Ke
dc.contributor.authorOng, Cheng Soon
dc.contributor.authorSchroder, Jan
dc.contributor.authorCorcoran, Niall M.
dc.contributor.authorPapenfuss, Anthony T
dc.contributor.authorHovens, Christopher M.
dc.contributor.authorMarkowetz, Florian
dc.contributor.authorMacintyre, I G
dc.date.accessioned2023-09-04T23:46:54Z
dc.date.available2023-09-04T23:46:54Z
dc.date.issued2020
dc.date.updated2022-07-24T08:22:05Z
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 2658 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.en_AU
dc.description.sponsorshipF.M., G.M. and K.Y. would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. G.M., K.Y. and F.M. were funded by CRUK grants C14303/A17197 and A19274. G.M. was funded by CRUK grant A15973.to N.M.C as well as a VCA early career seed grant 14010 to NMC. NMC was supported by a David Bickart Clinician Researcher Fellowship from the Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, and more recently by a Movember – Distinguished Gentleman’s Ride Clinician Scientist Award through the Prostate Cancer Foundation of Australia’s Research Program. M.C. would like to acknowledge the support of the Cybec Foundation and the Endeavour Research Fellowship.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2041-1723en_AU
dc.identifier.urihttp://hdl.handle.net/1885/298209
dc.language.isoen_AUen_AU
dc.provenanceThis 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/.en_AU
dc.publisherMacmillan Publishers Ltden_AU
dc.relationhttp://purl.org/au-research/grants/nhmrc/1047581en_AU
dc.relationhttp://purl.org/au-research/grants/nhmrc/1104010en_AU
dc.relationhttp://purl.org/au-research/grants/nhmrc/1024081en_AU
dc.rights© 2020 The authorsen_AU
dc.rights.licenseCreative Commons Attribution licenceen_AU
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_AU
dc.sourceNature Communicationsen_AU
dc.titleInferring structural variant cancer cell fractionen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue730en_AU
local.contributor.affiliationCmero, Marek, Royal Melbourne Hospitalen_AU
local.contributor.affiliationYuan, Ke, University of Glasgowen_AU
local.contributor.affiliationOng, Cheng Soon, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationSchroder, Jan, The Walter and Eliza Hall Institute of Medical Researchen_AU
local.contributor.affiliationCorcoran, Niall M., Royal Melbourne Hospitalen_AU
local.contributor.affiliationPapenfuss, Anthony T, Walter and Eliza Hall Institute of Medical Researchen_AU
local.contributor.affiliationHovens, Christopher M., Royal Melbourne Hospitalen_AU
local.contributor.affiliationMarkowetz, Florian, University of Cambridgeen_AU
local.contributor.affiliationMacintyre, I G, Smithsonian Institutionen_AU
local.contributor.authoremailu4028825@anu.edu.auen_AU
local.contributor.authoruidOng, Cheng Soon, u4028825en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor321101 - Cancer cell biologyen_AU
local.identifier.absfor461199 - Machine learning not elsewhere classifieden_AU
local.identifier.ariespublicationu6269649xPUB428en_AU
local.identifier.citationvolume11en_AU
local.identifier.doi10.1038/s41467-020-14351-8en_AU
local.identifier.scopusID2-s2.0-85079039901
local.identifier.thomsonIDWOS:000513499700012
local.identifier.uidSubmittedByu6269649en_AU
local.publisher.urlhttps://www.nature.com/en_AU
local.type.statusPublished Versionen_AU

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