Inferring structural variant cancer cell fraction
dc.contributor.author | Cmero, Marek | |
dc.contributor.author | Yuan, Ke | |
dc.contributor.author | Ong, Cheng Soon | |
dc.contributor.author | Schroder, Jan | |
dc.contributor.author | Corcoran, Niall M. | |
dc.contributor.author | Papenfuss, Anthony T | |
dc.contributor.author | Hovens, Christopher M. | |
dc.contributor.author | Markowetz, Florian | |
dc.contributor.author | Macintyre, I G | |
dc.date.accessioned | 2023-09-04T23:46:54Z | |
dc.date.available | 2023-09-04T23:46:54Z | |
dc.date.issued | 2020 | |
dc.date.updated | 2022-07-24T08:22:05Z | |
dc.description.abstract | We 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.sponsorship | F.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.mimetype | application/pdf | en_AU |
dc.identifier.issn | 2041-1723 | en_AU |
dc.identifier.uri | http://hdl.handle.net/1885/298209 | |
dc.language.iso | en_AU | en_AU |
dc.provenance | 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/. | en_AU |
dc.publisher | Macmillan Publishers Ltd | en_AU |
dc.relation | http://purl.org/au-research/grants/nhmrc/1047581 | en_AU |
dc.relation | http://purl.org/au-research/grants/nhmrc/1104010 | en_AU |
dc.relation | http://purl.org/au-research/grants/nhmrc/1024081 | en_AU |
dc.rights | © 2020 The authors | en_AU |
dc.rights.license | Creative Commons Attribution licence | en_AU |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_AU |
dc.source | Nature Communications | en_AU |
dc.title | Inferring structural variant cancer cell fraction | en_AU |
dc.type | Journal article | en_AU |
dcterms.accessRights | Open Access | en_AU |
local.bibliographicCitation.issue | 730 | en_AU |
local.contributor.affiliation | Cmero, Marek, Royal Melbourne Hospital | en_AU |
local.contributor.affiliation | Yuan, Ke, University of Glasgow | en_AU |
local.contributor.affiliation | Ong, Cheng Soon, College of Engineering and Computer Science, ANU | en_AU |
local.contributor.affiliation | Schroder, Jan, The Walter and Eliza Hall Institute of Medical Research | en_AU |
local.contributor.affiliation | Corcoran, Niall M., Royal Melbourne Hospital | en_AU |
local.contributor.affiliation | Papenfuss, Anthony T, Walter and Eliza Hall Institute of Medical Research | en_AU |
local.contributor.affiliation | Hovens, Christopher M., Royal Melbourne Hospital | en_AU |
local.contributor.affiliation | Markowetz, Florian, University of Cambridge | en_AU |
local.contributor.affiliation | Macintyre, I G, Smithsonian Institution | en_AU |
local.contributor.authoremail | u4028825@anu.edu.au | en_AU |
local.contributor.authoruid | Ong, Cheng Soon, u4028825 | en_AU |
local.description.notes | Imported from ARIES | en_AU |
local.identifier.absfor | 321101 - Cancer cell biology | en_AU |
local.identifier.absfor | 461199 - Machine learning not elsewhere classified | en_AU |
local.identifier.ariespublication | u6269649xPUB428 | en_AU |
local.identifier.citationvolume | 11 | en_AU |
local.identifier.doi | 10.1038/s41467-020-14351-8 | en_AU |
local.identifier.scopusID | 2-s2.0-85079039901 | |
local.identifier.thomsonID | WOS:000513499700012 | |
local.identifier.uidSubmittedBy | u6269649 | en_AU |
local.publisher.url | https://www.nature.com/ | en_AU |
local.type.status | Published Version | en_AU |
Downloads
Original bundle
1 - 1 of 1
Loading...
- Name:
- s41467-020-14351-8.pdf
- Size:
- 1.86 MB
- Format:
- Adobe Portable Document Format
- Description: