TY - JOUR
T1 - Inferring structural variant cancer cell fraction
AU - Cmero, Marek
AU - Yuan, Ke
AU - Ong, Cheng Soon
AU - Schröder, Jan
AU - Adams, David J.
AU - Anur, Pavana
AU - Beroukhim, Rameen
AU - Boutros, Paul C.
AU - Bowtell, David D.L.
AU - Campbell, Peter J.
AU - Cao, Shaolong
AU - Christie, Elizabeth L.
AU - Cun, Yupeng
AU - Dawson, Kevin J.
AU - Demeulemeester, Jonas
AU - Dentro, Stefan C.
AU - Deshwar, Amit G.
AU - Donmez, Nilgun
AU - Drews, Ruben M.
AU - Eils, Roland
AU - Fan, Yu
AU - Fittall, Matthew W.
AU - Garsed, Dale W.
AU - Gerstung, Moritz
AU - Getz, Gad
AU - Gonzalez, Santiago
AU - Ha, Gavin
AU - Haase, Kerstin
AU - Imielinski, Marcin
AU - Jerman, Lara
AU - Ji, Yuan
AU - Jolly, Clemency
AU - Kleinheinz, Kortine
AU - Lee, Juhee
AU - Lee-Six, Henry
AU - Leshchiner, Ignaty
AU - Livitz, Dimitri
AU - Malikic, Salem
AU - Martincorena, Iñigo
AU - Mitchell, Thomas J.
AU - Morris, Quaid D.
AU - Mustonen, Ville
AU - Oesper, Layla
AU - Peifer, Martin
AU - Peto, Myron
AU - Raphael, Benjamin J.
AU - Rosebrock, Daniel
AU - Rubanova, Yulia
AU - Sahinalp, S. Cenk
AU - Pritchard, Antonia L.
N1 - Funding Information:
We would like to thank Kangbo Mo for his assistance in developing the SV classification scheme during his Master studies, and Christoffer Flensburg for his helpful advice for the four-and five-cluster mixtures. 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. This work was supported, in part, by NHMRC grants 1047581 and 1104010 to C.M.H. and 1024081 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. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-020-14351-8
DO - 10.1038/s41467-020-14351-8
M3 - Article
C2 - 32024845
AN - SCOPUS:85079039901
SN - 2041-1723
VL - 11
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 730
ER -