Inferring structural variant cancer cell fraction

Marek Cmero; Ke Yuan; Cheng Soon Ong; Jan Schröder; David J. Adams; Pavana Anur; Rameen Beroukhim; Paul C. Boutros; David D.L. Bowtell; Peter J. Campbell; Shaolong Cao; Elizabeth L. Christie; Yupeng Cun; Kevin J. Dawson; Jonas Demeulemeester; Stefan C. Dentro; Amit G. Deshwar; Nilgun Donmez; Ruben M. Drews; Roland Eils; Yu Fan; Matthew W. Fittall; Dale W. Garsed; Moritz Gerstung; Gad Getz; Santiago Gonzalez; Gavin Ha; Kerstin Haase; Marcin Imielinski; Lara Jerman; Yuan Ji; Clemency Jolly; Kortine Kleinheinz; Juhee Lee; Henry Lee-Six; Ignaty Leshchiner; Dimitri Livitz; Salem Malikic; Iñigo Martincorena; Thomas J. Mitchell; Quaid D. Morris; Ville Mustonen; Layla Oesper; Martin Peifer; Myron Peto; Benjamin J. Raphael; Daniel Rosebrock; Yulia Rubanova; S. Cenk Sahinalp; Adriana Salcedo; PCAWG Consortium; PCAWG Evolution and Heterogeneity Working Group; David Townend

doi:10.1038/s41467-020-14351-8

Inferring structural variant cancer cell fraction

Marek Cmero^*
, Ke Yuan
, Cheng Soon Ong
, Jan Schröder
, David J. Adams
, Pavana Anur
, Rameen Beroukhim
, Paul C. Boutros
, David D.L. Bowtell
, Peter J. Campbell
, Shaolong Cao
, Elizabeth L. Christie
, Yupeng Cun
, Kevin J. Dawson
, Jonas Demeulemeester
, Stefan C. Dentro
, Amit G. Deshwar
, Nilgun Donmez
, Ruben M. Drews
, Roland Eils

Yu Fan, Matthew W. Fittall, Dale W. Garsed, Moritz Gerstung, Gad Getz, Santiago Gonzalez, Gavin Ha, Kerstin Haase, Marcin Imielinski, Lara Jerman, Yuan Ji, Clemency Jolly, Kortine Kleinheinz, Juhee Lee, Henry Lee-Six, Ignaty Leshchiner, Dimitri Livitz, Salem Malikic, Iñigo Martincorena, Thomas J. Mitchell, Quaid D. Morris, Ville Mustonen, Layla Oesper, Martin Peifer, Myron Peto, Benjamin J. Raphael, Daniel Rosebrock, Yulia Rubanova, S. Cenk Sahinalp, Adriana Salcedo, PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group, David Townend

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	730
Number of pages	15
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-14351-8
Publication status	Published - 1 Dec 2020

Access to Document

10.1038/s41467-020-14351-8Licence: CC BY

1 Erratum / corrigendum

Author Correction: Inferring structural variant cancer cell fraction
Cmero, M., Yuan, K., Ong, C. S., Schröder, J., Adams, D. J., Anur, P., Beroukhim, R., Boutros, P. C., Bowtell, D. D. L., Campbell, P. J., Cao, S., Christie, E. L., Cun, Y., Dawson, K. J., Demeulemeester, J., Dentro, S. C., Deshwar, A. G., Donmez, N., Drews, R. M. & Eils, R. & 33 others, Fan, Y., Fittall, M. W., Garsed, D. W., Gerstung, M., Getz, G., Gonzalez, S., Ha, G., Haase, K., Imielinski, M., Jerman, L., Ji, Y., Jolly, C., Kleinheinz, K., Lee, J., Lee-Six, H., Leshchiner, I., Livitz, D., Malikic, S., Martincorena, I., Mitchell, T. J., Morris, Q. D., Mustonen, V., Oesper, L., Peifer, M., Peto, M., Raphael, B. J., Rosebrock, D., Rubanova, Y., Sahinalp, S. C., Salcedo, A., PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group & Townend, D., 8 Dec 2022, In: Nature Communications. 13, 1, 1 p., 7568.
Research output: Contribution to journal › Erratum / corrigendum › Academic

Open Access

Cite this

Cmero, M., Yuan, K., Ong, C. S., Schröder, J., Adams, D. J., Anur, P., Beroukhim, R., Boutros, P. C., Bowtell, D. D. L., Campbell, P. J., Cao, S., Christie, E. L., Cun, Y., Dawson, K. J., Demeulemeester, J., Dentro, S. C., Deshwar, A. G., Donmez, N., Drews, R. M., ... Townend, D. (2020). Inferring structural variant cancer cell fraction. Nature Communications, 11(1), Article 730. https://doi.org/10.1038/s41467-020-14351-8

@article{6a9a9cdf24b241b2800bf5a43c2363e4,

title = "Inferring structural variant cancer cell fraction",

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{\textquoteright}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.",

author = "Marek Cmero and Ke Yuan and Ong, \{Cheng Soon\} and Jan Schr{\"o}der and Adams, \{David J.\} and Pavana Anur and Rameen Beroukhim and Boutros, \{Paul C.\} and Bowtell, \{David D.L.\} and Campbell, \{Peter J.\} and Shaolong Cao and Christie, \{Elizabeth L.\} and Yupeng Cun and Dawson, \{Kevin J.\} and Jonas Demeulemeester and Dentro, \{Stefan C.\} and Deshwar, \{Amit G.\} and Nilgun Donmez and Drews, \{Ruben M.\} and Roland Eils and Yu Fan and Fittall, \{Matthew W.\} and Garsed, \{Dale W.\} and Moritz Gerstung and Gad Getz and Santiago Gonzalez and Gavin Ha and Kerstin Haase and Marcin Imielinski and Lara Jerman and Yuan Ji and Clemency Jolly and Kortine Kleinheinz and Juhee Lee and Henry Lee-Six and Ignaty Leshchiner and Dimitri Livitz and Salem Malikic and I{\~n}igo Martincorena and Mitchell, \{Thomas J.\} and Morris, \{Quaid D.\} and Ville Mustonen and Layla Oesper and Martin Peifer and Myron Peto and Raphael, \{Benjamin J.\} and Daniel Rosebrock and Yulia Rubanova and Sahinalp, \{S. Cenk\} and Adriana Salcedo and \{PCAWG Consortium\} and \{PCAWG Evolution and Heterogeneity Working Group\} and David Townend",

note = "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 {\textendash} Distinguished Gentleman{\textquoteright}s Ride Clinician Scientist Award through the Prostate Cancer Foundation of Australia{\textquoteright}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: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-020-14351-8",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Research",

number = "1",

}

Cmero, M, Yuan, K, Ong, CS, Schröder, J, Adams, DJ, Anur, P, Beroukhim, R, Boutros, PC, Bowtell, DDL, Campbell, PJ, Cao, S, Christie, EL, Cun, Y, Dawson, KJ, Demeulemeester, J, Dentro, SC, Deshwar, AG, Donmez, N, Drews, RM, Eils, R, Fan, Y, Fittall, MW, Garsed, DW, Gerstung, M, Getz, G, Gonzalez, S, Ha, G, Haase, K, Imielinski, M, Jerman, L, Ji, Y, Jolly, C, Kleinheinz, K, Lee, J, Lee-Six, H, Leshchiner, I, Livitz, D, Malikic, S, Martincorena, I, Mitchell, TJ, Morris, QD, Mustonen, V, Oesper, L, Peifer, M, Peto, M, Raphael, BJ, Rosebrock, D, Rubanova, Y, Sahinalp, SC, Salcedo, A, PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group & Townend, D 2020, 'Inferring structural variant cancer cell fraction', Nature Communications, vol. 11, no. 1, 730. https://doi.org/10.1038/s41467-020-14351-8

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 - Salcedo, Adriana

AU - PCAWG Consortium

AU - PCAWG Evolution and Heterogeneity Working Group

AU - Townend, David

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.

U2 - 10.1038/s41467-020-14351-8

DO - 10.1038/s41467-020-14351-8

M3 - Article

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 730

ER -

Inferring structural variant cancer cell fraction

Abstract

Access to Document

Fingerprint

Research output

Author Correction: Inferring structural variant cancer cell fraction

Cite this