Inferring structural variant cancer cell fraction

M. Cmero; K. Yuan; C.S. Ong; J. Schröder; D.J. Adams; P. Anur; R. Beroukhim; P.C. Boutros; D.D.L. Bowtell; P.J. Campbell; S. Cao; E.L. Christie; Y. Cun; K.J. Dawson; J. Demeulemeester; S.C. Dentro; A.G. Deshwar; N. Donmez; R.M. Drews; R. Eils; Y. Fan; M.W. Fittall; D.W. Garsed; M. Gerstung; G. Getz; S. Gonzalez; G. Ha; K. Haase; M. Imielinski; L. Jerman; Y. Ji; C. Jolly; K. Kleinheinz; J. Lee; H. Lee-Six; I. Leshchiner; D. Livitz; S. Malikic; I. Martincorena; T.J. Mitchell; Q.D. Morris; V. Mustonen; L. Oesper; M. Peifer; M. Peto; B.J. Raphael; D. Rosebrock; Y. Rubanova; S.C. Sahinalp; A. Salcedo; M. Schlesner; S.E. Schumacher; Subhajit Sengupta; R. Shi; S.J. Shin; P.T. Spellman; O. Spiro; L.D. Stein; M. Tarabichi; P. Van Loo; S. Vembu; I. Vázquez-García; W. Wang; D.C. Wedge; D.A. Wheeler; J.A. Wintersinger; T.-P. Yang; X. Yao; K. Yu; H. Zhu; N.M. Corcoran; T. Papenfuss; C.M. Hovens; F. Markowetz; G. Macintyre

doi:10.1038/s41467-020-14351-8

Inferring structural variant cancer cell fraction

M. Cmero, K. Yuan, C.S. Ong, J. Schröder, D.J. Adams, P. Anur, R. Beroukhim, P.C. Boutros, D.D.L. Bowtell, P.J. Campbell, S. Cao, E.L. Christie, Y. Cun, K.J. Dawson, J. Demeulemeester, S.C. Dentro, A.G. Deshwar, N. Donmez, R.M. Drews, R. EilsY. Fan, M.W. Fittall, D.W. Garsed, M. Gerstung, G. Getz, S. Gonzalez, G. Ha, K. Haase, M. Imielinski, L. Jerman, Y. Ji, C. Jolly, K. Kleinheinz, J. Lee, H. Lee-Six, I. Leshchiner, D. Livitz, S. Malikic, I. Martincorena, T.J. Mitchell, Q.D. Morris, V. Mustonen, L. Oesper, M. Peifer, M. Peto, B.J. Raphael, D. Rosebrock, Y. Rubanova, S.C. Sahinalp, A. Salcedo, M. Schlesner, S.E. Schumacher, Subhajit Sengupta, R. Shi, S.J. Shin, P.T. Spellman, O. Spiro, L.D. Stein, M. Tarabichi, P. Van Loo, S. Vembu, I. Vázquez-García, W. Wang, D.C. Wedge, D.A. Wheeler, J.A. Wintersinger, T.-P. Yang, X. Yao, K. Yu, H. Zhu, N.M. Corcoran, T. Papenfuss, C.M. Hovens, F. Markowetz, G. Macintyre

Division of Cancer Sciences (L5)

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

112 Downloads (Pure)

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
Journal	Nature Communications
Volume	11
Issue number	1
Early online date	5 Feb 2020
DOIs	https://doi.org/10.1038/s41467-020-14351-8
Publication status	Published - 1 Dec 2020

Keywords

Algorithms
Computational Biology/methods
Computer Simulation
DNA Copy Number Variations
Female
Gene Frequency
Genome, Human
Humans
Liver Neoplasms/genetics
Male
Neoplasms/genetics
Ovarian Neoplasms/genetics
Pancreatic Neoplasms/genetics
Prostatic Neoplasms/genetics
Sensitivity and Specificity
Whole Genome Sequencing

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

s41467-020-14351-8Final published version, 1.88 MBLicence: CC BY

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., ... Macintyre, G. (2020). Inferring structural variant cancer cell fraction. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-14351-8

@article{577ec2b13d1643e2a71bbccde4509f9a,

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.",

keywords = "Algorithms, Computational Biology/methods, Computer Simulation, DNA Copy Number Variations, Female, Gene Frequency, Genome, Human, Humans, Liver Neoplasms/genetics, Male, Neoplasms/genetics, Ovarian Neoplasms/genetics, Pancreatic Neoplasms/genetics, Prostatic Neoplasms/genetics, Sensitivity and Specificity, Whole Genome Sequencing",

author = "M. Cmero and K. Yuan and C.S. Ong and J. Schr{\"o}der and D.J. Adams and P. Anur and R. Beroukhim and P.C. Boutros and D.D.L. Bowtell and P.J. Campbell and S. Cao and E.L. Christie and Y. Cun and K.J. Dawson and J. Demeulemeester and S.C. Dentro and A.G. Deshwar and N. Donmez and R.M. Drews and R. Eils and Y. Fan and M.W. Fittall and D.W. Garsed and M. Gerstung and G. Getz and S. Gonzalez and G. Ha and K. Haase and M. Imielinski and L. Jerman and Y. Ji and C. Jolly and K. Kleinheinz and J. Lee and H. Lee-Six and I. Leshchiner and D. Livitz and S. Malikic and I. Martincorena and T.J. Mitchell and Q.D. Morris and V. Mustonen and L. Oesper and M. Peifer and M. Peto and B.J. Raphael and D. Rosebrock and Y. Rubanova and S.C. Sahinalp and A. Salcedo and M. Schlesner and S.E. Schumacher and Subhajit Sengupta and R. Shi and S.J. Shin and P.T. Spellman and O. Spiro and L.D. Stein and M. Tarabichi and {Van Loo}, P. and S. Vembu and I. V{\'a}zquez-Garc{\'i}a and W. Wang and D.C. Wedge and D.A. Wheeler and J.A. Wintersinger and T.-P. Yang and X. Yao and K. Yu and H. Zhu and N.M. Corcoran and T. Papenfuss and C.M. Hovens and F. Markowetz and G. Macintyre",

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, Schlesner, M, Schumacher, SE, Sengupta, S, Shi, R, Shin, SJ, Spellman, PT, Spiro, O, Stein, LD, Tarabichi, M, Van Loo, P, Vembu, S, Vázquez-García, I, Wang, W, Wedge, DC, Wheeler, DA, Wintersinger, JA, Yang, T-P, Yao, X, Yu, K, Zhu, H, Corcoran, NM, Papenfuss, T, Hovens, CM, Markowetz, F & Macintyre, G 2020, 'Inferring structural variant cancer cell fraction', Nature Communications, vol. 11, no. 1. https://doi.org/10.1038/s41467-020-14351-8

TY - JOUR

T1 - Inferring structural variant cancer cell fraction

AU - Cmero, M.

AU - Yuan, K.

AU - Ong, C.S.

AU - Schröder, J.

AU - Adams, D.J.

AU - Anur, P.

AU - Beroukhim, R.

AU - Boutros, P.C.

AU - Bowtell, D.D.L.

AU - Campbell, P.J.

AU - Cao, S.

AU - Christie, E.L.

AU - Cun, Y.

AU - Dawson, K.J.

AU - Demeulemeester, J.

AU - Dentro, S.C.

AU - Deshwar, A.G.

AU - Donmez, N.

AU - Drews, R.M.

AU - Eils, R.

AU - Fan, Y.

AU - Fittall, M.W.

AU - Garsed, D.W.

AU - Gerstung, M.

AU - Getz, G.

AU - Gonzalez, S.

AU - Ha, G.

AU - Haase, K.

AU - Imielinski, M.

AU - Jerman, L.

AU - Ji, Y.

AU - Jolly, C.

AU - Kleinheinz, K.

AU - Lee, J.

AU - Lee-Six, H.

AU - Leshchiner, I.

AU - Livitz, D.

AU - Malikic, S.

AU - Martincorena, I.

AU - Mitchell, T.J.

AU - Morris, Q.D.

AU - Mustonen, V.

AU - Oesper, L.

AU - Peifer, M.

AU - Peto, M.

AU - Raphael, B.J.

AU - Rosebrock, D.

AU - Rubanova, Y.

AU - Sahinalp, S.C.

AU - Salcedo, A.

AU - Schlesner, M.

AU - Schumacher, S.E.

AU - Sengupta, Subhajit

AU - Shi, R.

AU - Shin, S.J.

AU - Spellman, P.T.

AU - Spiro, O.

AU - Stein, L.D.

AU - Tarabichi, M.

AU - Van Loo, P.

AU - Vembu, S.

AU - Vázquez-García, I.

AU - Wang, W.

AU - Wedge, D.C.

AU - Wheeler, D.A.

AU - Wintersinger, J.A.

AU - Yang, T.-P.

AU - Yao, X.

AU - Yu, K.

AU - Zhu, H.

AU - Corcoran, N.M.

AU - Papenfuss, T.

AU - Hovens, C.M.

AU - Markowetz, F.

AU - Macintyre, G.

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.

KW - Algorithms

KW - Computational Biology/methods

KW - Computer Simulation

KW - DNA Copy Number Variations

KW - Female

KW - Gene Frequency

KW - Genome, Human

KW - Humans

KW - Liver Neoplasms/genetics

KW - Male

KW - Neoplasms/genetics

KW - Ovarian Neoplasms/genetics

KW - Pancreatic Neoplasms/genetics

KW - Prostatic Neoplasms/genetics

KW - Sensitivity and Specificity

KW - Whole Genome Sequencing

U2 - 10.1038/s41467-020-14351-8

DO - 10.1038/s41467-020-14351-8

M3 - Article

C2 - 32024845

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

Inferring structural variant cancer cell fraction

Abstract

Keywords

Research Beacons, Institutes and Platforms

UN SDGs

Access to Document

Fingerprint

Cite this