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.
Description
Inferring structural variant cancer cell fraction | Nature Communications
%0 Journal Article
%1 cmero2020inferring
%A Cmero, Marek
%A Yuan, Ke
%A Ong, Cheng Soon
%A Schröder, Jan
%A Adams, David J.
%A Anur, Pavana
%A Beroukhim, Rameen
%A Boutros, Paul C.
%A Bowtell, David D. L.
%A Campbell, Peter J.
%A Cao, Shaolong
%A Christie, Elizabeth L.
%A Cun, Yupeng
%A Dawson, Kevin J.
%A Demeulemeester, Jonas
%A Dentro, Stefan C.
%A Deshwar, Amit G.
%A Donmez, Nilgun
%A Drews, Ruben M.
%A Eils, Roland
%A Fan, Yu
%A Fittall, Matthew W.
%A Garsed, Dale W.
%A Gerstung, Moritz
%A Getz, Gad
%A Gonzalez, Santiago
%A Ha, Gavin
%A Haase, Kerstin
%A Imielinski, Marcin
%A Jerman, Lara
%A Ji, Yuan
%A Jolly, Clemency
%A Kleinheinz, Kortine
%A Lee, Juhee
%A Lee-Six, Henry
%A Leshchiner, Ignaty
%A Livitz, Dimitri
%A Malikic, Salem
%A Martincorena, Iñigo
%A Mitchell, Thomas J.
%A Morris, Quaid D.
%A Mustonen, Ville
%A Oesper, Layla
%A Peifer, Martin
%A Peto, Myron
%A Raphael, Benjamin J.
%A Rosebrock, Daniel
%A Rubanova, Yulia
%A Sahinalp, S. Cenk
%A Salcedo, Adriana
%A Schlesner, Matthias
%A Schumacher, Steven E.
%A Sengupta, Subhajit
%A Shi, Ruian
%A Shin, Seung Jun
%A Spellman, Paul T.
%A Spiro, Oliver
%A Stein, Lincoln D.
%A Tarabichi, Maxime
%A Van Loo, Peter
%A Vembu, Shankar
%A Vázquez-García, Ignacio
%A Wang, Wenyi
%A Wedge, David C.
%A Wheeler, David A.
%A Wintersinger, Jeffrey A.
%A Yang, Tsun-Po
%A Yao, Xiaotong
%A Yu, Kaixian
%A Zhu, Hongtu
%A Corcoran, Niall M.
%A Papenfuss, Tony
%A Hovens, Christopher M.
%A Markowetz, Florian
%A Macintyre, Geoff
%A Evolution, PCAWG
%A Group, Heterogeneity Working
%A Consortium, PCAWG
%D 2020
%J Nature Communications
%K cancer-research software
%N 1
%P 730--
%R 10.1038/s41467-020-14351-8
%T Inferring structural variant cancer cell fraction
%U https://doi.org/10.1038/s41467-020-14351-8
%V 11
%X 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.
@article{cmero2020inferring,
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.},
added-at = {2020-12-21T19:12:04.000+0100},
author = {Cmero, Marek and Yuan, Ke and Ong, Cheng Soon and Schröder, Jan and Adams, David J. and Anur, Pavana and Beroukhim, Rameen and Boutros, Paul C. and Bowtell, David D. L. and Campbell, Peter J. and Cao, Shaolong and Christie, Elizabeth L. and Cun, Yupeng and Dawson, Kevin J. and Demeulemeester, Jonas and Dentro, Stefan C. and Deshwar, Amit G. and Donmez, Nilgun and Drews, Ruben M. and Eils, Roland and Fan, Yu and Fittall, Matthew W. and Garsed, Dale W. and Gerstung, Moritz and Getz, Gad and Gonzalez, Santiago and Ha, Gavin and Haase, Kerstin and Imielinski, Marcin and Jerman, Lara and Ji, Yuan and Jolly, Clemency and Kleinheinz, Kortine and Lee, Juhee and Lee-Six, Henry and Leshchiner, Ignaty and Livitz, Dimitri and Malikic, Salem and Martincorena, Iñigo and Mitchell, Thomas J. and Morris, Quaid D. and Mustonen, Ville and Oesper, Layla and Peifer, Martin and Peto, Myron and Raphael, Benjamin J. and Rosebrock, Daniel and Rubanova, Yulia and Sahinalp, S. Cenk and Salcedo, Adriana and Schlesner, Matthias and Schumacher, Steven E. and Sengupta, Subhajit and Shi, Ruian and Shin, Seung Jun and Spellman, Paul T. and Spiro, Oliver and Stein, Lincoln D. and Tarabichi, Maxime and Van Loo, Peter and Vembu, Shankar and Vázquez-García, Ignacio and Wang, Wenyi and Wedge, David C. and Wheeler, David A. and Wintersinger, Jeffrey A. and Yang, Tsun-Po and Yao, Xiaotong and Yu, Kaixian and Zhu, Hongtu and Corcoran, Niall M. and Papenfuss, Tony and Hovens, Christopher M. and Markowetz, Florian and Macintyre, Geoff and Evolution, PCAWG and Group, Heterogeneity Working and Consortium, PCAWG},
biburl = {https://www.bibsonomy.org/bibtex/26702682c199b6ecc0583b6f2a7a9afcb/marcsaric},
description = {Inferring structural variant cancer cell fraction | Nature Communications},
doi = {10.1038/s41467-020-14351-8},
interhash = {cc1dc1d061c591b0ca6c0ec67de92c56},
intrahash = {6702682c199b6ecc0583b6f2a7a9afcb},
issn = {20411723},
journal = {Nature Communications},
keywords = {cancer-research software},
number = 1,
pages = {730--},
refid = {Cmero2020},
timestamp = {2020-12-21T19:12:04.000+0100},
title = {Inferring structural variant cancer cell fraction},
url = {https://doi.org/10.1038/s41467-020-14351-8},
volume = 11,
year = 2020
}