Inferring structural variant cancer cell fraction

PCAWG Evolution and Heterogeneity Working Group, PCAWG Consortium, 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 DonmezRuben 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, Matthias Schlesner, Steven E. Schumacher, Subhajit Sengupta, Ruian Shi, Seung Jun Shin, Paul T. Spellman, Oliver Spiro, Lincoln D. Stein, Maxime Tarabichi, Peter Van Loo, Shankar Vembu, Ignacio Vázquez-García, Wenyi Wang, David C. Wedge, David A. Wheeler, Jeffrey A. Wintersinger, Tsun Po Yang, Xiaotong Yao, Kaixian Yu, Hongtu Zhu, Niall M. Corcoran, Tony Papenfuss, Christopher M. Hovens, Florian Markowetz, Geoff Macintyre, Lauri A. Aaltonen, Federico Abascal, Adam Abeshouse, Hiroyuki Aburatani, David J. Adams, Nishant Agrawal, Keun Soo Ahn, Sung Min Ahn, Hiroshi Aikata, Rehan Akbani, Kadir C. Akdemir, Hikmat Al-Ahmadie, Sultan T. Al-Sedairy, Fatima Al-Shahrour, Malik Alawi, Monique Albert, Kenneth Aldape, Ludmil B. Alexandrov, Adrian Ally, Kathryn Alsop, Eva G. Alvarez, Fernanda Amary, Samirkumar B. Amin, Brice Aminou, Ole Ammerpohl, Matthew J. Anderson, Yeng Ang, Davide Antonello, Pavana Anur, Samuel Aparicio, Elizabeth L. Appelbaum, Yasuhito Arai, Axel Aretz, Koji Arihiro, Shun ichi Ariizumi, Joshua Armenia, Laurent Arnould, Sylvia Asa, Yassen Assenov, Gurnit Atwal, Sietse Aukema, J. Todd Auman, Miriam R.R. Aure, Philip Awadalla, Marta Aymerich, Gary D. Bader, Adrian Baez-Ortega, Matthew H. Bailey, Peter J. Bailey, Miruna Balasundaram, Saianand Balu, Pratiti Bandopadhayay, Rosamonde E. Banks, Stefano Barbi, Andrew P. Barbour, Jonathan Barenboim, Jill Barnholtz-Sloan, Hugh Barr, Elisabet Barrera, John Bartlett, Javier Bartolome, Claudio Bassi, Oliver F. Bathe, Daniel Baumhoer, Prashant Bavi, Stephen B. Baylin, Wojciech Bazant, Duncan Beardsmore, Timothy A. Beck, Sam Behjati, Andreas Behren, Beifang Niu, Cindy Bell, Sergi Beltran, Christopher Benz, Andrew Berchuck, Anke K. Bergmann, Erik N. Bergstrom, Benjamin P. Berman, Daniel M. Berney, Stephan H. Bernhart, Mario Berrios, Samantha Bersani, Johanna Bertl, Miguel Betancourt, Vinayak Bhandari, Shriram G. Bhosle, Andrew V. Biankin, Matthias Bieg, Darell Bigner, Hans Binder, Ewan Birney, Michael Birrer, Nidhan K. Biswas, Bodil Bjerkehagen, Tom Bodenheimer, Lori Boice, Giada Bonizzato, Johann S. De Bono, Arnoud Boot, Moiz S. Bootwalla, Ake Borg, Arndt Borkhardt, Keith A. Boroevich, Ivan Borozan, Christoph Borst, Marcus Bosenberg, Mattia Bosio, Jacqueline Boultwood, Guillaume Bourque, Paul C. Boutros, G. Steven Bova, David T. Bowen, Reanne Bowlby, Sandrine Boyault, Rich Boyce, Li Ding, Lucinda A. Fulton, Robert S. Fulton, Ramaswamy Govindan, Reyka Jayasinghe, Tim Ley, Christopher A. Miller, David Mutch, Michael C. Wendl

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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 languageEnglish
Article number730
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020

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