Inferring structural variant cancer cell fraction
Marek Cmero, Ke Yuan, Cheng Soon Ong, Jan Schröder, PCAWG Evolution and Heterogeneity Working Group, 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 Malikić, Iñigo Martincorena, Thomas J. Mitchell, Quaid 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 Stein, Maxime Tarabichi, Peter Van Loo, Shankar Vembu, Ignacio Vázquez-Garćıa, Wenyi Wang, David C. Wedge, David A. Wheeler, Jeffrey A. Wintersinger, Tsun-Po Yang, Xiaotong Yao, Kaixian Yu, Hongtu Zhu, Niall M. Corcoran, Anthony T. 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, Fátima Al‐Shahrour, Malik Alawi, Monique Albert, Kenneth Aldape, Ludmil B. Alexandrov, Adrian Ally, Kathryn Alsop, Eva G. Álvarez, Fernanda Amary, Samirkumar B. Amin, Brice Aminou
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.