Identification of complex genomic rearrangements in cancers using CouGaR

  1. Michael Brudno1,2
  1. 1Department of Computer Science, University of Toronto, Toronto, Ontario, M5S 3G4, Canada;
  2. 2Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada;
  3. 3Division of Pathology, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, M5G 1E8, Canada;
  4. 4Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada;
  5. 5Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, M5G 1E8, Canada
  1. Corresponding author: brudno{at}cs.toronto.edu
  1. 6 These authors contributed equally to this work.

Abstract

The genomic alterations associated with cancers are numerous and varied, involving both isolated and large-scale complex genomic rearrangements (CGRs). Although the underlying mechanisms are not well understood, CGRs have been implicated in tumorigenesis. Here, we introduce CouGaR, a novel method for characterizing the genomic structure of amplified CGRs, leveraging both depth of coverage (DOC) and discordant pair-end mapping techniques. We applied our method to whole-genome sequencing (WGS) samples from The Cancer Genome Atlas and identify amplified CGRs in at least 5.2% (10+ copies) to 17.8% (6+ copies) of the samples. Furthermore, ∼95% of these amplified CGRs contain genes previously implicated in tumorigenesis, indicating the importance and widespread occurrence of CGRs in cancers. Additionally, CouGaR identified the occurrence of ‘chromoplexy’ in nearly 63% of all prostate cancer samples and 30% of all bladder cancer samples. To further validate the accuracy of our method, we experimentally tested 17 predicted fusions in two pediatric glioma samples and validated 15 of these (88%) with precise resolution of the breakpoints via qPCR experiments and Sanger sequencing, with nearly perfect copy count concordance. Additionally, to further help display and understand the structure of CGRs, we have implemented CouGaR-viz, a generic stand-alone tool for visualization of the copy count of regions, breakpoints, and relevant genes.

Footnotes

  • Received June 9, 2016.
  • Accepted November 10, 2016.

This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

| Table of Contents

Preprint Server