Identification of complex genomic rearrangements in cancers using CouGaR

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.

Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples

“…We reformulate Problem 1 of finding a proper derived genome G from the measurement data as a graph-theoretic problem. First, we define the diploid interval adjacency graph (DIAG), which can be viewed as a generalization of a breakpoint graph used in the area of comparative genomics [1,65,3], or graphs used in the area of structural analysis of normal and cancer genomes with haploid reference structure [36,42,32,12,15,35]. A DIAG G(R, É A N ) = (V , E) is constructed on a set {1, 2, … , m} of segments, and a set A = A(R) ‰ H( É A N ) of adjacencies.…”

Reconstructing and characterizing focal amplifications in cancer using AmpliconArchitect

“…We picked the seed intervals using the ReadDepth as described on Online methods section 1. 18 . We downsampled the bam files to coverage between 4X-7X by selecting read pairs with specific read group identifiers.…”

“…Traditional structural variant (SV) analyses cannot decipher complex rearrangements [10][11][12][13] . The few methods that extend the analysis, chain together breakpoints into paths and cycles, but often do not reconstruct the amplicon in the specific region of interest, and do not provide a comprehensive view of alternative structures [14][15][16][17][18][19] . Reconstruction remains challenging due to extreme variability in copy counts (5X-200X) and sizes (100kbp-25Mbp) of amplicons, samples containing heterogeneous mixture of multiple amplicon structures, and inaccuracy of SV identification.…”

Recovering rearranged cancer chromosomes from karyotype graphs

“…Here we consider the problem of inferring sequential structure of rearranged linear/circular chromosomes in a cancer genome given its karyotype graph representation. In previous studies either general observations about necessary and sufficient conditions on the karyotype graphs structure for the existence of the underlying cancer genome [17,1], or attempts at extracting information about specific local rearranged structures (e.g., amplicons, complex rearrangements, etc) [3,16,4] were made. In the present study we formulate a Eulerian Decomposition Problem (EDP) for a cancer karyotype graph with the objective of finding a covering collection of linear and/or circular paths/cycles (i.e., chromosomes).…”