Recovering rearranged cancer chromosomes from karyotype graphs

Abstract

BackgroundMany cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes. Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome. Recently it became possible to reconcile both of these types of large-scale variations into a karyotype graph representation of the rearranged cancer genomes. Such a representation, however, does not directly describe the linear and/or circular structure of the underlying rearranged cancer chromosomes, thus limiting possible analysis of cancer genomes somatic evolutionary process as well as functional genomic changes brought by the large-scale genome rearrangements.ResultsHere we address the aforementioned limitation by introducing a novel methodological framework for recovering rearranged cancer chromosomes from karyotype graphs. For a cancer karyotype graph we formulate an Eulerian Decomposition Problem (EDP) of finding a collection of linear and/or circular rearranged cancer chromosomes that are determined by the graph. We derive and prove computational complexities for several variations of the EDP. We then demonstrate that Eulerian decomposition of the cancer karyotype graphs is not always unique and present the Consistent Contig Covering Problem (CCCP) of recovering unambiguous cancer contigs from the cancer karyotype graph, and describe a novel algorithm CCR capable of solving CCCP in polynomial time. We apply CCR on a prostate cancer dataset and demonstrate that it is capable of consistently recovering large cancer contigs even when underlying cancer genomes are highly rearranged.ConclusionsCCR can recover rearranged cancer contigs from karyotype graphs thereby addressing existing limitation in inferring chromosomal structures of rearranged cancer genomes and advancing our understanding of both patient/cancer-specific as well as the overall genetic instability in cancer.

Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing

“…Further development of methods capable of incorporating SNPs, small indels, SVs, and large-scale CNVs, as well as examining somatic phylogenies and cancer clone trajectories, will require both shortand long-read sequencing, with long reads proving critical for sensitive and accurate inference of SVs. Integration of long-read SV analysis can also benefit methods (Aganezov et al 2019;Cameron et al 2019;Deshpande et al 2019) focusing on recovering linear organization of rearranged cancer genomes.…”

Integrative reconstruction of cancer genome karyotypes using InfoGenomeR

“…ShatterSeek 19 used an integrative approach of SVs and CNAs to identify CT; however, it did not provide karyotype structures such as derivative chromosomes and DMs resulted from CT. Recently, a decomposition method for DMs and/or linear chromosomes based on a haplotype graph has been introduced 42 . Nevertheless, this method lacks interpretation of other topologies such as HSRs, HSR/DMs or CTs.…”

“…Circular chromosomes, C , included in the DM cluster were observed as circular paths. Eulerian decomposition problem (EDP) was defined to find linear and circular chromosomes from the breakpoint graph 42 . Although the min-EDP, which minimised the number of paths and cycles, ∣ P ∣ + ∣ C ∣, was previously suggested to describe the most possible karyotype 42 , the min-EDP was not always biologically relevant (i.e.…”

GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

“…As we move closer to a world in which the CNA and SV primitives can be reliably detected, accurate interpretation of the causative biological events becomes increasingly possible by integrated analysis of this knowledge. While progress has been made on derivative chromosome reconstruction using long reads [ 13 ], reconstruction of complex events such as chromothripsis has been problematic for short reads [ 14 , 15 ]. To date, SV phasing has been used to reduce the complexity of reconstruction for long read based approaches [ 16 ] but has not been done by short read callers.…”