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Recovering rearranged cancer chromosomes from karyotype graphs

View ORCID ProfileSergey Aganezov, Ilya Zban, View ORCID ProfileVitaly Aksenov, View ORCID ProfileNikita Alexeev, View ORCID ProfileMichael C. Schatz
doi: https://doi.org/10.1101/831057
Sergey Aganezov
1Computer Science Department, Johns Hopkins University, Baltimore, MD, USA
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  • For correspondence: aganezov@cs.jhu.edu
Ilya Zban
2International Laboratory “Computer technologies”, ITMO University, Saint Petersburg, Russia
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Vitaly Aksenov
2International Laboratory “Computer technologies”, ITMO University, Saint Petersburg, Russia
3Institute of Science and Technology, Klosterneuburg, Austria
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Nikita Alexeev
2International Laboratory “Computer technologies”, ITMO University, Saint Petersburg, Russia
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Michael C. Schatz
1Computer Science Department, Johns Hopkins University, Baltimore, MD, USA
4Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Abstract

Many 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.

Here 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. CCR 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.

  • List of abbreviations

    CNA
    Copy Number Aberrations;
    NA
    Novel Adjacency;
    ED
    Eulerian Decomposition
    EDP
    Eulerian Decomposition Problem;
    CCCP
    Consistent Contig Covering Problem;
    IAG
    Interval Adjacency Graph;
    WGD
    Whole Genome Duplication;
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted November 05, 2019.
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    Recovering rearranged cancer chromosomes from karyotype graphs
    Sergey Aganezov, Ilya Zban, Vitaly Aksenov, Nikita Alexeev, Michael C. Schatz
    bioRxiv 831057; doi: https://doi.org/10.1101/831057
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    Recovering rearranged cancer chromosomes from karyotype graphs
    Sergey Aganezov, Ilya Zban, Vitaly Aksenov, Nikita Alexeev, Michael C. Schatz
    bioRxiv 831057; doi: https://doi.org/10.1101/831057

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