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Joint Inference of Genome Structure and Content in Heterogeneous Tumor Samples

  • Andrew McPhersonEmail author
  • Andrew Roth
  • Cedric Chauve
  • S. Cenk Sahinalp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)

Abstract

For a genomically unstable cancer, a single tumour biopsy will often contain a mixture of competing tumour clones. These tumour clones frequently differ with respect to their genomic content (copy number of each chromosome segment) and structure (order/adjacency of segments on tumour chromosomes). Whole genome sequencing mixes the signals of tumour clones and contaminating normal cells. The ability to unmix these signals and infer divergent genome structure and content is relevant to current avenues of cancer research. We propose a method to unmix tumour and contaminating normal signals and jointly predict genome structure and content of each tumour clone.

Keywords

Cancer Heterogeneity Copy number Rearrangements 

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References

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    Oesper, L., et al.: Quantifying Tumor Heterogeneity in Whole-Genome and Whole-Exome Sequencing Data. Bioinformatics 30(24), 3532–3540 (2014)CrossRefGoogle Scholar
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    Oesper, L., et al.: Reconstructing cancer genomes from paired-end sequencing data. BMC Bioinformatics 13(Suppl. 6), S10 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrew McPherson
    • 1
    • 2
    Email author
  • Andrew Roth
    • 2
    • 3
  • Cedric Chauve
    • 4
  • S. Cenk Sahinalp
    • 1
    • 5
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Department of Molecular OncologyBC Cancer AgencyVancouverCanada
  3. 3.Bioinformatics Graduate ProgramUniversity of British ColumbiaVancouverCanada
  4. 4.Department of MathematicsSimon Fraser UniversityBurnabyCanada
  5. 5.School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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