Clonality Inference from Single Tumor Samples Using Low Coverage Sequence Data

  • Nilgun Donmez
  • Salem Malikic
  • Alexander W. Wyatt
  • Martin E. Gleave
  • Colin C. Collins
  • S. Cenk Sahinalp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9649)

Abstract

Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. While several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims to infer the subclonal composition using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy even restricted to a coverage depth of \(\sim \)30x.

Keywords

Intra-tumor heterogeneity Cancer progression DNA sequencing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nilgun Donmez
    • 1
    • 2
  • Salem Malikic
    • 1
    • 2
  • Alexander W. Wyatt
    • 2
    • 3
  • Martin E. Gleave
    • 2
  • Colin C. Collins
    • 2
    • 3
  • S. Cenk Sahinalp
    • 1
    • 2
    • 4
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Vancouver Prostate CentreVancouverCanada
  3. 3.Department of Urologic SciencesUniversity of British ColumbiaVancouverCanada
  4. 4.School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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