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Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Author

Listed:
  • Salem Malikic

    (Simon Fraser University
    Vancouver Prostate Centre)

  • Katharina Jahn

    (Department of Biosystems Science and Engineering, ETH Zurich
    Swiss Institute of Bioinformatics)

  • Jack Kuipers

    (Department of Biosystems Science and Engineering, ETH Zurich
    Swiss Institute of Bioinformatics)

  • S. Cenk Sahinalp

    (Indiana University)

  • Niko Beerenwinkel

    (Department of Biosystems Science and Engineering, ETH Zurich
    Swiss Institute of Bioinformatics)

Abstract

Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees.

Suggested Citation

  • Salem Malikic & Katharina Jahn & Jack Kuipers & S. Cenk Sahinalp & Niko Beerenwinkel, 2019. "Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10737-5
    DOI: 10.1038/s41467-019-10737-5
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    Cited by:

    1. Seong-Hwan Jun & Hosein Toosi & Jeff Mold & Camilla Engblom & Xinsong Chen & Ciara O’Flanagan & Michael Hagemann-Jensen & Rickard Sandberg & Samuel Aparicio & Johan Hartman & Andrew Roth & Jens Lagerg, 2023. "Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Haochen Zhang & Elias-Ramzey Karnoub & Shigeaki Umeda & Ronan Chaligné & Ignas Masilionis & Caitlin A. McIntyre & Palash Sashittal & Akimasa Hayashi & Amanda Zucker & Katelyn Mullen & Jungeui Hong & A, 2023. "Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. David Lähnemann & Johannes Köster & Ute Fischer & Arndt Borkhardt & Alice C. McHardy & Alexander Schönhuth, 2021. "Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo," Nature Communications, Nature, vol. 12(1), pages 1-11, December.

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