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Inferring structural variant cancer cell fraction

Author

Listed:
  • Marek Cmero

    (Royal Melbourne Hospital and University of Melbourne
    The Epworth Prostate Centre, Epworth Hospital
    University of Melbourne
    Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research)

  • Ke Yuan

    (University of Glasgow, Sir Alwyn Williams Building
    University of Cambridge)

  • Cheng Soon Ong

    (University of Melbourne
    Machine Learning Research Group
    Research School of Computer Science, Australian National University)

  • Jan Schröder

    (Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research)

  • Niall M. Corcoran

    (Royal Melbourne Hospital and University of Melbourne
    The Epworth Prostate Centre, Epworth Hospital)

  • Tony Papenfuss

    (Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research)

  • Christopher M. Hovens

    (Royal Melbourne Hospital and University of Melbourne
    The Epworth Prostate Centre, Epworth Hospital)

  • Florian Markowetz

    (University of Cambridge)

  • Geoff Macintyre

    (University of Melbourne
    University of Cambridge)

Abstract

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.

Suggested Citation

  • Marek Cmero & Ke Yuan & Cheng Soon Ong & Jan Schröder & Niall M. Corcoran & Tony Papenfuss & Christopher M. Hovens & Florian Markowetz & Geoff Macintyre, 2020. "Inferring structural variant cancer cell fraction," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14351-8
    DOI: 10.1038/s41467-020-14351-8
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    Cited by:

    1. Naser Ansari-Pour & Yonglan Zheng & Toshio F. Yoshimatsu & Ayodele Sanni & Mustapha Ajani & Jean-Baptiste Reynier & Avraam Tapinos & Jason J. Pitt & Stefan Dentro & Anna Woodard & Padma Sheila Rajagop, 2021. "Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Dan Daniel Erdmann-Pham & Sanjit Singh Batra & Timothy K. Turkalo & James Durbin & Marco Blanchette & Iwei Yeh & Hunter Shain & Boris C. Bastian & Yun S. Song & Daniel S. Rokhsar & Dirk Hockemeyer, 2023. "Tracing cancer evolution and heterogeneity using Hi-C," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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