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Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data

Author

Listed:
  • Georgette Tanner

    (University of Leeds, St James’s University Hospital)

  • David R. Westhead

    (University of Leeds)

  • Alastair Droop

    (Wellcome Sanger Institute)

  • Lucy F. Stead

    (University of Leeds, St James’s University Hospital)

Abstract

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Suggested Citation

  • Georgette Tanner & David R. Westhead & Alastair Droop & Lucy F. Stead, 2021. "Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26698-7
    DOI: 10.1038/s41467-021-26698-7
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    References listed on IDEAS

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    1. Yao Xiao & Xueqing Wang & Hongjiu Zhang & Peter J. Ulintz & Hongyang Li & Yuanfang Guan, 2020. "FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    Cited by:

    1. Kang Wang & Ioannis Zerdes & Henrik J. Johansson & Dhifaf Sarhan & Yizhe Sun & Dimitris C. Kanellis & Emmanouil G. Sifakis & Artur Mezheyeuski & Xingrong Liu & Niklas Loman & Ingrid Hedenfalk & Jonas , 2024. "Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    2. Yoshitaka Sakamoto & Shuhei Miyake & Miho Oka & Akinori Kanai & Yosuke Kawai & Satoi Nagasawa & Yuichi Shiraishi & Katsushi Tokunaga & Takashi Kohno & Masahide Seki & Yutaka Suzuki & Ayako Suzuki, 2022. "Phasing analysis of lung cancer genomes using a long read sequencer," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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