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Measuring single cell divisions in human tissues from multi-region sequencing data

Author

Listed:
  • Benjamin Werner

    (The Institute of Cancer Research
    Queen Mary University of London)

  • Jack Case

    (The Institute of Cancer Research
    University of Cambridge)

  • Marc J. Williams

    (Queen Mary University London
    University College London
    University College London)

  • Ketevan Chkhaidze

    (The Institute of Cancer Research)

  • Daniel Temko

    (Queen Mary University London)

  • Javier Fernández-Mateos

    (The Institute of Cancer Research)

  • George D. Cresswell

    (The Institute of Cancer Research)

  • Daniel Nichol

    (The Institute of Cancer Research)

  • William Cross

    (Queen Mary University London)

  • Inmaculada Spiteri

    (The Institute of Cancer Research)

  • Weini Huang

    (Sun Yat-sen University
    Queen Mary University London)

  • Ian P. M. Tomlinson

    (University of Birmingham)

  • Chris P. Barnes

    (University College London
    University College London)

  • Trevor A. Graham

    (Queen Mary University London)

  • Andrea Sottoriva

    (The Institute of Cancer Research)

Abstract

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.

Suggested Citation

  • Benjamin Werner & Jack Case & Marc J. Williams & Ketevan Chkhaidze & Daniel Temko & Javier Fernández-Mateos & George D. Cresswell & Daniel Nichol & William Cross & Inmaculada Spiteri & Weini Huang & I, 2020. "Measuring single cell divisions in human tissues from multi-region sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14844-6
    DOI: 10.1038/s41467-020-14844-6
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    Cited by:

    1. Gunnarsson, Einar Bjarki & Leder, Kevin & Foo, Jasmine, 2021. "Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability," Theoretical Population Biology, Elsevier, vol. 142(C), pages 67-90.
    2. Chandler D. Gatenbee & Ann-Marie Baker & Ryan O. Schenck & Maximilian Strobl & Jeffrey West & Margarida P. Neves & Sara Yakub Hasan & Eszter Lakatos & Pierre Martinez & William C. H. Cross & Marnix Ja, 2022. "Immunosuppressive niche engineering at the onset of human colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Claire Chung & Bert M. Verheijen & Zoe Navapanich & Eric G. McGann & Sarah Shemtov & Guan-Ju Lai & Payal Arora & Atif Towheed & Suraiya Haroon & Agnes Holczbauer & Sharon Chang & Zarko Manojlovic & St, 2023. "Evolutionary conservation of the fidelity of transcription," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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