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Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer

Author

Listed:
  • Cameron Herberts

    (University of British Columbia)

  • Matti Annala

    (University of British Columbia
    Tampere University and Tays Cancer Center)

  • Joonatan Sipola

    (Tampere University and Tays Cancer Center)

  • Sarah W. S. Ng

    (University of British Columbia)

  • Xinyi E. Chen

    (University of British Columbia)

  • Anssi Nurminen

    (Tampere University and Tays Cancer Center)

  • Olga V. Korhonen

    (Tampere University and Tays Cancer Center)

  • Aslı D. Munzur

    (University of British Columbia)

  • Kevin Beja

    (University of British Columbia)

  • Elena Schönlau

    (University of British Columbia)

  • Cecily Q. Bernales

    (University of British Columbia)

  • Elie Ritch

    (University of British Columbia)

  • Jack V. W. Bacon

    (University of British Columbia)

  • Nathan A. Lack

    (University of British Columbia
    Koç University
    Koç University)

  • Matti Nykter

    (Tampere University and Tays Cancer Center)

  • Rahul Aggarwal

    (University of California San Francisco
    University of California San Francisco)

  • Eric J. Small

    (University of California San Francisco
    University of California San Francisco)

  • Martin E. Gleave

    (University of British Columbia)

  • David A. Quigley

    (University of California San Francisco
    University of California San Francisco
    University of California San Francisco)

  • Felix Y. Feng

    (University of California San Francisco
    University of California San Francisco
    University of California San Francisco
    University of California San Francisco)

  • Kim N. Chi

    (University of British Columbia
    BC Cancer)

  • Alexander W. Wyatt

    (University of British Columbia
    BC Cancer)

Abstract

> Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2–12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

Suggested Citation

  • Cameron Herberts & Matti Annala & Joonatan Sipola & Sarah W. S. Ng & Xinyi E. Chen & Anssi Nurminen & Olga V. Korhonen & Aslı D. Munzur & Kevin Beja & Elena Schönlau & Cecily Q. Bernales & Elie Ritch , 2022. "Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer," Nature, Nature, vol. 608(7921), pages 199-208, August.
  • Handle: RePEc:nat:nature:v:608:y:2022:i:7921:d:10.1038_s41586-022-04975-9
    DOI: 10.1038/s41586-022-04975-9
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    Cited by:

    1. Nicolette M. Fonseca & Corinne Maurice-Dror & Cameron Herberts & Wilson Tu & William Fan & Andrew J. Murtha & Catarina Kollmannsberger & Edmond M. Kwan & Karan Parekh & Elena Schönlau & Cecily Q. Bern, 2024. "Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Xian Sun & Dongshuo Yin & Fei Qin & Hongfeng Yu & Wanxuan Lu & Fanglong Yao & Qibin He & Xingliang Huang & Zhiyuan Yan & Peijin Wang & Chubo Deng & Nayu Liu & Yiran Yang & Wei Liang & Ruiping Wang & C, 2023. "Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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