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A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity

Author

Listed:
  • Louisa Nelson

    (University of Manchester, Manchester Cancer Research Centre)

  • Anthony Tighe

    (University of Manchester, Manchester Cancer Research Centre)

  • Anya Golder

    (University of Manchester, Manchester Cancer Research Centre)

  • Samantha Littler

    (University of Manchester, Manchester Cancer Research Centre)

  • Bjorn Bakker

    (University of Groningen, University Medical Center Groningen)

  • Daniela Moralli

    (University of Oxford, Roosevelt Drive)

  • Syed Murtuza Baker

    (University of Manchester)

  • Ian J. Donaldson

    (University of Manchester)

  • Diana C. J. Spierings

    (University of Groningen, University Medical Center Groningen)

  • René Wardenaar

    (University of Groningen, University Medical Center Groningen)

  • Bethanie Neale

    (Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre)

  • George J. Burghel

    (Central Manchester NHS Foundation Trust)

  • Brett Winter-Roach

    (The Christie NHS Foundation Trust)

  • Richard Edmondson

    (University of Manchester, Manchester Cancer Research Centre
    Central Manchester NHS Foundation Trust)

  • Andrew R. Clamp

    (The Christie NHS Foundation Trust)

  • Gordon C. Jayson

    (University of Manchester, Manchester Cancer Research Centre
    The Christie NHS Foundation Trust)

  • Sudha Desai

    (The Christie NHS Foundation Trust)

  • Catherine M. Green

    (University of Oxford, Roosevelt Drive)

  • Andy Hayes

    (University of Manchester)

  • Floris Foijer

    (University of Groningen, University Medical Center Groningen)

  • Robert D. Morgan

    (University of Manchester, Manchester Cancer Research Centre
    The Christie NHS Foundation Trust)

  • Stephen S. Taylor

    (University of Manchester, Manchester Cancer Research Centre)

Abstract

High-grade serous ovarian carcinoma is characterised by TP53 mutation and extensive chromosome instability (CIN). Because our understanding of CIN mechanisms is based largely on analysing established cell lines, we developed a workflow for generating ex vivo cultures from patient biopsies to provide models that support interrogation of CIN mechanisms in cells not extensively cultured in vitro. Here, we describe a “living biobank” of ovarian cancer models with extensive replicative capacity, derived from both ascites and solid biopsies. Fifteen models are characterised by p53 profiling, exome sequencing and transcriptomics, and karyotyped using single-cell whole-genome sequencing. Time-lapse microscopy reveals catastrophic and highly heterogeneous mitoses, suggesting that analysis of established cell lines probably underestimates mitotic dysfunction in advanced human cancers. Drug profiling reveals cisplatin sensitivities consistent with patient responses, demonstrating that this workflow has potential to generate personalized avatars with advantages over current pre-clinical models and the potential to guide clinical decision making.

Suggested Citation

  • Louisa Nelson & Anthony Tighe & Anya Golder & Samantha Littler & Bjorn Bakker & Daniela Moralli & Syed Murtuza Baker & Ian J. Donaldson & Diana C. J. Spierings & René Wardenaar & Bethanie Neale & Geor, 2020. "A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity," Nature Communications, Nature, vol. 11(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14551-2
    DOI: 10.1038/s41467-020-14551-2
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    Cited by:

    1. Chunman Zuo & Yijian Zhang & Chen Cao & Jinwang Feng & Mingqi Jiao & Luonan Chen, 2022. "Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Filipe Correia Martins & Dominique-Laurent Couturier & Ines Santiago & Carolin Margarethe Sauer & Maria Vias & Mihaela Angelova & Deborah Sanders & Anna Piskorz & James Hall & Karen Hosking & Anumithr, 2022. "Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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