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

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    Authors
    Nelson, Louisa
    Tighe, Anthony
    Golder, Anya
    Littler, Samantha
    Bakker, B
    Moralli, D
    Murtuza, BS
    Donaldson, IJ
    Spierings, DCJ
    Wardenaar, R
    Neale, B
    Burghel, GJ
    Winter-Roach, Brett
    Edmondson, Richard
    Clamp, Andrew R
    Jayson, Gordon C
    Desai, Sudha
    Green, CM
    Hayes, A
    Foijer, F
    Morgan, Robert David
    Taylor, Stephen S
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    Affiliation
    Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester
    Issue Date
    2020
    
    Metadata
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    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.
    Citation
    Nelson L, Tighe A, Golder A, Littler S, Bakker B, Moralli D, et al. A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity. Nat Commun. 2020;11:822.
    Journal
    Nature Communications
    URI
    http://hdl.handle.net/10541/622815
    DOI
    10.1038/s41467-020-14551-2
    PubMed ID
    32054838
    Additional Links
    https://dx.doi.org/10.1038/s41467-020-14551-2
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41467-020-14551-2
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