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卵巢癌体外模型的活体生物银行揭示了深刻的有丝分裂异质性。

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

机构信息

Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Cancer Research Centre, Wilmslow Road, Manchester, M20 4GJ, UK.

European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands.

出版信息

Nat Commun. 2020 Feb 13;11(1):822. doi: 10.1038/s41467-020-14551-2.

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.

摘要

高级别浆液性卵巢癌的特征是 TP53 突变和广泛的染色体不稳定性(CIN)。由于我们对 CIN 机制的理解在很大程度上是基于对已建立的细胞系进行分析,因此我们开发了一种从患者活检中生成体外培养物的工作流程,以提供支持在体外未广泛培养的细胞中探究 CIN 机制的模型。在这里,我们描述了一种具有广泛复制能力的卵巢癌模型的“活体生物银行”,这些模型源自腹水和实体活检。对 15 种模型进行了 p53 分析、外显子组测序和转录组学分析,并通过单细胞全基因组测序进行了染色体分析。延时显微镜揭示了灾难性的、高度异质性的有丝分裂,表明对已建立的细胞系的分析可能低估了晚期人类癌症中的有丝分裂功能障碍。药物分析显示顺铂敏感性与患者反应一致,表明该工作流程具有生成具有优势的个性化模拟物的潜力,优于当前的临床前模型,并有可能指导临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a92/7018727/f32826ba7716/41467_2020_14551_Fig1_HTML.jpg

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