High-grade serous ovarian cancer: exploiting a living biobank to delineate mechanisms underlying disease-specific chromosome instability

25年前，我们对癌症基因组学的理解经历了一个范式转变，当时发现癌细胞核型由于潜在的染色体不稳定性（CIN）而处于不断变化的状态，即染色体的连续获得和丢失和/或结构重排的获得。事实上，现在人们普遍认为CIN是肿瘤异质性，表型适应和耐药性的主要驱动因素。在过去的二十年里，我们已经了解了很多关于染色体复制和分离的分子机制，以及相关的细胞周期检查点控制。然而，直接参与这些过程的基因中的致癌突变非常罕见，这意味着我们对CIN获得的基本原理的理解，它如何驱动肿瘤发生并在面对选择压力时改变轨迹仍然非常有限。因此，我们的目标是了解这些原则，以利用CIN作为一个治疗靶点。尽管付出了巨大的努力，多种因素阻碍了进展。由于实验易处理性，机制研究通常集中在有限数量的已建立的癌细胞系上，但它们往往具有有限的临床注释，不反映疾病异质性，并且缺乏治疗前/治疗后对应物。此外，最适合长期细胞培养的更适合的亚克隆的生长产生相对稳定的核型。其他混杂因素是由于广泛的体外繁殖引起的遗传漂移，以及忽略疾病特异性CIN途径可能性的肿瘤部位不可知论哲学。另一个限制是缺乏非转化的，核型稳定的模型系统，代表细胞的起源，以概括CIN pathways.While癌症测序项目可以分析大量的临床注释样本，依赖于存档活检导致间质污染，单细胞方法在技术上具有挑战性，并与功能实验测试新兴的假设是不可能的。虽然测序空间分辨活检可以重建进化轨迹，但匹配的化疗初治、治疗和复发样本的纵向队列不太常见。此外，分离耐药克隆的祖细胞是不可能的。因此，为了定义CIN如何驱动肿瘤发生和耐药性的基本原理，我们现在提出了一种全新的方法，具有几个关键优势，以解决迄今为止阻碍进展的局限性。重要的是，我们将采取针对特定疾病的方法，重点关注高级别浆液性卵巢癌（HGSOC），其中CIN是关键驱动因素，获得性耐药是关键临床挑战。首先，我们将利用我们的患者来源的卵巢癌模型（OCM）的活体生物库，这可能是最大和最多样化的原代HGSOC细胞培养物集合。OCM是早期传代的纯化肿瘤组分，具有HGSOC的标志和异质性。再加上能够实现广泛增殖潜力的细胞培养系统，OCM适合多组学，包括单细胞组学，高分辨率细胞生物学研究和药物敏感性分析。随着生物库的成熟，我们正在建立纵向队列，即来自化疗前，化疗中和化疗后活检的OCM。其次，由于HGSOC来源于输卵管上皮细胞，我们建立了这些细胞的FNE 1模型系统。FNE 1细胞是非转化的，核型稳定，但我们已经证明，引入HGSOC特异性遗传病变足以诱导CIN，产生与我们的患者来源的模型相似的核型。第三，为了分离耐药后代的祖细胞，我们将联合收割机与我们的纵向OCM结合，研究对化疗反应的克隆动力学。

25 years ago, our understanding of cancer genomics underwent a paradigm shift when it was discovered that cancer cell karyotypes are in a state of constant flux due to underlying chromosome instability (CIN), ie continuous gain and loss of chromosomes and/or acquisition of structural rearrangements. And indeed, it is now widely accepted that CIN is a major driver of tumour heterogeneity, phenotypic adaptation and drug resistance.In the last two decades, we have learnt a great deal about the molecular mechanisms responsible for chromosome replication and segregation, as well as the associated cell cycle checkpoint controls. However, oncogenic mutations in genes directly involved in these processes are extremely rare, meaning our understanding of the basic principles governing the acquisition of CIN, how it drives tumorigenesis and alters trajectories in the face of selective pressures remains more limited. Thus we aim to understand these principles in order to exploit CIN as a therapeutic target.Despite intense efforts, multiple factors have hindered progress. Mechanistic studies typically focus on a limited number of established cancer cell lines, due to experimental tractability, yet they tend to have limited clinical annotation, do not reflect disease heterogeneity, and lack pre-/post- treatment counterparts. Moreover, outgrowth of fitter subclones best suited for long-term cell culture yields relatively stable karyotypes. Additional confounding factors are genetic drift due to extensive in vitro propagation, and a tumour-site agnostic philosophy that ignores the possibility of disease-specific CIN pathways. Another limitation is the lack of non-transformed, karyotypically-stable model systems that represent the cell-of-origin to recapitulate CIN pathways.While cancer sequencing projects can analyse large cohorts of clinically annotated samples, reliance on archival biopsies results in stromal contamination, single-cell approaches are technically challenging, and testing emerging hypotheses with functional experiments is impossible. While sequencing spatially resolved biopsies allows reconstruction of evolutionary trajectories, longitudinal cohorts of matched chemo-naïve, on-treatment and relapse samples are less common. Moreover, isolating progenitors of drug-resistant clones is impossible.Therefore, to define the basic principles governing how CIN drives tumorigenesis and drug resistance, we now propose a fundamentally fresh approach with several key benefits to address the limitations that have hindered progress to date. Importantly, we will take a disease-specific approach, focusing on high-grade serous ovarian cancer (HGSOC), where CIN is the key driver and acquired drug resistance the key clinical challenge.Firstly, we will exploit our living biobank of patient-derived ovarian cancer models (OCMs), possibly the largest and most diverse collection of primary HGSOC cell cultures. OCMs are early passage, purified tumour fractions that possess the hallmarks and heterogeneity of HGSOC. Coupled with a cell culture system that enables extensive proliferative potential, OCMs are amenable to multi-omics, including single-cell omics, high-resolution cell biology studies and drug-sensitivity profiling. As the biobank matures, we are assembling longitudinal cohorts, ie OCMs derived from biopsies taken before, during and after chemotherapy.Secondly, as HGSOC originates from fallopian tube epithelial cells, we have established the FNE1 model system of these cells. FNE1 cells are non-transformed and karyotypically stable, but we have shown that introducing HGSOC-specific genetic lesions is sufficient to induce CIN, yielding karyotypes similar to those of our patient-derived models.And thirdly, to isolate the progenitors of drug-resistant descendants, we will combine a state-of-the-art barcode-based lineage tracing technology with our longitudinal OCMs to study clonal dynamics in response to chemotherapy.