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

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.

Technical Summary
To exploit CIN as a therapeutic target, we first need to dissect the underlying biology. Three state-of-the-art technologies place us in a unique position to take a fundamentally fresh approach to dissect HGSOC-specific CIN mechanisms. Firstly, a diverse collection of patient-derived ovarian cancer models (OCMs) amenable to multi-omics, single-cell methods, cell biology and drug-sensitivity studies. Secondly, the ability to genetically engineer fallopian tube epithelial (FNE1) cells to recapitulate HGSOC-specific CIN. Finally, ClonMapper, a lineage tracing innovation to model clonal dynamics and drug resistance.

Work package 1: shallow single-cell whole-genome sequencing (WGS) to assemble ~100 HGSOC karyotypes, followed by clustering algorithms (NMF) and machine learning to reduce high-dimensional genomic features into a discrete number of CIN subgroups. These will be annotated with mutation profiles (25x bulk WGS); gene expression signatures (RNAseq); clinical outcomes (MCRC Biobank); and phenotype/drug sensitivity (WP2).

Work package 2: test hypotheses generated by WP1 multi-omics, using functional assays on OCMs to probe cell signalling networks, DNA damage repair pathways, cell cycle checkpoints, with proliferation (IncuCyte) and survival (clonogenic) assays probing pharmacological vulnerabilities. Targeting PTEN and Cyclin E in TP53-mutant FNE1 will generate a HR-proficient CIN model. Karyotypes of FNE1 models and OCM functional features will feed into clustering pipelines to refine the CIN subgroups (WP1).

Work package 3: lineage tracing in longitudinal OCMs to dissect how CIN drives drug resistance. Chemo-naïve OCMs will be barcoded and selective pressure applied to generate resistors. scRNAseq will define sub-populations and identify progenitors for isolation using ClonMapper's recall function, enabling the holy grail of directly comparing progenitor karyotype/phenotype with their in-vitro-derived resistors and cognate patient-derived relapse OCMs.