Elucidating targetable tumour-stroma metabolic symbiosis in HGSOC metastasis

Supervisors: Sara Zanivan (CRUK Beatson Institute), Rosalind Glasspool (Beatson West of Scotland Cancer Centre)

Background: High grade serous ovarian (HGSO) carcinoma is the most lethal gynaecological disease. It is typically diagnosed when patients have widespread peritoneal metastases and although the available treatments are initially effective, the vast majority develop recurrent disease which ultimately becomes resistant to current therapies. HGSO carcinoma is characterised by mutations in p53. A proportion have also mutations or loss of BRCA 1 and 2 but other mutations only occur at low frequency. Though PARP inhibitors have shown considerable success in tumours with BRCA mutations or other defects in homologous recombination repair (HRD), their effect in non HRD tumours is modest and whilst HRD tumours acquire resistance in the majority of cases. There is therefore a need to develop novel strategies to target HGSO metastases. With this project we aim to identify ways of exploiting the metabolism of the tumour stroma to eradicate HGSO metastases, using patient samples and cutting edge MS-proteomic technology.

Stromal cells can constitute a sizable portion of HGSO tumours, both primary and metastases, and the presence of reactive stroma is associated with poor prognosis. Cancer-associated fibroblast (CAF) is one of the most abundant cell types in the stroma of HGSO tumours, and ours and other groups have shown that they play key roles in the progression of the disease. Recent works have shown that CAFs and cancer cells can establish metabolic symbioses critical for cancer cell and tumour growth, and that targeting CAF alone or with cancer cells to block those symbioses can halt tumour growth. Formalin fixed paraffin embedded (FFPE) tissues contain a snapshot of the molecular status of both stroma and cancer cells in a tumour, and this information can be exploited to identify coexistent metabolic alterations in CAFs and cancer cells that may represent metabolic symbiosis. In the past few years, mass spectrometry (MS) technology has advanced enormously and it is now possible to accurately quantify in depth proteomes from small amounts of samples, such as laser captured microdissected tissues. This project aims to identify CAF-cancer cell metabolic symbioses in HGSO metastases upon which cancer cells are critically dependent using FFPE patient samples, cutting edge MS-based proteomics, CAF-cancer cell cultures relevant to HGSO metastases and functional assays. This vulnerability can them be exploited for therapy.

In collaboration with Dr. Glasspool, the Zanivan lab is collecting HGSO metastasis tissues from patients that have undergone cytoreductive surgery. The proposed project aims to use the FFPE tissues to dissect CAF-rich stroma and cancer cells using laser-captured microdissection. The dissected samples will be processed and analysed with cutting edge MS proteomics to measure their proteomes, followed by data and statistical analyses to identify metabolic proteins/pathways altered in the tumour stroma and in the cancer cells when compared with their normal cells counterpart (normal fibroblasts for the stroma and fallopian tube epithelial cells for the cancer cells). These analyses will be performed using robust workflows already established in the Zanivan group. Amongst the regulated proteins/pathways we will prioritise for further functional validation, those for which there are druggable targets or drugs available, particularly those already used in the clinic. In this way, the results of this project can be swiftly translated into in vivo models and into the clinic. To assess the functional relevance of the selected hits, we will use genetic (shRNA/CRISPR-Cas9) and pharmacological approaches in CAF-cancer cell organotypic cultures of HGSO metastasis recently established in the Zanivan group and in collaboration with the Advanced Technology Facilities at the Beatson. The top hits will be extensively validated using in vitro models.


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