Ovarian cancer tumours that relapse often switch into a more aggressive, drug-resistant state called carcinosarcoma, which contains both cancerous epithelial cells and cancer cells that have undergone a complete transformation into a mesenchymal-like form. This transformation, known as epithelial-to-mesenchymal transition (EMT), is a known driver of chemoresistance in many cancers. The researcher will use carcinosarcoma as a natural model to identify the molecular events that trigger EMT and chemoresistance in the more common types of ovarian cancer. By analysing tumour samples from over 2,000 patients—including matched samples taken at diagnosis and after relapse—the team will map when and how these events occur. They will then engineer laboratory models of these key events to test which drugs can block or reverse the resistance. If successful, this work could identify existing drugs—already with known safety profiles from other uses—that can be fast-tracked into early clinical trials for chemoresistant ovarian cancer. The goal is to prevent treatment failure and extend the duration of therapy response, potentially improving survival for patients whose cancers currently become untreatable.
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Background Ovarian cancer is usually highly chemosensitive. However, relapse is common and develops chemoresistance, leading to treatment failure. Ovarian carcinosarcoma is an uncommon ovarian cancer type harbouring both malignant epithelial (carcinomatous) and malignant mesenchymal (sarcomatous) compartments. The sarcomatous component arises through complete epithelial-to-mesenchymal transition (EMT) from carcinoma. EMT has been associated with chemoresistance in many tumour types, including ovarian cancer. Accordingly, carcinosarcoma demonstrates high levels of intrinsic chemoresistance. Carcinosarcoma therefore represents a unique opportunity to study mechanisms of EMT and associated chemoresistance that are therapeutically actionable in more common ovarian carcinomas. Aim: Exploit carcinosarcoma’s clear sarcomatous and carcinomatous compartments to identify drivers of EMT and associated chemoresistance in more common ovarian cancers. Characterise the frequency, timing, and clinical impact of EMT-associated events, identifying those linked to chemoresistance acquisition using large, clinically annotated patient cohorts. Engineer isogenic laboratory models of chemoresistance-associated events to determine their precise phenotypic consequences, then identify therapeutic strategies targeting these events. Methods Through multiomic profiling (exome, mRNA and microRNA sequencing) this project will identify drivers of acquired chemoresistance in ovarian cancer, using carcinosarcoma as a model of complete EMT and associated chemoresistance. I will determine the frequency, timing and impact of specific events on chemoresistance and outcome in the context of the most common type of ovarian cancer through profiling of spatially- and temporally-separated samples (multiple sites at diagnosis, matched diagnosis-relapse samples) and vast retrospective (N>2000) and clinical trial datasets. Using a large panel of available laboratory models (cell lines, primary patient-derived models, 3D tumouroids), I will engineer models of key chemoresistance-associated events for phenotypic characterisation using live-cell and high-content imaging (invasion, motility, proliferation, chemosensitivity, sensitivity to targeted agents). These assays will enable dissection of the precise phenotypic consequences of events. These chemoresistance-associated events will be mapped to targeted treatment strategies, enabling hypothesis-driven identification of therapeutic approaches to prevent or reverse chemoresistance. Additional opportunities to prevent/reverse chemoresistance will be identified using hypothesis-naïve drug screening approaches. How the results of this research will be used This research will yield validated drivers of chemoresistance and matching therapeutic opportunities for acceleration toward clinical studies of chemoresistant ovarian cancer. Fast-tracking to early phase studies is feasible for most agents due to known dosing/toxicity profiles from other indications. This strategy aims to prevent treatment failure and increase patient survival through improved magnitude and duration of therapy response. Findings will be directly relevant to other cancer types where EMT is linked to chemoresistance.
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