"About 75% of advanced epithelial ovarian cancer (EOC) patients respond to first-line surgery and chemotherapy but most relapse and ultimately acquire platinum resistance which soon leads to death. Relapsed high grade serous ovarian cancer (HGSOC) is the single main cause of EOC-related morbidity and mortality (despite the fact that HGSOC is highly chemosensitive). We hypothesize that the primary tumour includes a small population of resistant cells that are ultimately responsible for relapse and that by targeting this population front-line we may prolong disease-free survival or even achieve cure. OCTIPS will use unique retrospective and novel prospective paired tumour samples collected at the time of diagnosis and relapse to identify and validate molecules and pathways responsible for relapse. This identification will employ cutting edge high throughput multiplatform analyses such as next generation sequencing, mRNA and miRNA expression arrays and SNP array. Known and newly defined molecules or pathways will be evaluated in innovative integrated cancer model systems, utilising cell lines and avian egg and murine xenografts. New therapies to target these molecules and pathways will be developed and validated in these model systems. In order to translate these findings into patient benefit, agents that target the relapsing cell population will be tested for tolerability, efficacy, ability to combine with first line chemotherapy and then in randomised first line trials by the OCTIPS consortium.
By translating the clinical observation of treatment failures into innovative cancer models that mimic relapsed ovarian cancer, we will validate improved front-line therapeutic strategies to help prolong patient survival. The impact of this application is that it defines a highly rigorous approach to integrate the bedside to bench to bedside paradigm, leading to novel prognosis-changing strategies for the treatment of ovarian cancer patients."
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Funding SchemeCP-FP - Small or medium-scale focused research project
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DD1 5JJ Dundee
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