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Ovarian Cancer - Diagnosing a Silent Killer

Final Report Summary - OVCAD (Ovarian Cancer - Diagnosing a Silent Killer)

In Europe 63 000 ovarian cancer cases are diagnosed and 41 000 ovarian cancer patients die annually. 75 % of patients are diagnosed at advanced stages due to an asymptomatic course. 75 % of these patients die within 5 years. Treatment involves surgery followed by chemotherapy. However, 25 % of patients relapse within six months after initial treatment. There is doubt whether these patients benefit from this therapy at all. Recurrent disease is diagnosed by clinical evidences or by CA125 dynamics. But detection is limited due to lack of sensitivity and specificity, as is the case with primary diagnosis. Currently, there is no method to detect minimal disease, the first indicator of therapy failure and precursor of recurrence, which inevitably leaves specific traces throughout the body. There is strong need for molecular-oriented research to detect minimal disease in order to disburden patients from an inefficient and toxic therapy.

The aim of this project was to define clinically useful molecular-orientated early detection of minimal disease in ovarian cancer that can identify patients not responding to the standard therapy at the time of surgery. This would eventually lead to alternative therapy modalities, which can really bring benefits to this group of patients. Signatures that signal the presence of minimal disease were systematically investigated at various molecular levels (DNA, RNA and protein) and in a broad spectrum of biological materials (tumour tissue, disseminated tumour cells, sera, white blood cells, ascites) from ovarian cancer patients. Profiling of chromosomal loss or gain, gene methylation and mutations, mRNA expression and protein were also analyzed with the aim of extracting signatures that predict patient's outcome or indicate minimal disease for early primary diagnosis.

In the frame of the OVCAD project, the ovarian cancer tumour bank has been established. It contains biological materials from blood, tumour tissues and ascites of about 300 ovarian cancer patients with advanced FIGO stage and treated with surgery and platinum-base chemotherapy. With these clinical materials, epigenomic, genomic, transcriptomic, and proteomic data from different biological materials have been generated. In addition, candidate markers at various molecular levels were validated. Models and signatures have been generated for the prediction of response of the patients to standard treatment, and to predict the outcome of the patients.

A database has been established for the depository of all data generated from OVCAD, which will enable further collaboration of OVCA partners by data analysis and further networking in ovarian cancer research. Mechanisms involved in the drug resistance in ovarian cancer were investigated and an antibody-based therapy was evaluated. Circulating tumour cells were analysed for their protein expression, genomic alterations and gene expression and were found to be a very heterogeneous cell population. In addition, a three-step technology combining leukapheresis, elutriation and fluorescence activated cell sorting technique was established for the enrichment and isolation of circulating tumour cells from a large blood volume.