Prediction Project For Ovarian Disease Progression

By HospiMedica staff writers
Posted on 05 May 2006
A project has been initiated to identify proteins and changes in genes and RNA expression that could predict the course of ovarian cancer at the time of diagnosis.

Austrian researchers, together with more than 100 colleagues from Belgium, France, Germany, Israel, and the Netherlands, will examine molecular markers and their patterns in 200 ovarian cancer patients. Tissue samples, blood, and ascetic fluid will be tested for the salient molecular markers at the time of clinical diagnosis and six months after completion of standard therapy. The researchers will attempt to ascertain whether given markers are associated with therapy failure. If this is the case it will be possible to predict that the treatment will not be effective at the time of diagnosis.

Reasons for the lack of response to standard therapies will be investigated. Among these is the phenomenon of multidrug resistant genes and their proteins, which causes early efflux of chemotherapeutics from cancer cells. As a result, the chemotherapy agents do not remain in the cells for enough time to be fully effective.

For many years the glycoprotein CA125 marker in patients' serum has been used as a biomarker for diagnosis of ovarian cancer, but although it helps to monitor response to therapy it is too unspecific for early diagnosis and does not give any indication about how the disease will progress. The scientist coordinating the current research, Professor Robert Zeillinger of the Medical University in Vienna (Vienna, Austria) remarked, "Even the smallest tumors leave traces in the body. We want to find and understand the traces. This would permit quick and accurate diagnoses that would reveal which therapy offers the best prospects at an early stage.”

The research will be carried out by OVCAD (Ovarian Cancer Diagnosis), a specific targeted research project into cancer diagnostics that is supported by the European Union (EU).



Related Links:
Medical University in Vienna
OVCAD

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