High dimensional data analysis for new insight in ovarian cancer phenotypes

Standard treatment of patients with advanced stage ovarian cancer is surgical debulking followed by the combination of a platinum compound and paclitaxel. The 5-year survival rate of patients with advanced-stage disease is around 20%-30%. Recently, large clinical trials with different combinations and sequences of classic cyotoxic drugs indicated that further significant improvement in prognosis by this type of drugs is not to be expected. Therefore the ceiling seems to be reached for these conventional chemotherapeutic drugs, indicating that a paradigm shift is needed. Instead of treating all patients according to standard guidelines, we need to identify subgroups of patients that may benefit from targeted innovated molecular therapy. Utilizing gene-expression profiling might enable us to classify ovarian carcinomaas into such clinically relevant subtypes. Furthermore, gene-expression profiling might provide us with more insights into tumor aggressiveness and drug resistance, yielding potential new molecular targets for therapy. This thesis focuses on the identification of gene-expression profiles, biological pathways and transcription factors that are associated with survival and chemo-resistance in ovarian cancer.