Translational Significance of miRNAs to Optimize Diagnostics and Determination of Prognosis in Ovarian Cancer

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Ovarian cancer belongs to the leading causes of malignancy-related deaths in women. Its diagnosis is complicated by late-onset symptoms, often leading to detection in advanced stages and low overall survival rates. Current screening modalities are insufficient and failed to reduce ovarian cancer mortality. MicroRNAs (miRNAs) are non-coding RNAs which are involved in gene expression regulation. They can be measured in serum and were previously flagged with promising diagnostic potential in ovarian cancer. Furthermore, they are directly linked to tumor biology. For refined diagnostics, we propose to combine techniques of next-generation sequencing with novel statistical measures. Due to its complex regulation, analysis of miRNA networks by using machine learning may be useful to improve accuracy for screening and classification of patient risk. Growing understanding of miRNA regulation may furthermore be used to experimentally target tumor growth.The objectives of the proposed research plan are as follows: 1) Demographic confounders to miRNA networks are studied in a cohort of patients to improve its diagnostic specifications for screening and diagnosis of ovarian cancer. 2) To improve the diagnostic potential of miRNA networks, characteristics and subtypes of ovarian cancer will be studied in a cohort of patients to be included into a miRNA network model. 3) The potential of miRNA network kinetics is evaluated during therapy to determine its significance to predict therapeutic response and recurrence of disease. 4) As miRNAs are involved in ovarian cancer biology, the generated data from previous objectives will be used to target cancer growth by modulating systemic miRNA response in an experimental animal model.