The main purpose of this PhD project is to identify biomarkers that can be used for early detection, drug development, and stratification of early-stage (stage I-II) and late-stage (stage III-IV) ovarian cancer (OC) patients. We aim to identify candidate biomarkers that can sub-classify OC according to histotype, predict patient clinical outcome, and may improve the decision-making process for OC treatment. Subsequently, these biomarkers may be useful as targets for drug development and improved therapy of the disease. OC is mainly subdivided into five histotypes of epithelial origin: high-grade serous (HGSC), clear-cell (CCC), endometrioid (EC), mucinous (MC), and low-grade serous carcinomas (LGSC) with different clinical (origin, risk factors, survival, response to therapy) and molecular behavior (genetic and epigenetic features). OC is the second most common and most lethal gynecologic malignancy in the western world, affecting approximately 67,000 new patients annually in the EU and US combined . After initial successful treatment, 80% of all patients experience recurrent disease, which is often resistant to platinum-based chemotherapy. This creates a large unmet need for novel targeted therapies. We hypothesize that cancer driver genes involved in genetic and epigenetic abnormalities can be used as therapeutic targets and prognostic biomarkers for OC patients. To address this hypothesis, the PhD project will focus on the four following specific aims: 1. Identify cancer driver genes associated with late-stage (III-IV) ovarian cancer- specific survival and different OC histotypes. 2. Revisit data used in the project: sens2017614 to identify cancer genes associated with early-stage (I-II) ovarian cancer specific survival 2. Determine whether altered protein levels of the candidate biomarkers are associated with ovarian cancer-specific survival and different ovarian cancer histotypes. 4. Define the tumorigenic properties and therapeutic potential of candidate biomarkers. In the long term, the proposed project may provide healthcare professionals with new biomarkers that can be used in conjunction with established clinical variables to better sub-classify OC by histotype, predict patient clinical outcome more efficiently, and thereby improve the decision-making process ovarian cancer treatment. This could be done by first including the protein expression levels of the identified biomarkers in the final pathology report, which in turn could be used by oncologists to determine a suitable mode of treatment.