Chemotherapy resistance is a major issue in cancer treatment, leading to high mortality rates. Solid organ cancers, which make up over 90% of all adult cancers, are particularly difficult to manage due to challenges in obtaining and analyzing necessary data. This project focuses on high-grade serous carcinoma (HGSC), which is the most common and lethal type of epithelial ovarian cancer. We will use liquid biopsies, specifically circulating tumor DNA (ctDNA), to develop cost-effective methods for predicting treatment outcomes and suggesting effective treatments. The key output of the project is a software that enable treatment failure prediction and personalized medicine targets for a wide range of ctDNA technologies and cancer types.

2025

2026