The goal of the Systems Biology of Drug Resistance in Cancer research group (PI: Prof. ) is to understand how tumors develop treatment resistance and identify strategies to overcome it. We combine computational modeling with multi-modal molecular, clinical, and imaging data from cancer patients to uncover resistance-driving processes as they occur in clinical reality. These hypotheses are systematically validated using functional experiments in cell lines and patient-derived organoids, leading to mechanistic insights and therapeutic opportunities.
Our primary focus is ovarian high-grade serous carcinoma (HGSC), where we apply systems biology and data-driven inference to reconstruct tumor evolutionary dynamics, define treatment response states, and identify actionable vulnerabilities in chemotherapy-resistant disease (, ). Our research is based on the longitudinal, multi-region, observational and , which has produced one of the largest and most comprehensive real-world, patient-derived datasets for HGSC