A systems biology approach to identify effective drug combinations for cancer

Cancer therapy and oncology has entered a new exciting era of targeted therapy and personalised patient treatment, but resistance and tumour heterogeneity represent a significant hurdle for realizing the clinical impact of these discoveries. Overcoming this hurdle requires an ability to quantitatively describe heterogeneous tumour cell populations and their dynamic response to treatment over time. Computational modelling in close conjunction with experimental validation represents an attractive avenue towards evaluating which drugs, combinations, and schedules are best for a given patient. While it is unethical and too time-consuming to test all possible drug combinations and dosing schedules in pre-clinical or clinical studies (and therefore only a limited clinical experimentation can be performed), computational modelling can, in principle, be used to narrow the set of possibilities to identify the combinations and schedules that maximize patient survival. We have demonstrated this concept targeting the EGFR signalling network in triple-negative breast cancer, the most aggressive subtype of breast cancer. This project will further develop this highly integrative strategy for other pathways and/or other cancer types. Students will work in a highly stimulating and interdisciplinary research environment consisting of both computational and experimental scientists. Students with either excellent computational (physics, maths, engineering, etc.) or experimental background (or both) are encouraged to apply.