Speaker
Description
Systemic therapies have revolutionised our ability to treat metastatic cancer. However, improvements are all too often temporary due to compounding toxicity and the emergence of drug-resistance. Most drugs are given according to a one-size-fits all approach and only changed upon toxicity or progression. In this talk, I will present work across two different spatial-temporal scales in which we ask: can we improve outcomes by personalising treatment scheduling?
In the first part, I will discuss integration of in vitro experiments and mathematical modelling to study the impact of drug scheduling on cancer evolution. By treating fluorescent co-cultures of sensitive and resistant cells with four different treatment schedules (Continuous Therapy, Intermittent Therapy, Low-Dose Continuous Therapy), we show that intermittent scheduling can slow drug resistance and that cell plasticity plays an important role in shaping the treatment dynamics. In the second part, I will present recent work in which we are asking what we can learn about resistance dynamics in patients from routinely collected tumour burden data. I will present a Bayesian model selection framework with which we dissect the response dynamics of metastatic colorectal cancer patients by comparing different evolutionary hypotheses. Optimising drug scheduling is a key aim of mathematical oncology, and I hope to convince you of the opportunities – and importance - of studying this problem at multiple scales.
