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Treatment in advanced cancers often provides only temporary improvements due to the emergence of drug-resistant tumour subpopulations. Standard-of-care regimens seek to maximise cell kill in order to achieve a cure, but thereby inadvertently also free resistant cells from the intra-tumoral competition which usually constrains their growth. An emerging alternative strategy, called adaptive therapy (AT), aims to tackle this problem by prioritising tumour control over cure. The idea is to leverage intra-tumoral competition to slow, or even avoid, the expansion of resistance. In this thesis we present an integration of mathematical modelling, clinical data analysis, and biological experiments in order to investigate: i) the factors which determine whether a patient will benefit from AT, and ii) how to best adjust treatment to control resistance. We begin by studying a simple two-population model, which we formulate first as an ordinary differential equation (ODE), and subsequently as an agent-based, model. We dissect the impact of various model parameters on the benefit of AT, and identify cell turnover as an important, but previously overlooked, factor. Furthermore, we find that the tumour’s spatial organisation reflects, as well as modulates, intra-tumoral competition, and we discuss implications for intermittent androgen deprivation treatment in prostate cancer. In the second part of this thesis, we investigate whether adaptive scheduling of poly-adenosine di-phosphate ribose polymerase inhibitors (PARPis) can improve the treatment of ovarian cancer. We develop a framework which uses an ensemble of six ODE models to dynamically guide AT decision-making. In collaboration with an experimentalist, we test our framework in an in vitro spheroid model, and find that it compares favourably to other adaptive dosing algorithms. However, this work also shows that high-dose, continuous treatment achieves the best overall outcome. To conclude we therefore develop an ODE model of PARPi treatment, and discuss implications for PARPi scheduling. Overall, this thesis provides novel insights into the eco-evolutionary underpinnings of AT, and highlights some of the challenges involved in translating AT to a new cancer type. |
