The impact of ecological competition and cooperation on cancer adaptive therapy

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This project aims to use mathematical and computational modelling, combined with preclinical and clinical data, to develop a deeper understanding of the importance of spatial interactions ("ecology") in regulating therapeutic resistance in cancer. Specific goals are to: 1) understand how various ecological niches regulate cancer resistance, 2) build multiscale models that integrate cancer evolution and ecology, 3) develop multi-drug adaptive therapies that exploit space. Therapy failure is virtually assured in most cancers that have disseminated into metastatic disease. Trying to eliminate all cancer cells that may now inhabit multiple locations in the patient results in cancer cells acquiring therapeutic resistance via evolution. In fact, therapy resistance is probably the largest impediment to curing the disease, or maintaining it at a level that does not compromise the patient's quality of life. Recently, mathematical oncology and cancer biology have been tightly integrated to deliver novel clinical treatments for cancer that exploit evolution, rather than ignoring it. The key to success in such strategies is to anticipate adaptation and thus adjust therapeutic strategies before they become ineffective. Initial work will focus on the development of an agent-based model that considers resistance as a plastic state, modulated by the microenvironment, and treated with a combination of 2-3 drugs. Understanding the time scale of the emergence of resistance and the return to sensitivity for each drug, as well as potential synergies, will be critical in deciding how to sequence the drugs effectively to manage resistance. Preclinical data will be available to support this project in metastatic melanoma, as well as recurrent ovarian cancer. Ultimately, the goal is to develop a robust platform for informing clinical application on the best forms of adaptive therapy when there are multiple drugs, and multiple resistance strategies. Previous work has focused on the tumour response to a single drug, and mathematical approaches are currently insufficiently detailed to offer practical guidance to clinicians. In addition to this, work will also be conducted on a novel application of deep reinforcement learning to adaptive therapy, with a particular focus on developing and characterising models that independently learn adaptive strategies. Preliminary studies by the student and collaborators at the Moffitt Cancer Centre have suggested that deep learning algorithms trained on simple (non-spatial) tumour models are able to outperform human-developed strategies, and may be applied to more complex (spatial) models through transfer learning. This work is conducted in collaboration with industrial supervisors Alexander Anderson and Robert Gatenby based at the Moffitt Cancer Centre in Florida, who will advise on mathematical, modelling and clinical aspects of the research. This project falls within the EPSRC mathematical biology research area. It also contributes towards EPSRC's strategic priority to transform healthcare, with the potential to improve quality of life for patients with treatment resistant cancers.