###Mathematical modelling of tumour microenvironments and CAR T-cell therapy
Project ID: 2228bd1140 (You will need this ID for your application)
Research Theme: Mathematical Sciences
UCL Lead department: Mathematics
Lead Supervisor: Karen Page
Project Summary:
In CAR T-cell therapy, a cancer patient’s own T-cells (immune cells) are engineered in the laboratory to express tumour-specific or tumour-associated antigens. The cells are then reintroduced into the body to boost the immune response to the tumour.
CAR-T cell therapies have been successful in treating blood cancers, but treating solid tumours is more challenging. The reasons include the difficulty T-cells have in infiltrating such tumours, which have a microenvironment, including immunosuppressive cells. Secondly, solid tumours frequently lack tumour-specific antigens, hence cross-reactivity with normal tissues is a major obstacle. Thirdly, solid tumours often have heterogeneous antigens; this diversity aids the evolution of antigen escape variants.
In this project, we will build and analyse spatial mathematical models of tumours and their microenvironments, focusing on dynamics of CAR T-cells - access to tumour, activation, proliferation, exhaustion, and death - and tumour cell killing. Models will draw on data from chip cytometry experiments and tumour-on-a-chip, showing the spatial distribution of cell types in tumour sections (https://cancergrandchallenges.org/teams/nextgen). We will employ hybrid mathematical modelling approaches, with partial differential equations describing concentrations of cytokines (biomolecules that regulate cell populations) and cells in large regions of low diversity. Cells at low densities will be modelled individually.
We will study evolution of antigen escape variants with the aim of suggesting optimal strategies for CAR T-cell therapy. How broad should the CAR-T response be? CAR T-cells can prevent escape by targeting multiple antigens simultaneously. How should this be balanced against increased risk of toxicity? We will use stochastic processes and ordinary differential equations to study probabilities/ timescales of escape.
Applicants should have a master’s degree mathematics (or similar), with a strong background in Mathematical Biology, experience with PDEs and numerical simulation. The successful candidate will be trained in methods of mathematical modelling.