Spatial modelling and quantification of T cell exhaustion in the tumour microenvironment of oesophageal cancer

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Background. Recently, immunotherapy of oesophageal adenocarcenoma (OAC) with immune checkpoint inhibitors has been shown to improve depth and durability of therapeutic responses for a significant minority of treated patients. Successful control and elimination of a cancer by the immune system requires the trafficking and infiltration of activated tumour antigen specific cytotoxic T lymphocytes into tumours, followed by recognition and killing of cancer cells, as described in the cancer-immunity cycle. This process can be disrupted at multiple points, leading to the breakdown and prevention of effective lymphocyte-driven destruction of cancer. Prominent among these is the influence of a suppressive microenvironment that can inhibit tumour-specific, activated, cytotoxic CD8+ T lymphocytes from expanding, migrating to the tumour, and killing cancer cells; and the impact of lymphocyte exhaustion. These immunological "checkpoints" prevent an anti-tumour response from developing and frustrate immunotherapeutic activation of the anti-tumour response. Thus, identifying the mechanisms underlying primary resistance to targeted immunotherapies is vital for progress. Multiple immunosuppressive networks have been described, including interactions with regulatory T cells (T-regs) and cancer assisted fibroblasts (CAFs). In OAC, bulk counts of T cell infiltration correlate poorly with clinical outcomes, and CAFs are strongly implicated in cytotoxic T cell suppression. Together, these observations suggest that spatial relations between different types of T cells and CAFs are important for understanding immunosuppression in OAC. The aim of this project is to analyse and quantify spatial domains involving immune cells from patients with OAC in order to develop new metrics, based on spatial statistics, that will improve diagnostics, prognostics, and immunotherapeutic strategies. Novelty of research methodology. We will develop a spatially-resolved, multiscale computational model describing the in vivo growth of OAC and its interactions with CAFs and different T cell subtypes, adapting an existing model. Subcellular variables will represent each T cell's level of exhaustion as a continuous value, which will determine its efficacy for killing OAC cells. In turn, T cell exhaustion will be altered through interactions with CAFs and T regs and immunotherapy. Simulation outputs will be described quantitatively using a suite of spatial statistical analysis tools. These tools will also be applied to mIHC images of OAC, generated by the Elliott lab, with existing panel optimised for CD8+ subsets, T-regs and CAFs. direct quantitative comparison will therefore be possible between biomedical images and ABM simulations. We will identify combinations of spatial statistics which distinguish between simulations generated via different parameters, and apply these to IHC samples to predict patient outcomes and responses to therapy. Proposed outcomes. The project will deliver a versatile multiscale computational model that simulates interactions between immune cell subsets and OAC, and that generates synthetic spatial data for comparison with mIHC images. The model will provide new mechanistic understanding of processes driving T cell exhaustion and immunosuppression within OAC and also serve as tool for identifying potential new immunotherapies. By comparing model outcomes with mIHC data, we will identify statistical descriptions of cell colocalization which act as imaging biomarkers and which can distinguish patients who would benefit from immunotherapeutic treatments from those who would not. Professor Tim Elliott's lab at the Nuffield Department of Medicine will be collaborating to provide Multiplex Immunohistological (Vectra) images of OAC. This project falls within the EPSRC Mathematical Biology research area.