Identifying cell type-specific proliferation signatures in spatial transcriptomics data and inferring interactions driving tumour growth

Cancer is a dangerous disease caused by mutations in the host's genome that makes the cells proliferateuncontrollably and disrupts bodily functions. The immune system tries to prevent this, but tumours have methods ofdisrupting the immune system's ability to combat the cancer. These immunosuppression events can for examplehappen when the immune system interacts with the tumour to recognise it or try and destroy it. The tumours can bychanging their displayed proteins on the cell surface avoid detection or by excreting proteins, they can neutralisedangerous immune cells. This happens within the tumour microenvironment (TME), the immediate surrounding of atumour where there is a plethora of different cells both aiding and suppressing the tumour. Some of these cells arenot cancer cells but can still aid the tumour due to how the tumour has influenced them. For example, throughangiogenesis, where new blood vessels are formed which feeds the tumour. The interactions in the TME can be used as a target for immunotherapy, a field of treatments which improves theimmune system's own ability at defending against cancer. Immunotherapy can for example help the immune systemby guiding immune cells towards the tumour. It is therefore essential to understand the complex system ofinteractions within the TME to be able to create new methods of immunotherapy and thus treat cancers moreefficiently. Concurrently new methods of mapping what happens in a tissue have been developed in recent years,namely spatial transcriptomics (ST). It allows for the retrieval of transcriptomic information of cells throughsequencing while still retaining spatial information. However, the ST methods which capture the wholetranscriptome of the cells and reveal the cell-to-cell interactions are not of single-cell resolution yet. They capturemultiple cells in each spot, creating a mix of cells in the sequencing. This mix of cells can be detangled, and theproportions of each cell type revealed through the process of deconvolution. Deconvolution works by mapping thesingle cell expression profile of different cell types onto the ST data and figuring out what proportions of expressioneach cell type produces the expression of the mix. This reveals the cellular composition of the microenvironment.But since the interactions in the TME depend on the cells current expression we need to deconvolute according tophenotype and not just cell type. In this project we were able to create a tool which automatically finds phenotypes in the single-cell data and usesthose phenotypes to deconvolute ST data. Phenotypes are found using dimensionality reduction methods todifferentiate cells according to their contribution to the variability in the data. The resulting deconvoluted data wasthen used as the foundation for describing the growth of a cancer as a system of phenotype proportions in the tumourmicroenvironment. From this system a mathematical model was created which predicts the growth and couldprovide insight into how the phenotypes interact. The tool created worked as intended and the model explains thegrowth of a tumour in the TME with not just cancer cells phenotypes but other cell phenotypes as well. However, nonew interaction could be discovered by the final model and no phenotype found could provide us with new insightsto the structure of the TME. But our analysis was able to identify structures we expect to see in a tumour, eventhough they might not be so obvious, so an improved version of our tools might be able to find even more detailsand perhaps new, more subtle interactions