Hypothesis: The cell composition and proximity of the microenvironment surrounding tumour epithelial cells influences the magnitude of their cell cycle activity levels Background: Mounting evidence has shown the prognostic capacity of immune-related cells such as Tumour Infiltrating Lymphocytes (TIL), Tumour Associated Macrophage (TAMs) and T-cells in addition to the tumour microenvironment (TME) itself in breast cancer. However, no analysis to date has systematically determined how the composition and proximity of the cells of the tumour microenvironment factors influence one of the strongest prognostic factors in breast cancer – cellular proliferation and by extension, the cell cycle. Methods: In this project the student will first apply the METI algorithm to spatial transcriptomic (ST) data from 28 tissue sections, representing 14 primary Triple Negative Breast Cancer (TNBC) tumors. The METI Python package provides comprehensive understanding of the spatial cellular composition and organization within the tissue by the cancer cell domain and tumour microenvironment (B cells, T cells, Macrophages, Neutrophils, Plasma cells, Fibroblasts, and Dendritic cells) identification. Furthermore, our CCS signature will be applied to the same data. An example showing the presence of TCD4 (yellow) and TCD8 (green) on tumour cells (red) and proliferating cancer cells (blue) in a TNBC tissue section is shown in Figure X. Next, the student will apply CRAWDAD to the results from METI to characterise tumour-tumour microenvironment spatial relationships to determine determine (i) how the composition of the TME influences the magnitude of cell cycle activity and (ii) what role the proximity of the TME plays in this influence, e.g. we would anticipate that TME cells need to be in close proximity to tumour epithelial cells in order to influence their cell cycle activity levels.