Active Cancer Cells, Biochemistry & Physiology

Understanding and controlling the spatial organisation of cancer

Summary

Original abstract (not yet simplified)

The spatial of organisation of tumours is the cumulative result of a myriad of cell-cell and cell-matrix interactions. These interactions determine the shape and relative positions of cells and the deposition and structure of the extracellular matrix (ECM), which feeds back to guide cell migration. The spatial patterns of tumours indirectly encode information about the likely future course of the...

View original technical description
The spatial of organisation of tumours is the cumulative result of a myriad of cell-cell and cell-matrix interactions. These interactions determine the shape and relative positions of cells and the deposition and structure of the extracellular matrix (ECM), which feeds back to guide cell migration. The spatial patterns of tumours indirectly encode information about the likely future course of the disease, which is interpreted by pathologists to provide patient prognosis. To understand and control the spatial organisation of tumours requires not just knowledge of cell position, but of the underlying structural matrix. Furthermore, analysis of fixed tissues fails to capture information about cell movement or the relative sequence and kinetics of events. We will place both structural information about the ECM and dynamic analysis obtained through longitudinal intravital imaging and the centre of our work. We will focus on sequentially describing, understanding, and then re-configuring the spatial organisation of the tumour microenvironment. We will develop spatial metrics to analyse stromal architecture and link spatial features to disease outcomes in patient cohorts and therapy responses in intravital imaging studies. Reductionist co-culture assays and microfabricated tools will be used to study the mechanisms of cell-cell and cell-matrix crosstalk that generate spatial patterns. This analysis will be coupled to an agent-based computational model, parameterised using our experimental models, to explore both emergent pattern formation and possible mechanisms of interconversion between spatial patterns. Both experimental and modelling approaches will be used to identify effective mechanisms to control spatial patterns in breast and skin cancer models. Finally, interventional approaches will be adopted to re-organise the spatial architecture of the tumour stroma into patterns that favour effective therapy and tumour elimination.

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Original classification

H2020

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