Multiscale Modelling of Cancer Cell Motility and Tumour Evolution
September 26 @ 09:00 – 10:00 CEST
Paul Bates, Cricks institute, UK
Multiscale Modelling of Cancer Cell Motility and Tumour Evolution
Since Inhibiting metastasis is as crucial as minimising tumour growth for efficient treatment of cancer, we constructed a multiscale model of cancer cell motility, with our primary focus being on amoeboid type cell motility of metastasising tumour cells in the extracellular matrix (ECM). Our model covered a wide parameter space and provided a deeper understanding of the conditions governing the motility of the cell at multiscale levels. Both the extracellular conditions (e.g. ECM density) and intrinsic cell properties (e.g. relative distribution of contractile and expanding regions of the cell membrane) were investigated. The aim was to identify the combination of intrinsic properties metastasising cells are more likely to use under different extracellular conditions. After extensive benchmarking of the computational model using in vitro data, we were able to predict cancer cell motility in vivo and under a number of different combinations of motility inhibitory, such as key kinase inhibitors. We have also developed multiscale computer models to investigate how tumours evolve based upon Intra-tumour genetic heterogeneity (ITH), which fuels ongoing clonal evolution. Despite clarified clonal structure and acknowledged role of ITH in disease progression within our recent tumour study, investigating clear-cell renal cell carcinomas (ccRCCs), there lacks characterisation of ongoing evolution that may inform future risk. By the combination of computer modelling and experimental analysis, we investigated spatial features of narrow-scale clone diversity (microdiversity) and parallel evolution on the impact of spatial tumour growth. We observed frequent microdiversity hotspots and parallel evolution near the tumour margin and uncovered a scaling relationship between the area spanned by a genomic alteration and the number of subclones within that area, in simulated tumours of 66 ccRCCs. Furthermore, in-silico time-course studies showed that different modes of spatial growth caused varying extents and tempos of subclonal diversification. Interestingly, evolutionary trajectories were often predictable early, suggesting that spatially resolved sampling combined with sequencing may enable identification of evolutionary potential in early-stage tumours.
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