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Digital Cancer Twins: From Mechanistic Insights to Therapeutic Applications

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If you have a question about this talk, please contact Kate Davenport.

Cancer is a complex systemic disease driven by genetic and epigenetic aberrations that impact a multitude of signalling pathways operating in different cell types. The dynamic evolving nature of the disease leads to tumour heterogeneity and an inevitable resistance to treatment, which poses considerable challenges for the design of therapeutic strategies to combat cancer. Digital twins for cancer tumours are emerging as a transformative tool in oncology to enable a more personalised and dynamic approach to cancer treatment. In this talk, I will showcase a growing library of mechanistic, data-driven computational models, focused on the signalling pathways within tumour cells and their microenvironment in various cancer types (e.g., triple-negative breast cancer, non-small cell lung cancer, melanoma and glioblastoma). These computational models are mechanistically interpretable, enabling us to better understand and anticipate inevitable resistance mechanisms and predict patient-specific treatment plans that counteract the forces of clonal selection and phenotypic plasticity that ultimately lead to treatment relapse. I will demonstrate how these models are initialised with genetic data to create digital twins for individual patient tumours and predict optimised treatment strategies and outcomes for their evolving disease. We anticipate our cancer digital twins to be a starting point for a powerful and clinically deployable software application to optimise treatment plans and outcomes for hard-to-treat cancers.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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