Abstract

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 and evolving nature of the disease leads to tumour heterogeneity and treatment resistance, posing significant challenges for designing effective therapeutic strategies. Digital twins of cancer tumours are emerging as a transformative tool in oncology to enable a more personalised, adaptive approach to treatment. In this talk, I will present a growing library of mechanistic, data-driven computational models that capture key signalling pathways within tumour cells and their microenvironment across several cancer types, including triple-negative breast cancer, non-small cell lung cancer, melanoma and glioblastoma. These interpretable models allow us to uncover and anticipate emergent resistance mechanisms, and to design patient-specific treatment strategies that counteract the forces of clonal selection driving treatment relapse. By integrating mechanistic insights with clinical data, we aim to optimise therapeutic interventions for patients with hard-to-treat cancers and advance the clinical utility of digital twins in precision oncology.