Speaker
Description
CD8⁺ T cells play a central role in anti-tumour immunity by eliminating cancer cells. However, sustained exposure to tumour antigens can drive them into a differentiation pathway culminating in a dysfunctional exhausted state, limiting immune control of cancer. This process emerges from collective inter- and intracellular interactions within complex immune signalling networks. Revealing the differentiation trajectories and the mechanisms governing exhaustion therefore requires quantitative frameworks that capture the dynamical processes underlying immune cell fate decisions.
First, we develop a mathematical framework to analyse CD8⁺ T cell decision circuits in cancer. We analyse network motifs governing CD8⁺ T cell population dynamics, capturing proliferation, differentiation, and cytokine-mediated feedback. State transitions are described using response-time modelling, where waiting-time distributions follow gamma distribution, accounting for multi-step intracellular processes while maintaining analytical tractability. Complementary to this approach, we analyse high-dimensional flow cytometry data from a murine melanoma adoptive T cell therapy experiment. Using unsupervised high-dimensional clustering, we identify phenotypic CD8⁺ T cell subsets across organs and time points. Based on these subsets, we systematically generate candidate differentiation network topologies and fit them to the data. Model selection identifies the topology that best explains the observed dynamics.
