Fig. 1: In silico late-stage immunotherapy trials and their applications.
A Cellular interactions between a tumor and the immune system as implemented in ODE model M1 (Methods). This model describes immunogenic tumor growth leading to a T cell response originating from lymph nodes. Disease courses in patients can be steered by immunotherapy, chemotherapy, or a combination of both. Parameters: α=naive T cell priming rate, δ=effector T cell death rate, ξ=effector T cell killing rate, ρ=tumor growth rate, ρs=effector T cell proliferation rate, and ms=effector T cell migration rate. B After implementation, we used survival data from clinical trials to fit some of the model parameters. C Patients received either no treatment (placebo), chemotherapy, immunotherapy, or both. Disease trajectories based on tumor-immune dynamics were simulated for each patient, resulting in individual survival outcomes. D Subsequently, cohorts of patients were constructed based on the fitted parameters to simulate actual immunotherapy trials. E Applications of such trials include predicting possible survival outcomes of trials, estimating sample sizes needed for a range of scenarios, and investigating endpoints, randomization ratios, and the timing of interim analyses.
