An Integrative Multi-Scale Mathematical Model Of Tumor–Immune Ecosystem Dynamics For Precision Therapeutic Response Prediction

Abstract

The modulation of tumor progression is characterized by interactions between a variety of tumor cells, immune populations, molecular signaling pathways, and therapeutic interventions, making prediction of therapeutic responses in cancer a challenge in the context of precision oncology. The current mathematical models include only one scale of biology and are poor at capturing the dynamics of the tumor–immune ecosystem. This study introduces an integrative multi-scale mathematical model which couples tumor growth kinetic, dynamics of immune cell populations, cytokine signaling, angiogenesis and mechanisms of therapy response within a system of nonlinear ordinary and partial differential equations. To assess the disease progression during chemotherapy, immunotherapy, and combination therapy, numerical simulations were conducted utilizing biological parameters from patients and treating the patients for 365 days. The proposed model showed an accuracy of 81.6% over conventional single-scale models by 18.7% in predicting the therapeutic response, 68.4% reduction in tumor volume, and 2.9-fold increase in infiltration of cytotoxic T-cells into the tumor.Based on the proposed model, the accuracy of therapeutic response prediction was 81.6%, which was 18.7% higher than the conventional single-scale models, and the increase in the number of cytotoxic T-cells infiltrating the tumor was 2.9-fold. The results show that the framework is a powerful computational tool for forecasting personalised therapeutic response and precision treatment planning in oncology.