Mathematical and Mouse Models Identify Regulatory T Cell Influx as A Key Determinant of Acquired Resistance to PD-1 Immunotherapy.

The immune system can eradicate cancer, but various immunosuppressive mechanisms active within a tumor curb this beneficial response. However, unraveling the effects of multimodal interactions between tumor and immune cells and their contributions to tumor control using an experimental approach alone is time- and resource-intensive. To identify the critical immunological features associated with tumor control and escape, we built a mechanistic, structurally identifiable mathematical model of the interactions between CD8+ T cells, regulatory T cells (Tregs), dendritic cells, and tumor cells deeply rooted in current biological concepts. The model captured Treg accrual occurring after checkpoint blockade immunotherapy. After successfully fitting the mathematical model to experimental data from an immunogenic melanoma mouse model with acquired resistance to PD-1 immunotherapy and ensuring that the model was practically identifiable, hundreds of parameter sets were generated, each of which fit the data well and represented a unique 'virtual mouse' to capture variability across individuals. The model indicated that the initial tumor and immune conditions instruct cancer control or progression and that optimal initial ratios of immune cells exist that result in improved tumor control. The model further predicted that the Treg influx into the tumor is a key determinant of resistance to PD-1 immunotherapy. Experimental studies validated all these predictions from the model. Overall, this integrated approach of modeling and experimental validation identified crucial determinants of resistance to PD-1 immunotherapy and can be used to guide the development of more effective therapeutic strategies.