Integrating Mathematical and Mouse Models Identifies T regulatory Cell Influx as a Key Determinant of Acquired Resistance to PD-1 Immunotherapy

Abstract

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 mathematical model of the interactions between CD8+ T cells, Tregs, DCs, and tumor cells deeply rooted in current biological concepts. A distinguishing feature of our model is that it captures Treg accrual occurring after checkpoint blockade immunotherapy. After successfully fitting the model to experimental data of a mouse model of immunogenic melanoma, we generated hundreds of parameter sets, each representing a unique virtual mouse, that fit the data equally as well to capture variability across individuals. Our model indicates that the tumor and immune states before therapy are a key limiting factor of the immune response. Increasing the initial number of tumor-killing CD8+ T cells alone doesnt always result in a better outcome; instead, the model implies that there exist optimal initial ratios of immune cells that will result in improved tumor control. The model further predicts that the Treg influx into the tumor is a key determinant of resistance to PD-1 immunotherapy. We validated this predictions experimentally. Overall, this integrated approach of modeling and experimental validation identified crucial determinants of resistance to immunotherapy and can be used to guide the development of more effective therapeutic strategies.