Abstract
Combination therapy with immune checkpoint blockade and ionizing irradiation therapy (IR) generates a synergistic effect to inhibit tumor growth better than either therapy does alone. We modeled the tumor-immune interactions occurring during combined IT and IR based on the published data from Deng et al. The mathematical model considered programmed cell death protein 1 and programmed death ligand 1, to quantify data fitting and global sensitivity of critical parameters. Fitting of data from control, IR and IT samples was conducted to verify the synergistic effect of a combination therapy consisting of IR and IT. Our approach using the model showed that an increase in the expression level of PD-1 and PD-L1 was proportional to tumor growth before therapy, but not after initiating therapy. The high expression level of PD-L1 in T cells may inhibit IT efficacy. After combination therapy begins, the tumor size was also influenced by the ratio of PD-1 to PD-L1. These results highlight that the ratio of PD-1 to PD-L1 in T cells could be considered in combination therapy.
Highlights
Immunotherapy using immune checkpoint blockade (IT) is an anti-cancer therapy that recovers immunity by suppressing various tumor mechanisms that evade the immune response [1,2,3,4]
We proposed a mathematical model for tumor-immune interactions using anti-programmed death ligand 1 (PD-L1) and irradiation therapy (IR) in combination therapy
The model captured the synergistic effect of the combination therapy consisting of IR and IT, as shown in Figure 2 (IR + IT)
Summary
Immunotherapy using immune checkpoint blockade (IT) is an anti-cancer therapy that recovers immunity by suppressing various tumor mechanisms that evade the immune response [1,2,3,4]. There are mathematical models of immune-tumor interactions to demonstrate the data of Deng et al [14] These studies [15,16] revealed the synergistic effect of anti-PD-L1 and IR combination therapy in mice. The underlying assumption considered that IR affects the death rate of both tumors and T cells Based on this assumption, they formulated a mathematical model for the interaction between T cells and tumors. Due to excessive suppression of IT and the bias of control growth, the synergistic effect of combined IR and IT was not accurately measured. This model was able to explain how IT and IR affected tumors and T cells
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