TGF-β inhibition can overcome cancer primary resistance to PD-1 blockade: A mathematical model

Immune checkpoint inhibitors have demonstrated, over the recent years, impressive clinical response in cancer patients, but some patients do not respond at all to checkpoint blockade, exhibiting primary resistance. Primary resistance to PD-1 blockade is reported to occur under conditions of immunosuppressive tumor environment, a condition caused by myeloid derived suppressor cells (MDSCs), and by T cells exclusion, due to increased level of T regulatory cells (Tregs). Since TGF-beta activates Tregs, TGF-beta inhibitor may overcome primary resistance to anti-PD-1. Indeed, recent mice experiments show that combining anti-PD-1 with anti-TGF-beta yields significant therapeutic improvements compared to anti-TGF-beta alone. The present paper introduces two cancer-specific parameters and, correspondingly, develops a mathematical model which explains how primary resistance to PD-1 blockade occurs, in terms of the two cancer-specific parameters, and how, in combination with anti-TGF-beta, anti-PD-1 provides significant benefits. The model is represented by a system of partial differential equations and the simulations are in agreement with the recent mice experiments. In some cancer patients, treatment with anti-PD-1 results in rapid progression of the disease, known as hyperprogression disease (HPD). The mathematical model can also explain how this situation arises, and it predicts that HPD may be reversed by combining anti-TGF-beta to anti-PD-1. The model is used to demonstrate how the two cancer-specific parameters may serve as biomarkers in predicting the efficacy of combination therapy with PD-1 and TGF-beta inhibitors.

Automated summary

Immune checkpoint inhibitors have shown impressive clinical responses in cancer patients, but some exhibit primary resistance, which could potentially be overcome by inhibiting TGF-beta. Combining anti-PD-1 with anti-TGF-beta has shown significant therapeutic improvements in recent experiments, with potential for predicting treatment efficacy using cancer-specific parameters as biomarkers.