The ex-vivo human experiments indicated that Th1-related changes to the tumor micro-environment have the greatest impact on patient-to-patient heterogeneity resulting from PD-1 blockade27. Guided by these results and relevant interactions in the literature, we constructed a multi-scale systems biology (SB) model comprised of a tumor cell population along with five key interacting T-cell populations and four key cytokines involved in T-helper cell differentiation and activation, see Fig. 1. Specifically, the model consists of 17 coupled ordinary differential equations (ODEs), which describe the time evolution of naive CD4+ helper T cells (Th0), type 1 helper T cells (Th1), type 2 helper T cells (Th2), naive CD8+ T cells (TN8), cytotoxic CD8+ T cells (Tc), a cancer cell population, cytokine levels, PD-1 and PD-L1 levels, and drug concentration. The full details of the model, including all the molecular interactions and the corresponding system of coupled ODEs describing the time evolution of each chemical species, are presented in Supplementary Section A. In brief, the main interactions comprising the model are as follows. Cell proliferation and natural death are assumed for all cell populations in the model. Additionally, the CD4+ Th0 cells can differentiate into either CD4+ Th1 cells or CD4+ Th2 cells. The first differentiation process is mediated by the cytokines IL-4 and IL-6. IL-12 and IFNγ mediate the second differentiation process. Naive CD8+ cells differentiate into CD8+ Tc cells in the presence of CD4+ Th1 cells, and the proliferation rate of CD8+ Tc cells is increased by IL-12 expression. CD8+ Tc cells kill the cancer cells. All activated T-cells (CD4+ Th1, CD4+ Th2, CD8+ Tc) express PD-1 and PD-L1. Cancer cells also express PD-L1, which is mediated by IFNγ expression. PD-1 and PD-L1 form a protein complex which inhibits all T-cell differentiation processes. The production of cytokines depends on the T-cell populations and cancer cell population, and there are several feedback loops which affect the production rates. The resulting pathway, depicted in Supplementary Fig. 1, exhibits several potential mechanisms of intrinsic drug resistance.

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