Coengineering specificity, safety, and function into T cells for cancer immunotherapy

Distinct host preconditioning regimens differentially impact the antitumor potency of adoptively transferred Th17 cells

Background

Mechanisms by which distinct methods of host preconditioning impact the efficacy of adoptively transferred antitumor T helper cells is unknown.

Methods

CD4 + T cells with a transgenic TCR that recognize TRP-1 melanoma antigen were polarized to the T helper 17 (Th17) phenotype and then transferred into melanoma-bearing mice preconditioned with either total body irradiation or chemotherapy.

Results

We found that preconditioning mice with a non-myeloablative dose of total body irradiation (TBI of 5 Gy) was more effective than using an equivalently dosed non-myeloablative chemotherapy (CTX at 200 mg/kg) at augmenting therapeutic activity of anti-tumor TRP-1 Th17 cells. Anti-tumor Th17 cells engrafted better following preconditioning with TBI and regressed large established melanoma in all animals. Conversely, only half of mice survived long-term when preconditioned with CTX and infused with anti-melanoma Th17 cells. IL-17 and IFN-g produced by the infused Th17 cells, were detected in animals given either TBI or CTX preconditioning. Interestingly, inflammatory cytokines (G-CSF, IL-6, MCP-1, IL-5, and KC) were significantly elevated in the serum of mice preconditioned with TBI versus CTX after Th17 therapy.

Conclusions

Our results indicate, for the first time, that the antitumor response, persistence, and cytokine profiles resulting from Th17 therapy are impacted by the specific regimen of host preconditioning. This work is important for understanding mechanisms that promote long-lived responses by ACT, particularly as CD4 + based T cell therapies are now emerging in the clinic.

Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.