Highlights d Endogenous glucocorticoid signaling shapes CD8 + T cell differentiation in tumors d The glucocorticoid receptor transactivates IL-10 and checkpoint receptor expression d Tumor monocyte-macrophage lineage cells produce glucocorticoid d Glucocorticoid signaling in CD8 + T cells reduces immune checkpoint blockade efficacy

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The recognition of the immune system as a key component of the tumor microenvironment (TME) led to promising therapeutics. Because such therapies benefit only subsets of patients, understanding the activities of immune cells in the TME is required. Eosinophils are an integral part of the TME especially in mucosal tumors. Nonetheless, their role in the TME and the environmental cues that direct their activities are largely unknown. We report that breast cancer lung metastases are characterized by resident and recruited eosinophils. Eosinophil recruitment to the metastatic sites in the lung was regulated by G protein–coupled receptor signaling but independent of CCR3. Functionally, eosinophils promoted lymphocyte-mediated antitumor immunity. Transcriptome and proteomic analyses identified the TME rather than intrinsic differences between eosinophil subsets as a key instructing factor directing antitumorigenic eosinophil activities. Specifically, TNFα/IFNγ–activated eosinophils facilitated CD4+ and CD8+ T-cell infiltration and promoted antitumor immunity. Collectively, we identify a mechanism by which the TME trains eosinophils to adopt antitumorigenic properties, which may lead to the development of eosinophil-targeted therapeutics. Significance: These findings demonstrate antitumor activities of eosinophils in the metastatic tumor microenvironment, suggesting that harnessing eosinophil activity may be a viable clinical strategy in patients with cancer.

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Type 1 interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and may facilitate induction of T cell exhaustion in chronic viral infection1,2. Here we show that IFN-I regulates co-inhibitory receptors expression on human T cells, inducing PD-1/TIM-3/LAG-3 while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses enabled the construction of dynamic transcriptional regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors on human primary T cells revealed both canonical and non-canonical IFN-I transcriptional regulators, and identified unique regulators that control expression of co-inhibitory receptors. To provide direct in vivo evidence for the role of IFN-I on co-inhibitory receptors, we then performed single cell RNA-sequencing in subjects infected with SARS-CoV-2, where viral load was strongly associated with T cell IFN-I signatures. We found that the dynamic IFN-I response in vitro closely mirrored T cell features with acute IFN-I linked viral infection, with high LAG3 and decreased TIGIT expression. Finally, our gene regulatory network identified SP140 as a key regulator for differential LAG3 and TIGIT expression. The construction of co-inhibitory regulatory networks induced by IFN-I with identification of unique transcription factors controlling their expression may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

MotivationCell-cell crosstalk involves simultaneous interactions of multiple receptors and ligands, followed by downstream signaling cascades working through receptors converging at dominant transcription factors which then integrate and propagate multiple signals into a cellular response. Single-cell RNAseq of multiple cell subsets isolated from a defined microenvironment provides us with a unique opportunity to learn about such interactions reflected in their gene expression levels.ResultsWe developed the FLOW framework with the intention of mapping the potential ligand-receptor interactions between different cell subsets based on a maximum flow computation in a network of protein-protein interactions (PPIs). The maximum flow approach further allows characterizing the intracellular downstream signal transduction from differentially expressed receptors towards dominant transcription factors. This, therefore enables the association between a set of receptors and their downstream activated pathways. Importantly, we were able to identify key transcription factors toward which the convergence of signaling of multiple receptors occurs. These identified factors have a unique role in the integration and propagation of signaling following cell-cell interactions.