Distinct mechanisms govern populations of myeloid-derived suppressor cells in chronic viral infection and cancer.

Myeloid-derived suppressor cells (MDSCs) are major negative regulators of immune responses in cancer and chronic infections. It remains unclear if regulation of MDSC activity in different conditions is controlled by similar mechanisms. We compared MDSCs in mice with cancer and lymphocytic choriomeningitis virus (LCMV) infection. Chronic LCMV infection caused the development of monocytic MDSCs (M-MDSCs) but did not induce polymorphonuclear MDSCs (PMN-MDSCs). In contrast, both MDSC populations were present in cancer models. An acquisition of immune-suppressive activity by PMN-MDSCs in cancer was controlled by IRE1α and ATF6 pathways of the endoplasmic reticulum (ER) stress response. Abrogation of PMN-MDSC activity by blockade of the ER stress response resulted in an increase in tumor-specific immune response and reduced tumor progression. In contrast, the ER stress response was dispensable for suppressive activity of M-MDSCs in cancer and LCMV infection. Acquisition of immune-suppressive activity by M-MDSCs in spleens was mediated by IFN-γ signaling. However, it was dispensable for suppressive activity of M-MDSCs in tumor tissues. Suppressive activity of M-MDSCs in tumors was retained due to the effect of IL-6 present at high concentrations in the tumor site. These results demonstrate disease- and population-specific mechanisms of MDSC accumulation and the need for targeting different pathways to achieve inactivation of these cells.

Distinct Biomarker Profiles and TCR Sequence Diversity Characterize the Response to PD-L1 Blockade in a Mouse Melanoma Model.

Only a subset of patients responds to immune checkpoint blockade (ICB) in melanoma. A preclinical model recapitulating the clinical activity of ICB would provide a valuable platform for mechanistic studies. We used melanoma tumors arising from an Hgftg;Cdk4R24C/R24C genetically engineered mouse (GEM) model to evaluate the efficacy of an anti-mouse PD-L1 antibody similar to the anti-human PD-L1 antibodies durvalumab and atezolizumab. Consistent with clinical observations for ICB in melanoma, anti-PD-L1 treatment elicited complete and durable response in a subset of melanoma-bearing mice. We also observed tumor growth delay or regression followed by recurrence. For early treatment assessment, we analyzed gene expression profiles, T-cell infiltration, and T-cell receptor (TCR) signatures in regressing tumors compared with tumors exhibiting no response to anti-PD-L1 treatment. We found that CD8+ T-cell tumor infiltration corresponded to response to treatment, and that anti-PD-L1 gene signature response indicated an increase in antigen processing and presentation, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity. TCR sequence data suggest that an anti-PD-L1-mediated melanoma regression response requires not only an expansion of the TCR repertoire that is unique to individual mice, but also tumor access to the appropriate TCRs. Thus, this melanoma model recapitulated the variable response to ICB observed in patients and exhibited biomarkers that differentiate between early response and resistance to treatment, providing a valuable platform for prediction of successful immunotherapy. IMPLICATIONS: Our melanoma model recapitulates the variable response to anti-PD-L1 observed in patients and exhibits biomarkers that characterize early antibody response, including expansion of the TCR repertoire.

Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome.

Deeper understanding of T cell biology is crucial for the development of new therapeutics. Human naïve T cells have low RNA content and their numbers can be limiting; therefore we set out to determine the parameters for robust ultra-low input RNA sequencing. We performed transcriptome profiling at different cell inputs and compared three protocols: Switching Mechanism at 5' End of RNA Template technology (SMART) with two different library preparation methods (Nextera and Clontech), and AmpliSeq technology. As the cell input decreased the number of detected coding genes decreased with SMART, while stayed constant with AmpliSeq. However, SMART enables detection of non-coding genes, which is not feasible for AmpliSeq. The detection is dependent on gene abundance, but not transcript length. The consistency between technical replicates and cell inputs was comparable across methods above 1 K but highly variable at 100 cell input. Sensitivity of detection for differentially expressed genes decreased dramatically with decreased cell inputs in all protocols, support that additional approaches, such as pathway enrichment, are important for data interpretation at ultra-low input. Finally, T cell activation signature was detected at 1 K cell input and above in all protocols, with AmpliSeq showing better detection at 100 cells.