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Oncolytic viruses are currently tested as a novel platform for cancer therapy. These viruses preferentially replicate in and kill malignant cells. Due to their microbial origin, treatment with oncolytic viruses naturally results in anti-viral responses and general immune activation. Consequently, the oncolytic virus treatment also induces anti-viral T cells. Since these can constitute the dominant activated T cell pool, monitoring of the anti-viral T cell response may aid in better understanding of the immune responses post oncolytic virotherapy. This study aimed to identify the anti-viral T cells raised by VSV-GP virotherapy in C57BL/6J mice, one of the most widely used models for preclinical studies. VSV-GP is a novel oncolytic agent that recently entered a clinical phase I study. To identify the VSV-GP epitopes to which mouse anti-viral T cells react, we used a multilevel adapted bioinformatics viral epitope prediction approach based on the tools netMHCpan, MHCflurry and netMHCstabPan, which are commonly used in neoepitope identification. Predicted viral epitopes were ranked based on consensus binding strength categories, predicted stability, and dissimilarity to the mouse proteome. The top ranked epitopes were selected and included in the peptide candidate matrix in order to use a matrix deconvolution approach. Using ELISpot, we showed which viral epitopes presented on C57BL/6J mouse MHC-I alleles H2-Db and H2-Kb trigger IFN-γ secretion due to T cell activation. Furthermore, we validated these findings using an intracellular cytokine staining. Collectively, identification of the VSV-GP T cell epitopes enables monitoring of the full range of anti-viral T cell responses upon VSV-GP virotherapy in future studies with preclinical mouse models to more comprehensively delineate anti-viral from anti-tumor T cell responses. These findings also support the development of novel VSV-GP variants expressing immunomodulatory transgenes and can improve the assessment of anti-viral immunity in preclinical models.

The tumor microenvironment (TME) is a complex structure comprised of tumor, immune and stromal cells, vasculature, and extracellular matrix (ECM). During tumor development, ECM homeostasis is dysregulated. Collagen remodeling by matrix metalloproteinases (MMPs) generates specific collagen fragments, that can be detected in the circulation of cancer patients and correlate with poor disease outcome. Leukocyte-Associated Immunoglobulin-like Receptor-1 (LAIR-1) is an inhibitory collagen receptor expressed on immune cells in the TME and in the circulation. We hypothesized that in addition to ECM collagen, collagen fragments produced in cancer can mediate T cell immunosuppression through LAIR-1. Our analyses of TCGA datasets show that cancer patients with high tumor mRNA expression of MMPs, collagen I and LAIR-1 have worse overall survival. We show that in vitro generated MMP1 or MMP9 collagen I fragments bind to and trigger LAIR-1. Importantly, LAIR-1 triggering by collagen I fragments inhibits CD3 signaling and IFN-γ secretion in a T cell line. LAIR-2 is a soluble homologue of LAIR-1 with higher affinity for collagen and thereby acts as a decoy receptor. Fc fusion proteins of LAIR-2 have potential as cancer immunotherapeutic agents and are currently being tested in clinical trials. We demonstrate that collagen fragment-induced inhibition of T cell function could be reversed by LAIR-2 fusion proteins. Overall, we show that collagen fragments produced in cancer can mediate T cell suppression through LAIR-1, potentially contributing to systemic immune suppression. Blocking the interaction of LAIR-1 with collagen fragments could be an added benefit of LAIR-1-directed immunotherapy.

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

Collagens are a primary component of the extracellular matrix and are functional ligands for the inhibitory immune receptor leukocyte-associated immunoglobulin-like receptor (LAIR)-1. LAIR-2 is a secreted protein that can act as a decoy receptor by binding collagen with higher affinity than LAIR-1. We propose that collagens promote immune evasion by interacting with LAIR-1 expressed on immune cells, and that LAIR-2 releases LAIR-1-mediated immune suppression. Analysis of public human datasets shows that collagens, LAIR-1 and LAIR-2 have unique and overlapping associations with survival in certain tumors. We designed a dimeric LAIR-2 with a functional IgG1 Fc tail, NC410, and showed that NC410 increases human T cell expansion and effector function in vivo in a mouse xenogeneic-graft versus-host disease model. In humanized mouse tumor models, NC410 reduces tumor growth that is dependent on T cells. Immunohistochemical analysis of human tumors shows that NC410 binds to collagen-rich areas where LAIR-1+ immune cells are localized. Our findings show that NC410 might be a novel strategy for cancer immunotherapy for immune-excluded tumors.