Aim: To molecularly characterize the tumor microenvironment and evaluate immunologic parameters in canine glioma patients before and after treatment with oncolytic human IL-12-expressing herpes simplex virus (M032) and in treatment naïve canine gliomas. Methods: We assessed pet dogs with sporadically occurring gliomas enrolled in Stage 1 of a veterinary clinical trial that was designed to establish the safety of intratumoral oncoviral therapy with M032, a genetically modified oncolytic herpes simplex virus. Specimens from dogs in the trial and dogs not enrolled in the trial were evaluated with immunohistochemistry, NanoString, Luminex cytokine profiling, and multi-parameter flow cytometry. Results: Treatment-naive canine glioma microenvironment had enrichment of Iba1 positive macrophages and minimal numbers of T and B cells, consistent with previous studies identifying these tumors as immunologically “cold”. NanoString mRNA profiling revealed enrichment for tumor intrinsic pathways consistent with suppression of tumor-specific immunity and support of tumor progression. Oncolytic viral treatment induced an intratumoral mRNA transcription signature of tumor-specific immune responses in 83% (5/6) of canine glioma patients. Changes included mRNA signatures corresponding with interferon signaling, lymphoid and myeloid cell activation, recruitment, and T and B cell immunity. Multiplexed protein analysis identified a subset of oligodendroglioma subjects with increased concentrations of IL-2, IL-7, IL-6, IL-10, IL-15, TNFα, GM-CSF between 14 and 28 days after treatment, with evidence of CD4+ T cell activation and modulation of IL-4 and IFNγ production in CD4+ and CD8+ T cells isolated from peripheral blood. Conclusion: These findings indicate that M032 modulates the tumor-immune microenvironment in the canine glioma model.
Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.
Extracellular vesicles (EVs) obtained from endothelial cells (ECs) have significant therapeutic potential in the clinical management of individuals with ischemic stroke (IS) because they effectively treat ischemic stroke in animal models. However, because molecular probes with both high labeling efficiency and tracer stability are lacking, monitoring the actions of EC-EVs in the brain remains difficult. The specific intracellular targets in the brain that EC-EVs act on to produce their protective effects are still unknown, greatly impeding their use in clinical settings. For this research, we created a probe that possessed aggregation-induced emission (AIE) traits (namely, TTCP), enabling the effective labeling of EC-EVs while preserving their physiological properties. In vitro, TTCP simultaneously had a higher EC-EV labeling efficiency and better tracer stability than the commercial EV tags PKH-67 and DiI. In vivo, TTCP precisely tracked the actions of EC-EVs in a mouse IS model without influencing their protective effects. Furthermore, through the utilization of TTCP, it was determined that astrocytes were the specific cells affected by EC-EVs and that EC-EVs exhibited a safeguarding impact on astrocytes following cerebral ischemia-reperfusion (I/R) injury. These protective effects encompassed the reduction of the inflammatory reaction and apoptosis as well as the enhancement of cell proliferation. Further analysis showed that miRNA-155-5p carried by EC-EVs is responsible for these protective effects via regulation of the c-Fos/AP-1 pathway; this information provided a strategy for IS therapy. In conclusion, TTCP has a high EC-EV labeling efficiency and favorable in vivo tracer stability during IS therapy. Moreover, EC-EVs are absorbed by astrocytes during cerebral I/R injury and promote the restoration of neurological function through the regulation of the c-Fos/AP-1 signaling pathway.
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