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      Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion

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      Viruses
      MDPI AG

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          Abstract

          Epstein–Barr virus (EBV) is a gamma-herpesvirus associated with 10% of all gastric cancers (GCs) and 1.5% of all human cancers. EBV-associated GCs (EBVaGCs) are pathologically and clinically distinct entities from EBV-negative GCs (EBVnGCs), with EBVaGCs exhibiting differential molecular pathology, treatment response, and patient prognosis. However, the tumor immune landscape of EBVaGC has not been well explored. In this study, a systemic and comprehensive analysis of gene expression and immune landscape features was performed for both EBVaGC and EBVnGC. EBVaGCs exhibited many aspects of a T cell-inflamed phenotype, with greater T and NK cell infiltration, increased expression of immune checkpoint markers (BTLA, CD96, CTLA4, LAG3, PD1, TIGIT, and TIM3), and multiple T cell effector molecules in comparison with EBVnGCs. EBVaGCs also displayed a higher expression of anti-tumor immunity factors (PDL1, CD155, CEACAM1, galectin-9, and IDO1). Six EBV-encoded miRNAs (miR-BARTs 8-3p, 9-5p, 10-3p, 22, 5-5p, and 14-3p) were strongly negatively correlated with the expression of immune checkpoint receptors and multiple markers of anti-tumor immunity. These profound differences in the tumor immune landscape between EBVaGCs and EBVnGCs may help explain some of the observed differences in pathological and clinical outcomes, with an EBV-positive status possibly being a potential biomarker for the application of immunotherapy in GC.

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          QuPath: Open source software for digital pathology image analysis

          QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
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            The Immune Landscape of Cancer

            We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
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              Understanding the tumor immune microenvironment (TIME) for effective therapy

              The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient’s tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.
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                Author and article information

                Contributors
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                Journal
                VIRUBR
                Viruses
                Viruses
                MDPI AG
                1999-4915
                January 2023
                January 07 2023
                : 15
                : 1
                : 176
                Article
                10.3390/v15010176
                36680216
                30671f5c-3ef2-46b5-a93f-eb02638b64ce
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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