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      Lymph node and tumor-associated PD-L1 + macrophages antagonize dendritic cell vaccines by suppressing CD8 + T cells

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      1 , 1 , 2 , 1 , 1 , 1 , 1 , 1 , 3 , 2 , 4 , 4 , 4 , 4 , 5 , 6 , 5 , 6 , 5 , 6 , 7 , 8 , 9 , 5 , 6 , 10 , 3 , 11 , 12 , 13 , 2 , 1 , 14 ,
      Cell Reports Medicine
      Elsevier
      programmed cell death-1, PD-1, damage-associated molecular patterns, DAMPs, tumor-associated antigens, TAAs, apoptosis, necroptosis, mature regulatory DCs, mregDC, single-cell omics, immune-checkpoint blockers, ICB, nuclear factor κB, NF-κB

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          Summary

          Current immunotherapies provide limited benefits against T cell-depleted tumors, calling for therapeutic innovation. Using multi-omics integration of cancer patient data, we predict a type I interferon (IFN) response HIGH state of dendritic cell (DC) vaccines, with efficacious clinical impact. However, preclinical DC vaccines recapitulating this state by combining immunogenic cancer cell death with induction of type I IFN responses fail to regress mouse tumors lacking T cell infiltrates. Here, in lymph nodes (LNs), instead of activating CD4 +/CD8 + T cells, DCs stimulate immunosuppressive programmed death-ligand 1-positive (PD-L1 +) LN-associated macrophages (LAMs). Moreover, DC vaccines also stimulate PD-L1 + tumor-associated macrophages (TAMs). This creates two anatomically distinct niches of PD-L1 + macrophages that suppress CD8 + T cells. Accordingly, a combination of PD-L1 blockade with DC vaccines achieves significant tumor regression by depleting PD-L1 + macrophages, suppressing myeloid inflammation, and de-inhibiting effector/stem-like memory T cells. Importantly, clinical DC vaccines also potentiate T cell-suppressive PD-L1 + TAMs in glioblastoma patients. We propose that a multimodal immunotherapy and vaccination regimen is mandatory to overcome T cell-depleted tumors.

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          Highlights

          • Multi-omics analyses predict a highly immunogenic type I IFN HIGH DC vaccine state

          • DC vaccines fail because they facilitate PD-L1 + TAMs in lymph nodes and tumors

          • PD-L1 + TAMs suppress CD8 + T cell responses to disrupt DC vaccine efficacy

          • Targeting PD-L1 + TAMs via PD-L1 blockade improves DC vaccine-driven tumor control

          Abstract

          Sprooten et al. use human-to-mouse reverse translation to create DC vaccines. Counterintuitively, these induce an accumulation of CD8 + T cell-suppressive PD-L1 + macrophages in lymph nodes and tumors, such that vaccination and co-blockade of PD-L1 (but not PD-1) is mandatory for tumor suppression. This pathway is also operational in DC vaccinated cancer patients.

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          Most cited references128

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

            Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.
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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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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                16 January 2024
                16 January 2024
                16 January 2024
                : 5
                : 1
                : 101377
                Affiliations
                [1 ]Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
                [2 ]Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
                [3 ]Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
                [4 ]Department of Neurosurgery, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
                [5 ]Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France
                [6 ]Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
                [7 ]JJP Biologics, Warsaw, Poland
                [8 ]Laboratory for Molecular Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
                [9 ]Department of Immunology and Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
                [10 ]Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France
                [11 ]Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
                [12 ]Laboratory of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven, Leuven, Belgium
                [13 ]Leuven Brain Institute (LBI), Leuven, Belgium
                Author notes
                []Corresponding author abhishek.garg@ 123456kuleuven.be
                [14]

                Lead contact

                Article
                S2666-3791(23)00606-7 101377
                10.1016/j.xcrm.2023.101377
                10829875
                38232703
                891207c1-e72b-4776-b2c1-aa8ab3019d59
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 August 2022
                : 23 August 2023
                : 18 December 2023
                Categories
                Article

                programmed cell death-1,pd-1,damage-associated molecular patterns,damps,tumor-associated antigens,taas,apoptosis,necroptosis,mature regulatory dcs,mregdc,single-cell omics,immune-checkpoint blockers,icb,nuclear factor κb,nf-κb

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