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      Neoadjuvant PD-1 blockade induces T cell and cDC1 activation but fails to overcome the immunosuppressive tumor associated macrophages in recurrent glioblastoma.

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          Abstract

          Primary brain tumors, such as glioblastoma (GBM), are remarkably resistant to immunotherapy, even though pre-clinical models suggest effectiveness. To understand this better in patients, here we take advantage of our recent neoadjuvant treatment paradigm to map the infiltrating immune cell landscape of GBM and how this is altered following PD-1 checkpoint blockade using high dimensional proteomics, single cell transcriptomics, and quantitative multiplex immunofluorescence. Neoadjuvant PD-1 blockade increases T cell infiltration and the proportion of a progenitor exhausted population of T cells found within the tumor. We identify an early activated and clonally expanded CD8+ T cell cluster whose TCR overlaps with a CD8+ PBMC population. Distinct changes are also observed in conventional type 1 dendritic cells that may facilitate T cell recruitment. Macrophages and monocytes still constitute the majority of infiltrating immune cells, even after anti-PD-1 therapy. Interferon-mediated changes in the myeloid population are consistently observed following PD-1 blockade; these also mediate an increase in chemotactic factors that recruit T cells. However, sustained high expression of T-cell-suppressive checkpoints in these myeloid cells continue to prevent the optimal activation of the tumor infiltrating T cells. Therefore, future immunotherapeutic strategies may need to incorporate the targeting of these cells for clinical benefit.

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          Most cited references57

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          Integrating single-cell transcriptomic data across different conditions, technologies, and species

          Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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            Is Open Access

            Inference and analysis of cell-cell communication using CellChat

            Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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              Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

              To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.
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                Author and article information

                Journal
                Nat Commun
                Nature communications
                Springer Science and Business Media LLC
                2041-1723
                2041-1723
                Nov 26 2021
                : 12
                : 1
                Affiliations
                [1 ] Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
                [2 ] Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
                [3 ] Department of Neurology/Neuro-Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
                [4 ] UCLA Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
                [5 ] Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
                [6 ] UCLA Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA. HWilly@mednet.ucla.edu.
                [7 ] Parker Institute for Cancer Immunotherapy, 1 Letterman Drive, Suite D3500, San Francisco, CA, 94129, USA. HWilly@mednet.ucla.edu.
                [8 ] Department of Medicine/Dermatology, University of California, Los Angeles, Los Angeles, CA, 90095, USA. HWilly@mednet.ucla.edu.
                [9 ] Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, 90095, USA. RPrins@mednet.ucla.edu.
                [10 ] Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, 90095, USA. RPrins@mednet.ucla.edu.
                [11 ] UCLA Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA. RPrins@mednet.ucla.edu.
                [12 ] Parker Institute for Cancer Immunotherapy, 1 Letterman Drive, Suite D3500, San Francisco, CA, 94129, USA. RPrins@mednet.ucla.edu.
                Article
                10.1038/s41467-021-26940-2
                10.1038/s41467-021-26940-2
                8626557
                34836966
                6f713fa5-3d5f-4a1c-99e1-ba381a7d8d77
                History

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