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      Nasopharyngeal carcinoma cells promote regulatory T cell development and suppressive activity via CD70-CD27 interaction

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          Abstract

          Despite the intense CD8+ T-cell infiltration in the tumor microenvironment of nasopharyngeal carcinoma, anti-PD-1 immunotherapy shows an unsatisfactory response rate in clinical trials, hindered by immunosuppressive signals. To understand how microenvironmental characteristics alter immune homeostasis and limit immunotherapy efficacy in nasopharyngeal carcinoma, here we establish a multi-center single-cell cohort based on public data, containing 357,206 cells from 50 patient samples. We reveal that nasopharyngeal carcinoma cells enhance development and suppressive activity of regulatory T cells via CD70-CD27 interaction. CD70 blocking reverts Treg-mediated suppression and thus reinvigorate CD8+ T-cell immunity. Anti-CD70+ anti-PD-1 therapy is evaluated in xenograft-derived organoids and humanized mice, exhibiting an improved tumor-killing efficacy. Mechanistically, CD70 knockout inhibits a collective lipid signaling network in CD4+ naïve and regulatory T cells involving mitochondrial integrity, cholesterol homeostasis, and fatty acid metabolism. Furthermore, ATAC-Seq delineates that CD70 is transcriptionally upregulated by NFKB2 via an Epstein-Barr virus-dependent epigenetic modification. Our findings identify CD70+ nasopharyngeal carcinoma cells as a metabolic switch that enforces the lipid-driven development, functional specialization and homeostasis of Tregs, leading to immune evasion. This study also demonstrates that CD70 blockade can act synergistically with anti-PD-1 treatment to reinvigorate T-cell immunity against nasopharyngeal carcinoma.

          Abstract

          Response rates to immunotherapies in patients with nasopharyngeal carcinoma (NPC) are still limited. Here the authors show that tumor-restricted CD70 correlates with regulatory T cell abundance and suppressive activity in NPC and that CD70 blockade improves response to anti-PD1 in preclinical models.

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          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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            Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

            Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research.
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              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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                Author and article information

                Contributors
                xyguan@hku.hk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 April 2023
                6 April 2023
                2023
                : 14
                : 1912
                Affiliations
                [1 ]GRID grid.194645.b, ISNI 0000000121742757, Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, , The University of Hong Kong, ; Hong Kong, China
                [2 ]GRID grid.440671.0, ISNI 0000 0004 5373 5131, Department of Clinical Oncology, , The University of Hong Kong-Shenzhen Hospital, ; Shenzhen, China
                [3 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, Department of Pediatric Oncology, , Sun Yat-sen University Cancer Center, ; Guangzhou, China
                [4 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, , Sun Yat-sen University Cancer Center, ; Guangzhou, China
                [5 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, Collaborative Innovation Center for Cancer Medicine, , Sun Yat-sen University Cancer Center, ; Guangzhou, China
                [6 ]GRID grid.440671.0, ISNI 0000 0004 5373 5131, Department of Surgery, , The University of Hong Kong-Shenzhen Hospital, ; Shenzhen, China
                [7 ]GRID grid.440671.0, ISNI 0000 0004 5373 5131, Department of Pathology, , The University of Hong Kong-Shenzhen Hospital, ; Shenzhen, China
                [8 ]GRID grid.194645.b, ISNI 0000000121742757, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, , The University of Hong Kong, ; Hong Kong, China
                [9 ]GRID grid.450259.f, ISNI 0000 0004 1804 2516, Advanced Energy Science and Technology Guangdong Laboratory, ; Huizhou, China
                Author information
                http://orcid.org/0000-0002-1531-4948
                http://orcid.org/0000-0003-1966-2285
                http://orcid.org/0000-0001-7448-5811
                http://orcid.org/0000-0003-2972-0231
                http://orcid.org/0000-0002-5558-0685
                http://orcid.org/0000-0002-4485-6017
                Article
                37614
                10.1038/s41467-023-37614-6
                10079957
                37024479
                4cd543d9-38d9-4fa2-a57b-d51162876265
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 May 2022
                : 24 March 2023
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                cancer microenvironment,head and neck cancer,cancer immunotherapy
                Uncategorized
                cancer microenvironment, head and neck cancer, cancer immunotherapy

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