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      Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer

      1 , 2 , 3 , 1 , 2 , 4 , 1 , 2 , 5 , 1 , 6 , 1 , 1 , 7 , 1 , 7 , 5 , 1 , 1 , 2 , 3 , 8 , 1 , 9 , 1 , 9 , 10 , 4 , 4 , 11 , 12 , 13 , 14 , 14 , 14 , 14 , 14 , 1 , 15 , 6 , 1 , 16 , 9 , 11 , 12 , 13 , 17 , 18 , 1 , 7 , 14 , 14 , 12 , 13 , 1 , 2 , 3 , 9 , 1 , 2 , 4
      Science Immunology
      American Association for the Advancement of Science (AAAS)

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          Abstract

          About 50% of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) experience recurrences after definitive therapy. The presurgical administration of anti–programmed cell death protein 1 (PD-1) immunotherapy results in substantial pathologic tumor responses (pTR) within the tumor microenvironment (TME). However, the mechanisms underlying the dynamics of antitumor T cells upon neoadjuvant PD-1 blockade remain unresolved, and approaches to increase pathologic responses are lacking. In a phase 2 trial (NCT02296684), we observed that 45% of patients treated with two doses of neoadjuvant pembrolizumab experienced marked pTRs (≥50%). Single-cell analysis of 17,158 CD8 + T cells from 14 tumor biopsies, including 6 matched pre-post neoadjuvant treatment, revealed that responding tumors had clonally expanded putative tumor-specific exhausted CD8 + tumor-infiltrating lymphocytes (TILs) with a tissue-resident memory program, characterized by high cytotoxic potential (CTX + ) and ZNF683 expression, within the baseline TME. Pathologic responses after 5 weeks of PD-1 blockade were consistent with activation of preexisting CTX + ZNF683 + CD8 + TILs, paralleling loss of viable tumor and associated tumor antigens. Response was associated with high numbers of CD103 + PD-1 + CD8 + T cells infiltrating pretreatment lesions, whereas revival of nonexhausted persisting clones and clonal replacement were modest. By contrast, nonresponder baseline TME exhibited a relative absence of ZNF683 + CTX + TILs and subsequent accumulation of highly exhausted clones. In HNSCC, revival of preexisting ZNF683 + CTX + TILs is a major mechanism of response in the immediate postneoadjuvant setting.

          Abstract

          Neoadjuvant anti–PD-1 HNSCC response associates with revival of preexisting TILs with cytotoxic potential and resident memory program.

          Editor’s summary

          Immune checkpoint blockade (ICB) such as anti-PD-1 reinvigorates tumor-specific T cell responses, but the mechanisms underlying specific clinical responses remain unclear. Using single-cell transcriptomics and TCR sequencing, Oliveira et al . analyzed tumor specimens from patients with head and neck cancer enrolled in a phase II clinical trial testing two doses of neoadjuvant anti–PD-1 before surgical resection. Tumors responding to anti–PD-1 contained a baseline population of ZNF683 + CD8 + T cells expressing genes associated with T cell exhaustion, tissue resident memory, and cytotoxicity. In paired pre- and post-treatment biopsies, ZNF683 + CD8 + T cells were clonally expanded and exhibited the strongest change in patients responding to ICB. These findings identify reinvigoration of cytotoxicity among ZNF683 + CD8 + T cells as a likely mechanism underlying response to neoadjuvant anti–PD-1 in head and neck cancer. —Claire Olingy

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          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Journal
                Science Immunology
                Sci. Immunol.
                American Association for the Advancement of Science (AAAS)
                2470-9468
                September 08 2023
                September 08 2023
                : 8
                : 87
                Affiliations
                [1 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
                [2 ]Harvard Medical School, Boston, MA, USA.
                [3 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA.
                [4 ]Department of Surgery, Brigham and Women’s Hospital, Boston, MA, USA.
                [5 ]Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
                [6 ]Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
                [7 ]Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA.
                [8 ]Department of Hematology, Oncology and Tumor Immunology, Campus Virchow Klinikum, Berlin, Charité–Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
                [9 ]Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA.
                [10 ]Department of Radiation-Oncology, Brigham and Women’s Hospital, Boston, MA, USA.
                [11 ]Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA.
                [12 ]Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
                [13 ]Department of Medicine/Medical Oncology, Washington University School of Medicine, St. Louis, MO, USA.
                [14 ]Department of Otolaryngology, Washington University School of Medicine, St. Louis, MO, USA.
                [15 ]Department of Otolaryngology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
                [16 ]Department of Neurological Surgery, Massachusetts General Hospital, Boston, MA, USA.
                [17 ]Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [18 ]Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                Article
                10.1126/sciimmunol.adf4968
                37683037
                660acebc-6914-407a-98eb-e1ec7fa34d5c
                © 2023

                Free to read

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