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      Immune landscape of tertiary lymphoid structures in hepatocellular carcinoma (HCC) treated with neoadjuvant immune checkpoint blockade

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      1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 3 , 1 , 1 , 4 , 1 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 2 , 1 , 1 , 2 , 2 , 4 , 1 , 2 , 3 , 1 , 1 , 1 , 2 , 1 , 2 , 5 , 6 , 7 , 5 , 6 , 7 , 1 , 1 , 2 , 1 , 1 , 2 , 4 , 1 , 2 , 7 , 1 , 2 , 3 , 8 , * , 1 , 2 , 7 , *
      bioRxiv
      Cold Spring Harbor Laboratory
      neoadjuvant immunotherapy, tertiary lymphoid structures, hepatocellular carcinoma, immune checkpoint inhibitors, single cell multiomics, imaging mass cytometry, single cell RNA, single cell TCR

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          Abstract

          Neoadjuvant immunotherapy is thought to produce long-term remissions through induction of antitumor immune responses before removal of the primary tumor. Tertiary lymphoid structures (TLS), germinal center-like structures that can arise within tumors, may contribute to the establishment of immunological memory in this setting, but understanding of their role remains limited. Here, we investigated the contribution of TLS to antitumor immunity in hepatocellular carcinoma (HCC) treated with neoadjuvant immunotherapy. We found that neoadjuvant immunotherapy induced the formation of TLS, which were associated with superior pathologic response, improved relapse free survival, and expansion of the intratumoral T and B cell repertoire. While TLS in viable tumor displayed a highly active mature morphology, in areas of tumor regression we identified an involuted TLS morphology, which was characterized by dispersion of the B cell follicle and persistence of a T cell zone enriched for ongoing antigen presentation and T cell-mature dendritic cell interactions. Involuted TLS showed increased expression of T cell memory markers and expansion of CD8 + cytotoxic and tissue resident memory clonotypes. Collectively, these data reveal the circumstances of TLS dissolution and suggest a functional role for late-stage TLS as sites of T cell memory formation after elimination of viable tumor.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            NIH Image to ImageJ: 25 years of image analysis

            For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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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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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                26 October 2023
                : 2023.10.16.562104
                Affiliations
                [1 ]Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
                [2 ]Convergence Institute, Johns Hopkins University, Baltimore, Maryland.
                [3 ]Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
                [4 ]Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
                [5 ]Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
                [6 ]The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland.
                [7 ]Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland.
                [8 ]Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland.
                Author notes

                Author contributions

                D.S., M.Y., and E.J.F. conceived and designed this study. D.S., L.K., M.Y., and L.D. performed data analysis and interpreted results. Q.Z. and R.A. performed the pathologic review. D.S., K.M., Q.Z., and R.A. performed the histologic analysis. All authors assisted with the data analysis, provided valuable discussion, and reviewed and edited the final manuscript draft. D.S. and M.Y. wrote the manuscript with input from all the authors.

                [* ]Corresponding authors: Mark Yarchoan ( mark.yarchoan@ 123456jhmi.edu ); Elana J. Fertig ( ejfertig@ 123456jhmi.edu )
                Article
                10.1101/2023.10.16.562104
                10614819
                37904980
                4904c0d2-1871-4588-a189-30039413653c

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                neoadjuvant immunotherapy,tertiary lymphoid structures,hepatocellular carcinoma,immune checkpoint inhibitors,single cell multiomics,imaging mass cytometry,single cell rna,single cell tcr

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