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      Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses

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          Abstract

          The treatment of low-risk primary prostate cancer entails active surveillance only, while high-risk disease requires multimodal treatment including surgery, radiation therapy, and hormonal therapy. Recurrence and development of metastatic disease remains a clinical problem, without a clear understanding of what drives immune escape and tumor progression. Here, we comprehensively describe the tumor microenvironment of localized prostate cancer in comparison with adjacent normal samples and healthy controls. Single-cell RNA sequencing and high-resolution spatial transcriptomic analyses reveal tumor context dependent changes in gene expression. Our data indicate that an immune suppressive tumor microenvironment associates with suppressive myeloid populations and exhausted T-cells, in addition to high stromal angiogenic activity. We infer cell-to-cell relationships from high throughput ligand-receptor interaction measurements within undissociated tissue sections. Our work thus provides a highly detailed and comprehensive resource of the prostate tumor microenvironment as well as tumor-stromal cell interactions.

          Abstract

          The immune suppressive tumour microenvironment drives recurrence and metastatic disease in prostate cancer. Here authors provide a detailed analysis of the microenvironment via single cell RNA sequencing and high-resolution spatial transcriptomics to identify tumour-dependent changes compared to healthy tissue.

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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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                Author and article information

                Contributors
                THIRZ@mgh.harvard.edu
                smei8@mgh.harvard.edu
                DBSYKES@mgh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 February 2023
                7 February 2023
                2023
                : 14
                : 663
                Affiliations
                [1 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Regenerative Medicine, , Massachusetts General Hospital, ; Boston, MA USA
                [2 ]GRID grid.511171.2, Harvard Stem Cell Institute, ; Cambridge, MA USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Stem Cell and Regenerative Biology, , Harvard University, ; Cambridge, MA USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA USA
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Urology, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA USA
                [7 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Childhood Cancer Research Unit, , Department of Women’s and Children’s Health, Karolinska Institutet, ; Stockholm, Sweden
                [8 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of Harvard and MIT, ; Cambridge, MA USA
                [9 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Psychiatry, , Massachusetts General Hospital, ; Boston, MA USA
                [10 ]GRID grid.38142.3c, ISNI 000000041936754X, Massachusetts General Hospital Cancer Center, , Harvard Medical School, ; Boston, MA USA
                [11 ]Present Address: Altos Labs, San Diego, CA USA
                Author information
                http://orcid.org/0000-0001-9230-1756
                http://orcid.org/0000-0001-8258-5898
                http://orcid.org/0000-0001-7940-4558
                http://orcid.org/0000-0003-2308-3649
                http://orcid.org/0000-0002-2794-5165
                http://orcid.org/0000-0001-6054-4163
                http://orcid.org/0000-0003-4849-3695
                http://orcid.org/0000-0002-6036-5875
                http://orcid.org/0000-0002-9788-0221
                Article
                36325
                10.1038/s41467-023-36325-2
                9905093
                36750562
                8b782bea-5928-4d94-8979-2e11846dc905
                © 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
                : 15 April 2022
                : 26 January 2023
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                © The Author(s) 2023

                Uncategorized
                cancer microenvironment,rna sequencing,prostate cancer,tumour angiogenesis
                Uncategorized
                cancer microenvironment, rna sequencing, prostate cancer, tumour angiogenesis

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