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      TOX is a critical regulator of tumour-specific T cell differentiation

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          Abstract

          Tumour-specific CD8 T cell dysfunction is a differentiation state that is distinct from the functional effector or memory T cell states 16 . Here we identify the nuclear factor TOX as a crucial regulator of the differentiation of tumour-specific T (TST) cells. We show that TOX is highly expressed in dysfunctional TST cells from tumours and in exhausted T cells during chronic viral infection. Expression of TOX is driven by chronic T cell receptor stimulation and NFAT activation. Ectopic expression of TOX in effector T cells in vitro induced a transcriptional program associated with T cell exhaustion. Conversely, deletion of Tox in TST cells in tumours abrogated the exhaustion program: Tox-deleted TST cells did not upregulate genes for inhibitory receptors (such as Pdcd1, Entpd1, Havcr2, Cd244 and Tigit), the chromatin of which remained largely inaccessible, and retained high expression of transcription factors such as TCF-1. Despite their normal, ‘non-exhausted’ immunophenotype, Tox-deleted TST cells remained dysfunctional, which suggests that the regulation of expression of inhibitory receptors is uncoupled from the loss of effector function. Notably, although Tox-deleted CD8 T cells differentiated normally to effector and memory states in response to acute infection, Tox-deleted TST cells failed to persist in tumours. We hypothesize that the TOX-induced exhaustion program serves to prevent the overstimulation of T cells and activation-induced cell death in settings of chronic antigen stimulation such as cancer.

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          Most cited references45

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          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              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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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                20 September 2020
                17 June 2019
                July 2019
                28 November 2020
                : 571
                : 7764
                : 270-274
                Affiliations
                [1 ]Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [2 ]Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.
                [3 ]Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
                [4 ]Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, USA.
                [5 ]Parker Institute for Cancer Immunotherapy, New York, NY, USA.
                [6 ]Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [7 ]Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [8 ]Weill Cornell Medical College, New York, NY, USA.
                [9 ]Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [10 ]Department of Dermatology, Weill Cornell Medical College, New York, NY, USA.
                [11 ]Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York Presbyterian Hospital, New York, NY, USA.
                [12 ]Aduro Biotech, Inc., Berkeley, CA, USA.
                [13 ]Research Division of Immunology, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
                [14 ]Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
                [15 ]Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
                [16 ]Department of Medicine, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, USA.
                Author notes

                Author contributions A.C.S., M.P. and A. Schietinger conceived and designed the study. A.C.S., M.P., D.B., F.D., P.Z. and A. Schietinger conceived the computational analyses; D.B., F.D. and P.Z. performed all of the computational analyses. A.C.S., M.P., P.T., L.M., M.S., H.A. and S.S.C. carried out experiments. A.C.S., M.P., F.D., P.Z., D.B., S.S.C., C.A.K. and A. Schietinger interpreted data. S.C. and H.A. assisted with mouse breeding; T.W., A. Snyder, D.Z., M.D.H., M.R.F., E.A.C., H.Y.W. and C.A.K. provided human samples; N.A., Y.L. and N.K.A. contributed to the analysis of human samples. O.L. and M.S.G. provided help in establishing the knockout model. O.L., M.S.G. and J.K. provided mice. P.L. provided Listeria strains. A.C.S., M.P., F.D., P.Z., D.P. and A. Schietinger wrote the manuscript, with all authors contributing to writing and providing feedback.

                [* ] Correspondence and requests for materials should be addressed to M.P. and A. Schietinger mary.philip@ 123456vumc.org ; schietia@ 123456mskcc.org
                Article
                NIHMS1531023
                10.1038/s41586-019-1324-y
                7698992
                31207604
                a3820cdb-379c-4a49-910a-a4b405707756

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