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      Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

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          Abstract

          We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data.

          quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients’ responses to checkpoint blockers.

          Availability: quanTIseq is available at http://icbi.at/quantiseq.

          Electronic supplementary material

          The online version of this article (10.1186/s13073-019-0638-6) contains supplementary material, which is available to authorized users.

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          Most cited references66

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            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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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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                Author and article information

                Contributors
                zlatko.trajanoski@i-med.ac.at
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                24 May 2019
                24 May 2019
                2019
                : 11
                : 34
                Affiliations
                [1 ]ISNI 0000 0000 8853 2677, GRID grid.5361.1, Biocenter, Division of Bioinformatics, , Medical University of Innsbruck, ; Innrain 80, Innsbruck, Austria
                [2 ]ISNI 0000 0000 8853 2677, GRID grid.5361.1, Division of Hygiene and Medical Microbiology, , Medical University of Innsbruck, ; Innsbruck, Austria
                [3 ]ISNI 0000 0000 8853 2677, GRID grid.5361.1, Department of Haematology and Oncology, , Medical University of Innsbruck, ; Innsbruck, Austria
                [4 ]ISNI 0000000089452978, GRID grid.10419.3d, Department of Pathology, , Leiden University Medical Centre, ; Leiden, The Netherlands
                [5 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Vanderbilt University, ; Nashville, TN USA
                [6 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Department of Medicine, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [7 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Department of Biostatistics, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [8 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Department Pathology Microbiology and Immunology, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [9 ]ISNI 0000 0001 0328 4908, GRID grid.5253.1, Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, , University Hospital Heidelberg, ; Heidelberg, Germany
                [10 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Division of Translational Immunotherapy, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [11 ]Austrian Drug Screening Institute, Innrain 66A, Innsbruck, Austria
                Author information
                http://orcid.org/0000-0002-0636-7351
                Article
                638
                10.1186/s13073-019-0638-6
                6534875
                31126321
                3172df8a-e100-435a-9c73-bce9c3d85198
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 26 October 2018
                : 9 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 633592
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 786295
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: T 974-B30
                Award ID: I3291
                Award ID: I3978
                Award Recipient :
                Categories
                Method
                Custom metadata
                © The Author(s) 2019

                Molecular medicine
                cancer immunology,immunotherapy,deconvolution,rna-seq,immune contexture
                Molecular medicine
                cancer immunology, immunotherapy, deconvolution, rna-seq, immune contexture

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