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      Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data

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          High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 ( 13). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community’s founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets ( join@ ).

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          Most cited references 69

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          Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

          Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.
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            T cells with chimeric antigen receptors have potent antitumor effects and can establish memory in patients with advanced leukemia.

            Tumor immunotherapy with T lymphocytes, which can recognize and destroy malignant cells, has been limited by the ability to isolate and expand T cells restricted to tumor-associated antigens. Chimeric antigen receptors (CARs) composed of antibody binding domains connected to domains that activate T cells could overcome tolerance by allowing T cells to respond to cell surface antigens; however, to date, lymphocytes engineered to express CARs have demonstrated minimal in vivo expansion and antitumor effects in clinical trials. We report that CAR T cells that target CD19 and contain a costimulatory domain from CD137 and the T cell receptor ζ chain have potent non-cross-resistant clinical activity after infusion in three of three patients treated with advanced chronic lymphocytic leukemia (CLL). The engineered T cells expanded >1000-fold in vivo, trafficked to bone marrow, and continued to express functional CARs at high levels for at least 6 months. Evidence for on-target toxicity included B cell aplasia as well as decreased numbers of plasma cells and hypogammaglobulinemia. On average, each infused CAR-expressing T cell was calculated to eradicate at least 1000 CLL cells. Furthermore, a CD19-specific immune response was demonstrated in the blood and bone marrow, accompanied by complete remission, in two of three patients. Moreover, a portion of these cells persisted as memory CAR(+) T cells and retained anti-CD19 effector functionality, indicating the potential of this major histocompatibility complex-independent approach for the effective treatment of B cell malignancies.
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              Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells.

              The adaptive immune system uses several strategies to generate a repertoire of T- and B-cell antigen receptors with sufficient diversity to recognize the universe of potential pathogens. In alphabeta T cells, which primarily recognize peptide antigens presented by major histocompatibility complex molecules, most of this receptor diversity is contained within the third complementarity-determining region (CDR3) of the T-cell receptor (TCR) alpha and beta chains. Although it has been estimated that the adaptive immune system can generate up to 10(16) distinct alphabeta pairs, direct assessment of TCR CDR3 diversity has not proved amenable to standard capillary electrophoresis-based DNA sequencing. We developed a novel experimental and computational approach to measure TCR CDR3 diversity based on single-molecule DNA sequencing, and used this approach to determine the CDR3 sequence in millions of rearranged TCRbeta genes from T cells of 2 adults. We find that total TCRbeta receptor diversity is at least 4-fold higher than previous estimates, and the diversity in the subset of CD45RO(+) antigen-experienced alphabeta T cells is at least 10-fold higher than previous estimates. These methods should prove valuable for assessment of alphabeta T-cell repertoire diversity after hematopoietic cell transplantation, in states of congenital or acquired immunodeficiency, and during normal aging.

                Author and article information

                1Department of Biological Sciences, Simon Fraser University , Burnaby, BC, Canada
                2Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, United States
                3La Jolla Institute for Allergy and Immunology , La Jolla, CA, United States
                4Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine , Stanford, CA, United States
                5Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) , Bethesda, MD, United States
                6Division of B Cell Immunology, Deutsches Krebsforschungszentrum (DKFZ) , Heidelberg, Germany
                7Department of Neurology, Yale University School of Medicine , New Haven, CT, United States
                8Department of Clinical Sciences, University of Texas Southwestern Medical Center , Dallas, TX, United States
                9Department of Pathology, Yale University School of Medicine , New Haven, CT, United States
                10entre of Genomics and Policy, McGill University , Montreal, QC, Canada
                11Public Health Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center , Seattle, WA, United States
                12Department of Molecular Microbiology and Biotechnology, Tel Aviv University , Tel Aviv, Israel
                13Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai , New York, NY, United States
                14Immunology-Immunopathology-Immunotherapy (i3 & i2B), Sorbonne Université , Paris, France
                15IMGT, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier , Montpellier, France
                16School of Biotechnology and Biomolecular Sciences, University of New South Wales , Kensington, NSW, Australia
                17Faculty of Health Sciences, Simon Fraser University , Burnaby, BC, Canada
                18Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine , Louisville, KY, United States
                19Faculty of Health Sciences, Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, BC, Canada
                20Department of Microbiology, Boston University School of Medicine , Boston, MA, United States
                21Department of Mathematics and Statistics, Boston University , Boston, MA, United States
                Author notes

                Edited by: Gregory C. Ippolito, University of Texas at Austin, United States

                Reviewed by: Michael Zemlin, Universitätsklinikum des Saarlandes, Germany; Deborah K. Dunn-Walters, University of Surrey, United Kingdom

                *Correspondence: Felix Breden, breden@ ; Eline T. Luning Prak, luning@

                Specialty section: This article was submitted to B Cell Biology, a section of the journal Frontiers in Immunology

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                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                01 November 2017
                : 8
                5671925 10.3389/fimmu.2017.01418
                Copyright © 2017 Breden, Luning Prak, Peters, Rubelt, Schramm, Busse, Vander Heiden, Christley, Bukhari, Thorogood, Matsen IV, Wine, Laserson, Klatzmann, Douek, Lefranc, Collins, Bubela, Kleinstein, Watson, Cowell, Scott and Kepler.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Figures: 0, Tables: 0, Equations: 0, References: 69, Pages: 6, Words: 5428
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Award ID: 137191
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: 1R13AI116349-01, P01-AI106697, P30-CA016520
                Funded by: Illumina 10.13039/100010905
                Funded by: Genentech 10.13039/100004328
                Funded by: Amgen 10.13039/100002429


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