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      Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade

      1 , 2 , 3 , 3 , 4 , 3 , 5 , 5 , 5 , 3 , 1 , 3 , 1 , 3 , 1 , 3 , 1 , 3 , 3 , 3 , 1 , 4 , 6 , 4 , 6 , 4 , 6 , 3 , 4 , 7 , 8 , 9 , 7 , 8 , 10 , 11 , 10 , 11 , 7 , 8 , 9 , 12 , 13 , 14 , 15 , 14 , 15 , 14 , 16 , 14 , 15 , 16 , 7 , 8 , 7 , 8 , 17 , 18 , 3 , 4 , 12 , 5 , 3 , 4 , 1 , 3
      Science
      American Association for the Advancement of Science (AAAS)

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          The cellular ancestry of tumor antigens

          One contributing factor in antitumor immunity is the repertoire of neoantigens created by genetic mutations within tumor cells. Like the corresponding mutations, these neoantigens show intratumoral heterogeneity. Some are present in all tumor cells (clonal), and others are present in only a fraction of cells (subclonal). In a study of lung cancer and melanoma, McGranahan et al. found that a high burden of clonal tumor neoantigens correlated with improved patient survival, an increased presence of tumor-infiltrating lymphocytes, and a durable response to immunotherapy.

          Science , this issue p. [Related article:]1463

          Abstract

          Analysis of the cellular ancestry of tumor neoantigens can predict which are most likely to induce an immune response.

          Abstract

          As tumors grow, they acquire mutations, some of which create neoantigens that influence the response of patients to immune checkpoint inhibitors. We explored the impact of neoantigen intratumor heterogeneity (ITH) on antitumor immunity. Through integrated analysis of ITH and neoantigen burden, we demonstrate a relationship between clonal neoantigen burden and overall survival in primary lung adenocarcinomas. CD8 + tumor-infiltrating lymphocytes reactive to clonal neoantigens were identified in early-stage non–small cell lung cancer and expressed high levels of PD-1. Sensitivity to PD-1 and CTLA-4 blockade in patients with advanced NSCLC and melanoma was enhanced in tumors enriched for clonal neoantigens. T cells recognizing clonal neoantigens were detectable in patients with durable clinical benefit. Cytotoxic chemotherapy–induced subclonal neoantigens, contributing to an increased mutational load, were enriched in certain poor responders. These data suggest that neoantigen heterogeneity may influence immune surveillance and support therapeutic developments targeting clonal neoantigens.

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          Most cited references36

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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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            Is Open Access

            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                March 25 2016
                March 25 2016
                : 351
                : 6280
                : 1463-1469
                Affiliations
                [1 ]The Francis Crick Institute, London WC2A 3LY, UK.
                [2 ]Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London (UCL), London WC1E 6BT, UK.
                [3 ]Cancer Research UK Lung Cancer Centre of Excellence, UCL Cancer Institute, London WC1E 6BT, UK.
                [4 ]Cancer Immunology Unit, UCL Cancer Institute, UCL, London WC1E 6BT, UK.
                [5 ]Section for Immunology and Vaccinology, National Veterinary Institute, Technical University of Denmark, 1970 Frederiksberg C, Denmark.
                [6 ]Department of Cellular Pathology, UCL, London WC1E 6BT, UK.
                [7 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
                [8 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [9 ]Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
                [10 ]Department of Dermatology, University Hospital, University Duisburg–Essen, 45147 Essen, Germany.
                [11 ]German Cancer Consortium (DKTK), 69121 Heidelberg, Germany.
                [12 ]Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
                [13 ]Hematology/Oncology Division, 177 Fort Washington Avenue, Columbia University, New York, NY 10032, USA.
                [14 ]Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
                [15 ]Weill Cornell Medical College, New York, NY 10065, USA.
                [16 ]Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
                [17 ]Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
                [18 ]Department of Internal Medicine, Brigham and Woman’s Hospital, Boston, MA 02115, USA.
                Article
                10.1126/science.aaf1490
                26940869
                41bcb4e8-3d64-4528-803b-f2128b1bfcc2
                © 2016
                History

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