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      Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer

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          Abstract

          Combining whole exome sequencing, transcriptome profiling, and T cell repertoire analysis, we investigate the spatial features of surgically-removed biopsies from multiple loci in tumor masses of 15 patients with non-small cell lung cancer (NSCLC). This revealed that the immune microenvironment has high spatial heterogeneity such that intratumoral regional variation is as large as inter-personal variation. While the local total mutational burden (TMB) is associated with local T-cell clonal expansion, local anti-tumor cytotoxicity does not directly correlate with neoantigen abundance. Together, these findings caution against that immunological signatures can be predicted solely from TMB or microenvironmental analysis from a single locus biopsy.

          Abstract

          Intratumoral immunity heterogeneity is poorly characterized. Here the authors apply exome sequencing, transcriptome profiling and T-cell repertoire profiling to multiple loci of non-small-cell lung cancer patients' biopsies and find high spatial immune heterogeneity with local mutational burden correlating with T-cell clonal expansion but not with cytotoxicity.

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          Most cited references19

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          Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

          The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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            Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses

            To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single cell analysis of the tumor, non-involved lung and blood cells together with multiplex tissue imaging to assess spatial cell distribution, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Comparing single tumor cells with adjacent normal tissue and blood from patients with lung adenocarcinoma charts early changes in tumor immunity and provides insights to guide immunotherapy design.
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              OptiType: precision HLA typing from next-generation sequencing data

              Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications. Contact: szolek@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Qi-Jing.Li@Duke.edu
                bo.zhu@tmmu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 December 2018
                18 December 2018
                2018
                : 9
                : 5361
                Affiliations
                [1 ]ISNI 0000 0004 1760 6682, GRID grid.410570.7, Institute of Cancer, Xinqiao Hospital, , Third Military Medical University, ; Chongqing, 400037 China
                [2 ]Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037 China
                [3 ]ISNI 0000 0004 1760 6682, GRID grid.410570.7, Department of Cardiothorathic Surgery, Southwest Hospital, , Third Military Medical University, ; Chongqing, 400038 China
                [4 ]Geneplus-Beijing Institute, Beijing, 102206 China
                [5 ]ISNI 0000000100241216, GRID grid.189509.c, Department of Immunology, , Duke University Medical Center, ; Durham, 27710 NC USA
                [6 ]ISNI 0000 0004 1760 6682, GRID grid.410570.7, Biomedical Analysis Center, , Third Military Medical University, ; Chongqing, 400038 China
                [7 ]ISNI 0000 0004 0445 0041, GRID grid.63368.38, Houston Methodist Research Institute, ; Houston, 77030 TX USA
                [8 ]ISNI 0000000122483208, GRID grid.10698.36, Department of Microbiology and Immunology, Lineberger Comprehensive Cancer Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, 27514 NC USA
                [9 ]GeneCast Biotechnology Co., Ltd, Beijing, 102206 China
                [10 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, Department of Genomic Medicine, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                Article
                7767
                10.1038/s41467-018-07767-w
                6299138
                30560866
                4999fb42-8879-4f4f-b1e2-fe115d7da508
                © The Author(s) 2018

                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
                : 28 December 2017
                : 23 November 2018
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