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      Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

      research-article
      1 , 2 , 3 , 4 , 5 , 1 , 2 , 6 , 4 , 5 , 4 , 5 , 2 , 4 , 5 , 7 , 7 , 8 , 8 , 1 , 2 , 1 , 2 , 2 , 6 , 2 , 6 , 9 , 1 , 2 , 1 , 2 , 1 , 2 , 6 , 2 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 5 , 19 , 20 , 21 , 22 , 1 , 2 , 23 , 2 , 24 , 4 , 5 , 1 , 2 , , 4 , 5 ,
      Nature Medicine
      Nature Publishing Group US
      Cancer genomics, Melanoma, Computational biology and bioinformatics

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          Abstract

          Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma ( n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.

          Abstract

          Analysis of fully clinically annotated and sequenced melanoma tumor samples collected before anti-PD1 treatment suggests that determinants of response differ on the basis of previous anti-CTLA4 therapy, and that tumor mutational burden may not be a strong predictor of response across melanoma subtypes.

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          Most cited references13

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          MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma

          Combination anti–cytotoxic T lymphocyte antigen 4 (CTLA-4) and anti–programmed cell death protein 1 (PD-1) therapy promotes antitumor immunity and provides superior benefit to patients with advanced-stage melanoma compared with either therapy alone. T cell immunity requires recognition of antigens in the context of major histocompatibility complex (MHC) class I and class II proteins by CD8+ and CD4+ T cells, respectively. We examined MHC class I and class II protein expression on tumor cells from previously untreated melanoma patients and correlated the results with transcriptional and genomic analyses and with clinical response to anti–CTLA-4, anti–PD-1, or combination therapy. Most (>50% of cells) or complete loss of melanoma MHC class I membrane expression was observed in 78 of 181 cases (43%), was associated with transcriptional repression of HLA-A, HLA-B, HLA-C, and B2M, and predicted primary resistance to anti–CTLA-4, but not anti–PD-1, therapy. Melanoma MHC class II membrane expression on >1% cells was observed in 55 of 181 cases (30%), was associated with interferon- (IFN-) and IFN-–mediated gene signatures, and predicted response to anti–PD-1, but not anti–CTLA-4, therapy. We conclude that primary response to anti–CTLA-4 requires robust melanoma MHC class I expression. In contrast, primary response to anti–PD-1 is associated with preexisting IFN-–mediated immune activation that includes tumor-specific MHC class II expression and components of innate immunity when MHC class I is compromised. The benefits of combined checkpoint blockade may be attributable, in part, to distinct requirements for melanoma-specific antigen presentation to initiate antitumor immunity.
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            Baseline Biomarkers for Outcome of Melanoma Patients Treated with Pembrolizumab.

            Biomarkers for outcome after immune-checkpoint blockade are strongly needed as these may influence individual treatment selection or sequence. We aimed to identify baseline factors associated with overall survival (OS) after pembrolizumab treatment in melanoma patients.
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              • Article: not found

              Oncotator: cancer variant annotation tool.

              Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.
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                Author and article information

                Contributors
                eliezerm_vanallen@dfci.harvard.edu
                dirk.schadendorf@uk-essen.de
                Journal
                Nat Med
                Nat. Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                2 December 2019
                2 December 2019
                2019
                : 25
                : 12
                : 1916-1927
                Affiliations
                [1 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Dana-Farber Cancer Institute, ; Boston, MA USA
                [2 ]GRID grid.66859.34, Broad Institute of Harvard and MIT, ; Cambridge, MA USA
                [3 ]ISNI 0000 0001 1378 7891, GRID grid.411760.5, Department of Dermatology, , University Hospital Würzburg, ; Würzburg, Germany
                [4 ]Department of Dermatology, University Hospital, Essen, Germany
                [5 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Consortium of Translational Cancer Research, , German Cancer Research Center, ; Heidelberg, Germany
                [6 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; Boston, MA USA
                [7 ]ISNI 0000 0000 9529 9877, GRID grid.10423.34, Skin Cancer Center Hannover, Department of Dermatology and Allergy, , Hannover Medical School, ; Hannover, Germany
                [8 ]GRID grid.410607.4, Department of Dermatology, , University Medical Center, ; Mainz, Germany
                [9 ]ISNI 000000041936754X, GRID grid.38142.3c, Biophysics Program, , Harvard University, ; Cambridge, MA USA
                [10 ]ISNI 0000 0001 2155 0800, GRID grid.5216.0, First Department of Medicine, , National and Kapodistrian University of Athens, ; Athens, Greece
                [11 ]ISNI 0000 0004 0478 9977, GRID grid.412004.3, Department of Dermatology, , University Hospital Zürich, ; Zürich, Switzerland
                [12 ]ISNI 0000 0001 2162 1728, GRID grid.411778.c, Department of Dermatology, , University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, ; Mannheim, Germany
                [13 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Skin Cancer Unit, , German Cancer Research Center, ; Heidelberg, Germany
                [14 ]GRID grid.430814.a, Department of Medical Oncology, , The Netherlands Cancer Institute, ; Amsterdam, the Netherlands
                [15 ]Skin Cancer Center at the University Cancer Centre, Department of Dermatology, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
                [16 ]ISNI 0000 0001 0328 4908, GRID grid.5253.1, National Center for Tumor Diseases, ; Dresden, Germany
                [17 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Research Centre, ; Heidelberg, Germany
                [18 ]Department of Dermatology, Medical Center- University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
                [19 ]ISNI 0000000123222966, GRID grid.6936.a, Medizinische Klinik III, Klinikum Rechts der Isar, , Technische Universität München, ; Munich, Germany
                [20 ]ISNI 0000 0001 0196 8249, GRID grid.411544.1, Department of Dermatology, , University Medical Center Tübingen, ; Tübingen, Germany
                [21 ]ISNI 0000 0000 9194 7179, GRID grid.411941.8, Department of Dermatology, , University Hospital Regensburg, ; Regensburg, Germany
                [22 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Department of Dermatology, , University Hospital Berlin, ; Berlin, Germany
                [23 ]ISNI 0000 0000 2220 2544, GRID grid.417540.3, Eli Lilly and Co., ; Indianapolis, IN USA
                [24 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Massachusetts General Hospital, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0001-8859-4103
                http://orcid.org/0000-0003-1754-2319
                http://orcid.org/0000-0001-5316-0241
                http://orcid.org/0000-0002-7945-5846
                http://orcid.org/0000-0003-3293-3158
                http://orcid.org/0000-0002-3402-0478
                http://orcid.org/0000-0002-0201-4444
                http://orcid.org/0000-0003-3524-7858
                Article
                654
                10.1038/s41591-019-0654-5
                6898788
                31792460
                58f3b73c-3b0f-4b5d-a9ec-329cf9517d5e
                © The Author(s) 2019

                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
                : 25 March 2019
                : 17 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: SCHA 422/17-1; PA 2376/1-1; HO 6389/2-1 (KFO 337)
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000982, name>Conquer Cancer Foundation (Conquer Cancer Foundation of the American Society of Clinical Oncology);
                Funded by: FundRef https://doi.org/10.13039/100011619, Society for Immunotherapy of Cancer (SITC);
                Funded by: FundRef https://doi.org/10.13039/100001021, Damon Runyon Cancer Research Foundation (Cancer Research Fund of the Damon Runyon-Walter Winchell Foundation);
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: T32 GM008313
                Award Recipient :
                Funded by: NIH (funding source); R01 CA227388, U01 CA233100 (grant ID #s) BroadNext10 (funding source, no ID)
                Categories
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                © The Author(s), under exclusive licence to Springer Nature Limited 2019

                Medicine
                cancer genomics,melanoma,computational biology and bioinformatics
                Medicine
                cancer genomics, melanoma, computational biology and bioinformatics

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