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      Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

      1 , 15 , , 2 , 14 , 3 , 4 , 5 , 5 , 5 , 6 , 6 , 1 , 1 , 1 , 7 , 3 , 13 , 3 , 8 , 9 , 10 , 11 , 3 , 3 , 8 , 7 , 12 , 14 , 8 , 12 , 2 , 13 , 14 , 4 , 5 , 11 , 12 , 1 , 1 , 3 ,
      Genome Biology
      BioMed Central
      Tumor immune microenvironment, Checkpoint blockade, Clear cell renal cell carcinoma (ccRCC), Computational deconvolution, Cancer immunotherapy

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          Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.


          We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8 + T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.


          Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-1092-z) contains supplementary material, which is available to authorized users.

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          Most cited references33

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
            • Record: found
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            • Article: not found

            Random forest: a classification and regression tool for compound classification and QSAR modeling.

            A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
              • Record: found
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              Angiogenesis in cancer, vascular, rheumatoid and other disease.

              J Folkman (1995)
              Recent discoveries of endogenous negative regulators of angiogenesis, thrombospondin, angiostatin and glioma-derived angiogenesis inhibitory factor, all associated with neovascularized tumours, suggest a new paradigm of tumorigenesis. It is now helpful to think of the switch to the angiogenic phenotype as a net balance of positive and negative regulators of blood vessel growth. The extent to which the negative regulators are decreased during this switch may dictate whether a primary tumour grows rapidly or slowly and whether metastases grow at all.

                Author and article information

                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                17 November 2016
                17 November 2016
                : 17
                : 231
                [1 ]Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [2 ]Molecular Pharmacology and Chemistry Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [3 ]Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [4 ]Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA USA
                [6 ]Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [7 ]Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [8 ]Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [9 ]Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
                [10 ]Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
                [11 ]Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [12 ]Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [13 ]Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
                [14 ]Weill Cornell Medical College, New York, NY USA
                [15 ]Present address: Swim Across America/Ludwig Collaborative Laboratory, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
                © The Author(s). 2016

                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.

                : 1 August 2016
                : 26 October 2016
                Funded by: MSKCC Department of Surgery
                Funded by: Stephen P. Hanson Family Fund Fellowship in Kidney Cancer
                Funded by: Sidney Kimmel Center for Prostate and Urologic Cancers
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: U24CA143840
                Award ID: U24CA143840
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32GM007739
                Award ID: T32CA082088-15
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007052, Memorial Sloan-Kettering Cancer Center;
                Award ID: Core Grant P30 CA008748
                Award Recipient :
                Funded by: MSKCC Cycle for Survival
                Funded by: RBRF
                Award ID: 13-04-40279-Н
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RO1CA55349
                Award ID: P01CA23766
                Award Recipient :
                Funded by: MSK Translational Kidney Cancer Research Program
                Funded by: FundRef http://dx.doi.org/10.13039/100009859, Geoffrey Beene Cancer Research Center;
                Custom metadata
                © The Author(s) 2016

                tumor immune microenvironment,checkpoint blockade,clear cell renal cell carcinoma (ccrcc),computational deconvolution,cancer immunotherapy


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