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      Prognostic significance and immune correlates of CD73 expression in renal cell carcinoma

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          Abstract

          Background

          CD73–adenosine signaling in the tumor microenvironment is immunosuppressive and may be associated with aggressive renal cell carcinoma (RCC). We investigated the prognostic significance of CD73 protein expression in RCC leveraging nephrectomy samples. We also performed a complementary analysis using The Cancer Genome Atlas (TCGA) dataset to evaluate the correlation of CD73 (ecto-5′-nucleotidase ( NT5E), CD39 (ectonucleoside triphosphate diphosphohydrolase 1 ( ENTPD1)) and A2 adenosine receptor (A2AR; ADORA2A) transcript levels with markers of angiogenesis and antitumor immune response.

          Methods

          Patients with RCC with available archived nephrectomy samples were eligible for inclusion. Tumor CD73 protein expression was assessed by immunohistochemistry and quantified using a combined score (CS: % positive cells×intensity). Samples were categorized as CD73 negative (CS=0), CD73 low or CD73 high (< and ≥median CS, respectively). Multivariable Cox regression analysis compared disease-free survival (DFS) and overall survival (OS) between CD73 expression groups. In the TCGA dataset, samples were categorized as low, intermediate and high NT5E, ENTPD1 and ADORA2A gene expression groups. Gene expression signatures for infiltrating immune cells, angiogenesis, myeloid inflammation, and effector T-cell response were compared between NT5E, ENTPD1 and ADORA2A expression groups.

          Results

          Among the 138 patients eligible for inclusion, ‘any’ CD73 expression was observed in 30% of primary tumor samples. High CD73 expression was more frequent in patients with M1 RCC (29% vs 12% M0), grade 4 tumors (27% vs 13% grade 3 vs 15% grades 1 and 2), advanced T-stage (≥T3: 22% vs T2: 19% vs T1: 12%) and tumors with sarcomatoid histology (50% vs 12%). In the M0 cohort (n=107), patients with CD73 high tumor expression had significantly worse 5-year DFS (42%) and 10-year OS (22%) compared with those in the CD73 negative group (DFS: 75%, adjusted HR: 2.7, 95% CI 1.3 to 5.9, p=0.01; OS: 64%, adjusted HR: 2.6, 95% CI 1.2 to 5.8, p=0.02) independent of tumor stage and grade. In the TCGA analysis, high NT5E expression was associated with significantly worse 5-year OS (p=0.008). NT5E and ENTPD1 expression correlated with higher regulatory T cell (Treg) signature, while ADORA2A expression was associated with increased Treg and angiogenesis signatures.

          Conclusions

          High CD73 expression portends significantly worse survival outcomes independent of stage and grade. Our findings provide compelling support for targeting the immunosuppressive and proangiogenic CD73–adenosine pathway in RCC.

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          Most cited references39

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          HTSeq—a Python framework to work with high-throughput sequencing data

          Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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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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              Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma

              Nivolumab plus ipilimumab produced objective responses in patients with advanced renal-cell carcinoma in a pilot study. This phase 3 trial compared nivolumab plus ipilimumab with sunitinib for previously untreated clear-cell advanced renal-cell carcinoma.
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                Author and article information

                Journal
                J Immunother Cancer
                J Immunother Cancer
                jitc
                jitc
                Journal for Immunotherapy of Cancer
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2051-1426
                2020
                11 November 2020
                : 8
                : 2
                : e001467
                Affiliations
                [1 ]University of Oklahoma Health Sciences Center, Stephenson Cancer Center , Oklahoma City, Oklahoma, USA
                [2 ]departmentLank Center for Genitourinary Oncology , Dana-Farber Cancer Institute , Boston, Massachusetts, USA
                [3 ]University of Utah, Huntsman Cancer Institute , Salt Lake City, Utah, USA
                [4 ]departmentDepartment of Data Sciences , Dana Farber Cancer Institute , Boston, Massachusetts, USA
                [5 ]Brigham and Women's Hospital , Boston, Massachusetts, USA
                [6 ]Beth Israel Deaconess Medical Center , Boston, Massachusetts, USA
                [7 ]Oklahoma Medical Research Foundation , Oklahoma City, Oklahoma, USA
                [8 ]Dana-Farber Cancer Institute , Boston, Massachusetts, USA
                Author notes
                [Correspondence to ] Dr Lauren C Harshman; lcharshman@ 123456gmail.com

                AT and EL are joint first authors.

                Author information
                http://orcid.org/0000-0002-5198-0673
                http://orcid.org/0000-0002-2340-7415
                http://orcid.org/0000-0002-9201-3217
                http://orcid.org/0000-0003-1076-0428
                http://orcid.org/0000-0002-7636-1588
                Article
                jitc-2020-001467
                10.1136/jitc-2020-001467
                7661372
                33177176
                bc65746f-b272-44a5-87ff-3f28ed2bba56
                © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See https://creativecommons.org/licenses/by/4.0/.

                History
                : 02 October 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P50 CA101942
                Categories
                Clinical/Translational Cancer Immunotherapy
                1506
                2435
                Original research
                Custom metadata
                unlocked

                adenosine,kidney neoplasms,immunotherapy,biomarkers,tumor,gene expression profiling

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