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      Targeting myeloid-derived suppressor cells to attenuate vasculogenic mimicry and synergistically enhance the anti-tumor effect of PD-1 inhibitor

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          Summary

          Myeloid-derived suppressor cells (MDSCs) enhance the proliferation of endothelial cells to stimulate angiogenesis. However, many aggressive malignant tumors do not have endothelial cell-dependent blood vessels in the early stage and instead generate microcirculation by forming vasculogenic mimicry (VM). To date, the relationship between MDSCs and tumor cells remains the focus of ongoing studies. In this work, MDSCs were co-cultured with mouse melanoma cells and can enhance proliferation and VM formation of melanoma cells. For MDSCs targeting, doxycycline (DOX) was found to selectively suppress PMN-MDSCs but has no influence on T cells. In addition, DOX pretreatment substantially reduced the promoting ability of MDSCs for the VM formation of B16-F10 cells. DOX also inhibited tumor growth and enhanced the antitumor activity of PD-1 inhibitors in C57BL6 and BALB/c mice subcutaneously inoculated with B16-F10 and 4T1 cells, respectively. In conclusion, the combination of DOX and PD-1 inhibitor could be an anticancer strategy.

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          Highlights

          • MDSCs accumulated in the B16-F10 tumor-bearing mice

          • MDSCs promote the formation of vasculogenic mimicry

          • Doxycycline selectively suppressed PMN-MDSCs

          • Doxycycline combined with PD-1 inhibitor significantly inhibited tumor growth

          Abstract

          Cancer; Genetics

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          Most cited references46

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          Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

          A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              Myeloid-derived suppressor cells as regulators of the immune system.

              Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells that expand during cancer, inflammation and infection, and that have a remarkable ability to suppress T-cell responses. These cells constitute a unique component of the immune system that regulates immune responses in healthy individuals and in the context of various diseases. In this Review, we discuss the origin, mechanisms of expansion and suppressive functions of MDSCs, as well as the potential to target these cells for therapeutic benefit.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                01 November 2021
                17 December 2021
                01 November 2021
                : 24
                : 12
                : 103392
                Affiliations
                [1 ]State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Nankai University, Tianjin 300350, China
                [2 ]Tianjin Key Laboratory of Molecular Drug Research, Tianjin International Joint Academy of Biomedicine, Tianjin 300450, China
                [3 ]Molecular Pathology Institute of Gastrointestinal Tumors, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining 272013, Shandong, China
                Author notes
                []Corresponding author tao.sun@ 123456nankai.edu.cn
                [4]

                These authors contributed equally

                [5]

                Lead contact

                Article
                S2589-0042(21)01363-8 103392
                10.1016/j.isci.2021.103392
                8605339
                34841231
                0bef0f80-2d20-4544-86ea-c619125568a9
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 8 June 2021
                : 21 August 2021
                : 28 October 2021
                Categories
                Article

                cancer,genetics
                cancer, genetics

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