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      Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study

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          ABSTRACT

          Treatment of ovarian cancer (OC) remains the biggest challenge among gynecological malignancies. Immune checkpoint blockade therapy is promising in many cancers but shows low response rates in OC because of its heterogeneity. Although the biological and molecular heterogeneity of OC has been extensively investigated, heterogeneity of immune microenvironment remains elusive. We have collected the expression profiles of 3071 OC patients from 22 publicly available datasets. CIBERSORT was applied to infer the infiltration fraction of 22 immune cells among 2086 patients with CIBERSORT P < .05. We then explored the heterogeneity landscape of immune microenvironment in OC at three levels (immune infiltration, prognostic relevance of immune infiltration, immune checkpoint expression patterns). Multivariable Cox regression model was used to investigate the associations between survival risk and immune infiltration. Constructed immune risk score stratified patients with significantly different survival risk (HR: 1.47, 95% CI: 1.31–1.66, P < .0001). The immune infiltration landscape, prognostic relevance of immune cells, and expression patterns of 79 immune checkpoints exhibited remarkable clinicopathological heterogeneity. For instance, M1 macrophages were significantly associated with better outcomes among patients with high-grade, late-stage, type-II OC (HR: 0.77–0.83), and worse outcomes among patients with type-I OC (HR: 1.78); M2 macrophages were significantly associated with worse outcomes among patients with high-grade, type-II OC (HR: 1.14–1.17); Neutrophils were significantly associated with worse outcomes among patients with high-grade, late-stage, type-I OC (HR: 1.14–1.73). The heterogeneous landscape of immune microenvironment presented in this study provided new insights into prognostic prediction and tailored immunotherapy of OC.

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          Most cited references20

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          Transforming growth factor-beta regulation of immune responses.

          Transforming growth factor-beta (TGF-beta) is a potent regulatory cytokine with diverse effects on hemopoietic cells. The pivotal function of TGF-beta in the immune system is to maintain tolerance via the regulation of lymphocyte proliferation, differentiation, and survival. In addition, TGF-beta controls the initiation and resolution of inflammatory responses through the regulation of chemotaxis, activation, and survival of lymphocytes, natural killer cells, dendritic cells, macrophages, mast cells, and granulocytes. The regulatory activity of TGF-beta is modulated by the cell differentiation state and by the presence of inflammatory cytokines and costimulatory molecules. Collectively, TGF-beta inhibits the development of immunopathology to self or nonharmful antigens without compromising immune responses to pathogens. This review highlights the findings that have advanced our understanding of TGF-beta in the immune system and in disease.
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            The Immune Landscape of Cancer

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              Neutrophils facilitate ovarian cancer premetastatic niche formation in the omentum

              Metastasis of ovarian cancer frequently involves the omentum and has been described as a passive process that is governed by peritoneal fluid dynamics. Lee et al. show that metastatic tropism of ovarian cancer is actively orchestrated through the induction, by early-stage tumors, of neutrophil influx and chromatin extrusion in the premetastatic omental niche.
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                Author and article information

                Journal
                Oncoimmunology
                Oncoimmunology
                KONI
                koni20
                Oncoimmunology
                Taylor & Francis
                2162-4011
                2162-402X
                2020
                30 April 2020
                30 April 2020
                : 9
                : 1
                : 1760067
                Affiliations
                [a ]Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Peoples Republic of China
                [b ]Department of Computer Science, City University of Hong Kong , Kowloon Tong, Hong Kong
                [c ]Department of Biomedical Engineering, City University of Hong Kong , Kowloon Tong, Hong Kong
                Author notes
                CONTACT Shuai Cheng Li shuaicli@ 123456cityu.edu.hk ;
                Qinglei Gao qingleigao@ 123456hotmail.com Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , 1095 Jiefang Anv., Wuhan, Peoples Republic of China
                [*]

                These authors contributed equally to this work.

                Article
                1760067
                10.1080/2162402X.2020.1760067
                7199814
                32391193
                caa987ca-54b3-4de0-a46a-efb16fcfec28
                © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 October 2019
                : 2 April 2020
                : 3 April 2020
                Page count
                Figures: 5, Tables: 3, References: 39, Pages: 12
                Funding
                Funded by: the National Science Foundation of China
                Award ID: 81472783
                Funded by: the National Science Foundation of China
                Award ID: 81772787
                Funded by: the Fundamental Research Funds for the Central Universities
                Award ID: 2017JYCXJJ025
                This work was supported by the National Science Foundation of China under Grant 81472783; National Key Research Development Program of China under Grant 2016YFC0902901; and the Fundamental Research Funds for the Central Universities under Grant 2017JYCXJJ025; the National Science Foundation of China [81772787].
                Categories
                Original Research

                Immunology
                ovarian cancer,immune infiltration,prognosis,heterogeneity,immunotherapy
                Immunology
                ovarian cancer, immune infiltration, prognosis, heterogeneity, immunotherapy

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