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      Classification of triple-negative breast cancers based on Immunogenomic profiling

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          Abstract

          Background

          Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.

          Methods

          Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification.

          Results

          We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K–Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes ( CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes ( IRF8 and SPI1) characteristic of the Immunity_L subtype.

          Conclusions

          The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

          Electronic supplementary material

          The online version of this article (10.1186/s13046-018-1002-1) contains supplementary material, which is available to authorized users.

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          Most cited references19

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          Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer

          Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogeneity regarding breast cancer subtype and crucial cancer-related pathways. Most of the non-cancer cells are immune cells, with three distinct clusters of T lymphocytes, B lymphocytes and macrophages. T lymphocytes and macrophages both display immunosuppressive characteristics: T cells with a regulatory or an exhausted phenotype and macrophages with an M2 phenotype. These results illustrate that the breast cancer transcriptome has a wide range of intratumoral heterogeneity, which is shaped by the tumour cells and immune cells in the surrounding microenvironment.
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            Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients.

            T-cell infiltration in estrogen receptor (ER)-negative breast tumours has been associated with longer survival. To investigate this association and the potential of tumour T-cell infiltration as a prognostic and predictive marker, we have conducted the largest study of T cells in breast cancer to date.
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              Combination Immunotherapy of MUC1 mRNA Nano-vaccine and CTLA-4 Blockade Effectively Inhibits Growth of Triple Negative Breast Cancer.

              Triple negative breast cancer (TNBC), which constitutes 10%-20% of all breast cancers, is associated with aggressive progression, a high rate of metastasis, and poor prognosis. The treatment of patients with TNBC remains a great clinical challenge. Preclinical reports support the combination immunotherapy of cancer vaccines and immune checkpoint blockades in non-immunogenic tumors. In this study, we constructed nanoparticles (NPs) to deliver an mRNA vaccine encoding tumor antigen MUC1 to dendritic cells (DCs) in lymph nodes to activate and expand tumor-specific T cells. An anti-CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) monoclonal antibody was combined with the mRNA vaccine to enhance the anti-tumor benefits. In vivo studies demonstrated that the NP-based mRNA vaccine, targeted to mannose receptors on DCs, could successfully express tumor antigen in the DCs of the lymph node; that the NP vaccine could induce a strong, antigen-specific, in vivo cytotoxic T lymphocyte response against TNBC 4T1 cells; and that combination immunotherapy of the vaccine and anti-CTLA-4 monoclonal antibody could significantly enhance anti-tumor immune response compared to the vaccine or monoclonal antibody alone. These data support both the NP as a carrier for delivery of mRNA vaccine and a potential combination immunotherapy of the NP-based mRNA vaccine and the CTLA-4 inhibitor for TNBC.
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                Author and article information

                Contributors
                yin.he@stu.cpu.edu.cn
                zehang.jiang@stu.cpu.edu.cn
                cac005@eng.ucsd.edu
                xiaosheng.wang@cpu.edu.cn
                Journal
                J Exp Clin Cancer Res
                J. Exp. Clin. Cancer Res
                Journal of Experimental & Clinical Cancer Research : CR
                BioMed Central (London )
                0392-9078
                1756-9966
                29 December 2018
                29 December 2018
                2018
                : 37
                : 327
                Affiliations
                [1 ]Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, Nanjing, 211198 China
                [2 ]Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, Nanjing, 211198 China
                [3 ]ISNI 0000 0000 9776 7793, GRID grid.254147.1, Big Data Research Institute, , China Pharmaceutical University, ; Nanjing, 211198 China
                [4 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Electrical and Computer Engineering, , University of California, San Diego, ; La Jolla, CA 92093 USA
                Author information
                http://orcid.org/0000-0002-7199-7093
                Article
                1002
                10.1186/s13046-018-1002-1
                6310928
                30594216
                9f480953-c1c9-4de6-9284-3f0a1260dc20
                © The Author(s). 2018

                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.

                History
                : 17 October 2018
                : 11 December 2018
                Funding
                Funded by: China Pharmaceutical University
                Award ID: 2632018YX01
                Award ID: 3150120001
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Oncology & Radiotherapy
                triple-negative breast cancer,tumor immunity,immunogenomic profiling,classification,machine learning

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