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      Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping

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          Abstract

          Extracellular vesicles (EVs) are a potential source of disease-associated biomarkers for diagnosis. In breast cancer, comprehensive analyses of EVs could yield robust and reliable subtype-specific biomarkers that are still critically needed to improve diagnostic routines and clinical outcome. Here, we show that proteome profiles of EVs secreted by different breast cancer cell lines are highly indicative of their respective molecular subtypes, even more so than the proteome changes within the cancer cells. Moreover, we detected molecular evidence for subtype-specific biological processes and molecular pathways, hyperphosphorylated receptors and kinases in connection with the disease, and compiled a set of protein signatures that closely reflect the associated clinical pathophysiology. These unique features revealed in our work, replicated in clinical material, collectively demonstrate the potential of secreted EVs to differentiate between breast cancer subtypes and show the prospect of their use as non-invasive liquid biopsies for diagnosis and management of breast cancer patients.

          Abstract

          Stamatia Rontogianni et al. show that the proteomic profiles of extracellular vesicles secreted by different classes of breast cancer cell lines can be used as biomarkers of their respective subtype. They show these provide molecular evidence for subtype-specific biological processes and pathways.

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          Most cited references36

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          ExoCarta: A Web-Based Compendium of Exosomal Cargo.

          Exosomes are membranous vesicles that are released by a variety of cells into the extracellular microenvironment and are implicated in intercellular communication. As exosomes contain RNA, proteins and lipids, there is a significant interest in characterizing the molecular cargo of exosomes. Here, we describe ExoCarta (http://www.exocarta.org), a manually curated Web-based compendium of exosomal proteins, RNAs and lipids. Since its inception, the database has been highly accessed (>54,000 visitors from 135 countries). The current version of ExoCarta hosts 41,860 proteins, >7540 RNA and 1116 lipid molecules from more than 286 exosomal studies annotated with International Society for Extracellular Vesicles minimal experimental requirements for definition of extracellular vesicles. Besides, ExoCarta features dynamic protein-protein interaction networks and biological pathways of exosomal proteins. Users can download most often identified exosomal proteins based on the number of studies. The downloaded files can further be imported directly into FunRich (http://www.funrich.org) tool for additional functional enrichment and interaction network analysis.
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            Breast cancer intrinsic subtype classification, clinical use and future trends.

            Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.
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              Biological subtypes of breast cancer: Prognostic and therapeutic implications.

              Breast cancer is a heterogeneous complex of diseases, a spectrum of many subtypes with distinct biological features that lead to differences in response patterns to various treatment modalities and clinical outcomes. Traditional classification systems regarding biological characteristics may have limitations for patient-tailored treatment strategies. Tumors with similar clinical and pathological presentations may have different behaviors. Analyses of breast cancer with new molecular techniques now hold promise for the development of more accurate tests for the prediction of recurrence. Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics. The present review summarizes current knowledge in breast cancer molecular biology, focusing on novel prognostic and predictive factors.
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                Author and article information

                Contributors
                w.wu1@uu.nl
                m.altelaar@uu.nl
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                3 September 2019
                3 September 2019
                2019
                : 2
                : 325
                Affiliations
                [1 ]ISNI 0000000120346234, GRID grid.5477.1, Biomolecular Mass Spectrometry and Proteomics Group, Utrecht Institute for Pharmaceutical Science, , Utrecht University, ; Utrecht, The Netherlands
                [2 ]Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
                [3 ]GRID grid.430814.a, Division of Molecular Pathology, , The Netherlands Cancer Institute, ; 1066 CX Amsterdam, The Netherlands
                [4 ]GRID grid.430814.a, Department of Pathology, , The Netherlands Cancer Institute, ; 1066 CX Amsterdam, The Netherlands
                [5 ]GRID grid.430814.a, Mass Spectrometry and Proteomics Facility, , The Netherlands Cancer Institute, ; 1066 CX Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-7335-5836
                http://orcid.org/0000-0002-1092-603X
                Article
                570
                10.1038/s42003-019-0570-8
                6722120
                31508500
                d75ba949-67c5-4922-8648-27d212b1f9e7
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 July 2019
                : 1 August 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research);
                Award ID: 723.012.102
                Award ID: 184.032.201
                Award Recipient :
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                Custom metadata
                © The Author(s) 2018

                breast cancer,mass spectrometry,proteomic analysis
                breast cancer, mass spectrometry, proteomic analysis

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