27
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Unique signalling connectivity of FGFR3-TACC3 oncoprotein revealed by quantitative phosphoproteomics and differential network analysis

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The FGFR3-TACC3 fusion is an oncogenic driver in diverse malignancies, including bladder cancer, characterized by upregulated tyrosine kinase activity. To gain insights into distinct properties of FGFR3-TACC3 down-stream signalling, we utilised telomerase-immortalised normal human urothelial cell lines expressing either the fusion or wild-type FGFR3 (isoform IIIb) for subsequent quantitative proteomics and network analysis. Cellular lysates were chemically labelled with isobaric tandem mass tag reagents and, after phosphopeptide enrichment, liquid chromatography-high mass accuracy tandem mass spectrometry (LC-MS/MS) was used for peptide identification and quantification. Comparison of data from the two cell lines under non-stimulated and FGF1 stimulated conditions and of data representing physiological stimulation of FGFR3 identified about 200 regulated phosphosites. The identified phosphoproteins and quantified phosphosites were further analysed in the context of functional biological networks by inferring kinase-substrate interactions, mapping these to a comprehensive human signalling interaction network, filtering based on tissue-expression profiles and applying disease module detection and pathway enrichment methods. Analysis of our phosphoproteomics data using these bioinformatics methods combined into a new protocol—Disease Relevant Analysis of Genes On Networks (DRAGON)—allowed us to tease apart pathways differentially involved in FGFR3-TACC3 signalling in comparison to wild-type FGFR3 and to investigate their local phospho-signalling context. We highlight 9 pathways significantly regulated only in the cell line expressing FGFR3-TACC3 fusion and 5 pathways regulated only by stimulation of the wild-type FGFR3. Pathways differentially linked to FGFR3-TACC3 fusion include those related to chaperone activation and stress response and to regulation of TP53 expression and degradation that could contribute to development and maintenance of the cancer phenotype.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: not found

          Proteomics. Tissue-based map of the human proteome.

          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Molecular biology of bladder cancer: new insights into pathogenesis and clinical diversity.

              Urothelial carcinoma of the bladder comprises two long-recognized disease entities with distinct molecular features and clinical outcome. Low-grade non-muscle-invasive tumours recur frequently but rarely progress to muscle invasion, whereas muscle-invasive tumours are usually diagnosed de novo and frequently metastasize. Recent genome-wide expression and sequencing studies identify genes and pathways that are key drivers of urothelial cancer and reveal a more complex picture with multiple molecular subclasses that traverse conventional grade and stage groupings. This improved understanding of molecular features, disease pathogenesis and heterogeneity provides new opportunities for prognostic application, disease monitoring and personalized therapy.
                Bookmark

                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                28 November 2017
                25 October 2017
                : 8
                : 61
                : 102898-102911
                Affiliations
                1 Proteomics and Molecular Cell Dynamics, Center for Nephrology, School of Life and Medical Sciences, University College London, London NW3 2PF, United Kingdom
                2 Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
                3 Section of Molecular Oncology, Leeds Institute of Molecular Medicine, St James’s University Hospital, Leeds LS9 7TF, United Kingdom
                Author notes
                Correspondence to: Jasminka Godovac-Zimmermann, j.godovac-zimmermann@ 123456ucl.ac.uk
                [*]

                Joint first authors

                Article
                22048
                10.18632/oncotarget.22048
                5732698
                5a61e4d3-53b0-440e-9f5f-4bc39696f298
                Copyright: © 2017 Lombardi et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 August 2017
                : 3 October 2017
                Categories
                Research Paper

                Oncology & Radiotherapy
                fgfr-tacc-fusion,cancer,quantitative phosphoproteomics,signaling pathways,network analysis

                Comments

                Comment on this article