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      Elucidating Fibroblast Growth Factor–Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling


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          Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.

          Graphical Abstract


          • Treatment with different FGFs activate distinct signaling pathways in cancer cells.

          • A targeted kinome activity assay enables the quantification of kinome dynamics.

          • Different FGF stimulations generate disparate kinome dynamics.

          • Logic-based dynamic modeling provides biological pathway validation.

          • FGF2 treatment induces BRAF activation and FGF4 results in ARAF activation.

          In Brief

          We provide an extensive overview of FGF-regulated kinome signaling in breast cancer cells, enabled via a custom-made targeted kinome activity assay. We apply logic-based dynamic modeling to verify literature-derived biological pathways. This in-depth comparison between FGF2, FGF3, FGF4, FGF10, and FGF19 signaling revealed differential response and involvement of kinases hitherto undescribed in the FGF context. Moreover, we expanded on the existing FGF-signaling knowledge, for example, by revealing differential activation of ARAF and BRAF for FGF4 and FGF2, respectively.

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          Most cited references124

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Hallmarks of Cancer: The Next Generation

              The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

                Author and article information

                Mol Cell Proteomics
                Mol Cell Proteomics
                Molecular & Cellular Proteomics : MCP
                American Society for Biochemistry and Molecular Biology
                15 June 2023
                August 2023
                15 June 2023
                : 22
                : 8
                : 100594
                [1 ]Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
                [2 ]Netherlands Proteomics Center, Utrecht, The Netherlands
                [3 ]Division of Molecular and Cellular Function, School of Biological Science, and Manchester Breast Centre, Manchester Cancer Research Centre, Faculty of Biology Medicine and Health (FBMH), The University of Manchester, Manchester, UK
                Author notes
                []For correspondence: Maarten Altelaar m.altelaar@ 123456uu.nl
                S1535-9476(23)00105-6 100594
                © 2023 The Authors

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

                : 1 February 2023
                : 2 May 2023

                Molecular biology
                fibroblast,growth factors,kinome,signaling,phosphoproteomics,breast cancer,modeling,targeted ms,srm,mapk


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