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  1. Artikel ; Online: Data integration and mechanistic modelling for breast cancer biology: Current state and future directions.

    Mo, Hanyi / Breitling, Rainer / Francavilla, Chiara / Schwartz, Jean-Marc

    Current opinion in endocrine and metabolic research

    2022  Band 24, Seite(n) None

    Abstract: Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for ... ...

    Abstract Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of 'big data' and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
    Sprache Englisch
    Erscheinungsdatum 2022-08-23
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ISSN 2451-9650
    ISSN (online) 2451-9650
    DOI 10.1016/j.coemr.2022.100350
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Spatially resolved phosphoproteomics reveals fibroblast growth factor receptor recycling-driven regulation of autophagy and survival.

    Watson, Joanne / Ferguson, Harriet R / Brady, Rosie M / Ferguson, Jennifer / Fullwood, Paul / Mo, Hanyi / Bexley, Katherine H / Knight, David / Howell, Gareth / Schwartz, Jean-Marc / Smith, Michael P / Francavilla, Chiara

    Nature communications

    2022  Band 13, Heft 1, Seite(n) 6589

    Abstract: Receptor Tyrosine Kinase (RTK) endocytosis-dependent signalling drives cell proliferation and motility during development and adult homeostasis, but is dysregulated in diseases, including cancer. The recruitment of RTK signalling partners during ... ...

    Abstract Receptor Tyrosine Kinase (RTK) endocytosis-dependent signalling drives cell proliferation and motility during development and adult homeostasis, but is dysregulated in diseases, including cancer. The recruitment of RTK signalling partners during endocytosis, specifically during recycling to the plasma membrane, is still unknown. Focusing on Fibroblast Growth Factor Receptor 2b (FGFR2b) recycling, we reveal FGFR signalling partners proximal to recycling endosomes by developing a Spatially Resolved Phosphoproteomics (SRP) approach based on APEX2-driven biotinylation followed by phosphorylated peptides enrichment. Combining this with traditional phosphoproteomics, bioinformatics, and targeted assays, we uncover that FGFR2b stimulated by its recycling ligand FGF10 activates mTOR-dependent signalling and ULK1 at the recycling endosomes, leading to autophagy suppression and cell survival. This adds to the growing importance of RTK recycling in orchestrating cell fate and suggests a therapeutically targetable vulnerability in ligand-responsive cancer cells. Integrating SRP with other systems biology approaches provides a powerful tool to spatially resolve cellular signalling.
    Mesh-Begriff(e) Receptor, Fibroblast Growth Factor, Type 2/metabolism ; Ligands ; Endosomes/metabolism ; Endocytosis/physiology ; Autophagy ; Fibroblast Growth Factor 10/metabolism
    Chemische Substanzen Receptor, Fibroblast Growth Factor, Type 2 (EC 2.7.10.1) ; Ligands ; Fibroblast Growth Factor 10
    Sprache Englisch
    Erscheinungsdatum 2022-11-03
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-34298-2
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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