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  • 808118-40-3 br Phosphatase overexpression can drive tumor pr

    2020-08-12


    Phosphatase overexpression can drive tumor progression, but the underlying signaling mechanisms have been unclear (Ju-lien et al., 2011). Our data indicate that rather than directly acti-vating a cancer-driving signaling pathway, the overexpression of ERK-specific phosphatases modulates signaling dynamics, resulting in a prolonged proliferative signal in cells. This supports the suggestion that phosphatases should be considered thera-peutic targets for cancer treatment (Bollu et al., 2017; Julien et al., 2011; Low and Zhang, 2016).
    The MAPK-ERK pathway reactivation induced by protein overexpression is one cause of drug resistance to BRAF inhibi-tors in melanoma (Johannessen et al., 2010). Therapies that concurrently inhibit BRAF and MEK have been developed to simultaneously target the active BRAF signal and the MAPK-ERK reactivation signal. However, drug resistance still occurs (Carlino et al., 2014; Eroglu and Ribas, 2016). In our kinome and phosphatome analysis, we discovered a group of tyrosine 
    kinases, including SRC, FES, YES1, and BLK, that led to hy-per-ERK activation independent of MEK activity, suggesting a mechanism underlying drug resistance to the combined BRAF and MEK inhibition in melanoma patients with BRAF mutations. The identified kinases could be used as biomarkers to predict the drug response to BRAF-MEK combined inhibition and to screen for patients to be treated with alternative therapies. Compared to previous population-based assays (Johannessen et al., 2010, 2013), our screening method is more sensitive and robust in identifying drug resistance-related protein overexpression, as it assesses signaling variances over a large POI range and can be applied to highly heterogeneous samples.
    Our analysis has several limitations. First, the measured effects of overexpression may be indirect; for example, protein overex-pression may lead to cellular stress that activates MAPK-p38 or MAPK-JNK cascades. However, even if indirect, these signaling responses may be typical of such overexpression in diseased conditions. Second, our mass-cytometry-based analysis used 808118-40-3 targeting 30 specific phosphorylation sites. This antibody panel does, however, cover the most critical and informative phosphorylation sites known to be involved in the cancer-related signaling network. Third, GFP-tag can disrupt the localization of a kinase or phosphatase. In a previous study, we cross-validated our results with multiple protein tagging sys-tems and showed that perturbations on overexpression effects due to the tag were rare (Lun et al., 2017). Fourth, the catalytic functionality of many phosphatases requires the co-presence of a phosphatase catalytic subunit and a phosphatase regulatory subunit (Chen et al., 2017). Individually overexpressing one of these subunits may not result in phosphatase activation; rather, it may affect the kinetics of related dephosphorylating reaction in cells, and this is what we characterized in the present study.
    In summary, we demonstrated, in a human kinome- and phos-phatome-scale analysis, how the overexpression of individual signaling proteins modulates signaling networks in an abun-dance-dependent manner and how the provided datasets can reveal biological insights underlying diseased conditions. Our data established that protein expression levels can result in different signaling states in a population of cells treated identi-cally. Our analysis expands the functional classification of the human kinases and phosphatases and suggests 208 signaling relationships that can be interrogated to improve our under-standing of signaling causality and network structure. Our data are also suitable for the inference of signaling pathway kinetics using mathematical models and for the development of network reconstruction methods.
    STAR+METHODS
    Detailed methods are provided in the online version of this paper and include the following:
    d KEY RESOURCES TABLE
    d CONTACT FOR REAGENT AND RESOURCE SHARING d EXPERIMENTAL MODEL AND SUBJECT DETAILS
    d METHOD DETAILS B Cloning
    B HEK293T cell transfection and stimulation
    B A375 cell transfection
    B Kinase inhibition with small molecules B Methanol permeabilization
    B Antibody conjugation
    B Barcoding and staining protocol B Mass cytometry analysis
    d QUANTIFICATION AND STATISTICAL ANALYSIS B Data preprocessing and BP-R2 analysis
    B Hierarchical clustering B t-SNE analysis
    B Functional enrichment and association analysis using STRING database
    B Shortest signed directed path analysis using OmniPath B Shape-based clustering
    B Selection of strong signaling dynamic influencing POIs
    B Signaling amplitudes analysis