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    Dataset describing the development, optimization and application of SRM/MRM based targeted proteomics strategy for quantification of potential biomarkers of EGFR TKI sensitivity

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    Author
    Awasthi, S.
    Maity, T.
    Oyler, B.L.
    Date
    2018
    Journal
    Data in Brief
    Publisher
    Elsevier Inc.
    Type
    Article
    
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    See at
    https://dx.doi.org/10.1016/j.dib.2018.04.086
    Abstract
    The data presented here describes the use of targeted proteomic assays to quantify potential biomarkers of Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) sensitivity in lung adenocarcinoma and is related to the research article: "Quantitative targeted proteomic analysis of potential markers of tyrosine kinase inhibitor (TKI) sensitivity in EGFR mutated lung adenocarcinoma" [1]. This article describes the data associated with liquid chromatography coupled to multiple reaction monitoring (LC-MRM) method development which includes selection of an optimal transition list, retention time prediction and building of reverse calibration curves. Sample preparation and optimization which includes phosphotyrosine peptide enrichment via a combination of pan-phosphotyrosine antibodies is described. The dataset also consists of figures, tables and Excel files describing the quantitative results of testing these optimized methods in two lung adenocarcinoma cell lines with EGFR mutations. Copyright 2018
    Sponsors
    This research was supported by the Intramural Research Program of the NIH , (ZIABC011410) Center for Cancer Research, National Cancer Institute (U.G.). Transparency document
    Keyword
    SRM/MRM
    Adenocarcinoma of the Lung
    Biomarkers
    ErbB Receptors
    Proteomics
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047458198&doi=10.1016%2fj.dib.2018.04.086&partnerID=40&md5=136e0a9569c393436fc99ad4383b623f; http://hdl.handle.net/10713/9149
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.dib.2018.04.086
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    UMB Open Access Articles 2018

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