Proteomic Analysis Defines Kinase Taxonomies Specific for Subtypes of Breast Cancer
作者
Kyla Collins,Timothy J. Stuhlmiller,Jon S. Zawistowski,Michael P. East,Trang T. Pham,Claire R. Hall,Daniel R. Goulet,Samantha M. Bevill,Steven P. Angus,Sara H. Velarde,Noah Sciaky,Tudor I. Oprea,Lee M. Graves,Gary L. Johnson,Shawn M. Gomez
出处
期刊: [Cold Spring Harbor Laboratory] 日期:2017-04-01被引量:3
标识
DOI:10.1101/122739
摘要
Abstract Multiplexed small molecule inhibitors covalently bound to Sepharose beads (MIBs) were used to capture functional kinases in luminal, HER2-enriched and triple negative, basal-like and claudin-low breast cancer cell lines and tumors. Kinase MIB-binding profiles at baseline without perturbation proteomically distinguished the four breast cancer subtypes. Kinases lacking defined functions in breast cancer were highly represented in the MIB-binding taxonomies. We show that these understudied kinases, whose disease associations and pharmacology are generally unexplored, are integrated in kinase signaling subnetworks with kinases that have been previously well characterized in breast cancer. Computationally it was possible to define subtypes using profiles of less than 50 of the more than 300 kinases bound to MIBs that included understudied as well as metabolic and lipid kinases. Furthermore, analysis of MIB-binding profiles established potential functional annotations for these understudied kinases. Thus, comprehensive MIBs-based capture of kinases provides a unique proteomics-based method for integration of poorly characterized kinases of the understudied kinome into functional subnetworks in breast cancer cells and tumors that is not possible using genomic strategies. The MIB-binding profiles readily defined subtype-selective differential adaptive kinome reprogramming in response to targeted kinase inhibition, demonstrating how MIB profiles can be used in determining dynamic kinome changes that result in subtype selective phenotypic state changes.