Proteomic analysis defines kinase taxonomies specific for subtypes of breast cancer
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Kyla A.L. Collins1,*, Timothy J. Stuhlmiller2,3,*, Jon S. Zawistowski2,3, Michael P. East2,3, Trang T. Pham2,3, Claire R. Hall4, Daniel R. Goulet2,3, Samantha M. Bevill2,3, Steven P. Angus2,3, Sara H. Velarde2,3, Noah Sciaky2, Tudor I. Oprea5,6, Lee M. Graves2,3, Gary L. Johnson2,3 and Shawn M. Gomez1,2,4
1Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
2Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
3Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
4Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27514, USA
5Translational Informatics Division, School of Medicine, University of New Mexico, Albuquerque, NM 87106, USA
6UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131, USA
*These authors contributed equally to this work
Gary L. Johnson, email: [email protected]
Shawn M. Gomez, email: [email protected]
Keywords: cancer biology; proteomics; kinase signaling
Received: September 27, 2017 Accepted: January 19, 2018 Epub: January 29, 2018 Published: March 20, 2018
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. Understudied kinases, whose disease associations and pharmacology are generally unexplored, were highly represented in MIB-binding taxonomies and are integrated into 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.
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