Abstract
Abstract Glioblastoma (GBM) is a devastating primary brain tumor with 5-year survival < 5%. CDKN2A deletion (~60%) and EGFR amplification (55–60%) mutations frequently co-occur in these tumors. EGFR is an attractive therapeutic target due to its mutational frequency and availability of multiple brain-penetrant tyrosine kinase inhibitors (TKI). Several EGFR TKI have failed clinically, due in part to acquired resistance. To mechanistically examine this type of resistance, we used genetically engineered mouse astrocytes harboring Cdkn2a deletion and EGFRvIII, a common (35%) activating mutation. Resistant cells were generated via chronic exposure to gefitinib or erlotinib, either in vitro or in vivo. Resistance to these first-generation EGFR TKI conferred cross resistance (up to 36-fold ΔIC50) to a panel of second- and third-generation TKI relative to sensitive parental lines. Moreover, integrated RNA sequencing (RNA-seq) and chemical proteomics (multiplexed inhibitor beads and mass spectrometry (MIB-MS)) showed that the kinase transcriptome and proteome were rewired in resistant cells: 113 of ~300 detected kinases were differentially expressed (p< 0.05). We then used these techniques to examine acute (≤ 48 h) kinome changes in both sensitive and resistant cells upon treatment with a CNS-penetrant, second-generation EGFR TKI, afatinib. Whereas exposure of treatment-naïve, sensitive cells to afatinib significantly rewired the kinome (120 differentially expressed kinases), the response of resistant cells to drug re-challenge was significantly blunted (13 differentially expressed kinases). A subset of expressed kinases (35 of 263) dynamically responded to afatinib in both sensitive and resistant cells. Overall, upregulated kinases include those implicated in the biology of gliomas (Bmx, Fgfr2) and of other cancers (Pdgfrb, Mapk3/4, Ddr1/2, Pdk2). These kinases thus represent putative druggable targets for dual inhibition therapy. Integrated kinome profiling using MIB-MS and RNA-seq in GBM models with defined mutational profiles provides a powerful framework to identify novel therapeutic targets that could significantly alter current treatment paradigms.
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