Competitive Kinase Enrichment Proteomics Reveals that Abemaciclib Inhibits GSK3{beta} and Activates WNT Signaling

The cellular and organismal phenotypic response to a small-molecule kinase inhibitor is defined collectively by the inhibitor's targets and their functions. The selectivity of small-molecule kinase inhibitors is commonly determined in vitro, using purified kinases and substrates. Recently, competitive chemical proteomics has emerged as a complementary, unbiased, cell-based methodology to define the target landscape of kinase inhibitors. Here, we evaluated and optimized a competitive multiplexed inhibitor bead mass spectrometry (MIB/MS) platform using cell lysates, live cells, and treated mice. Several clinically active kinase inhibitors were profiled, including trametinib, BMS-777607, dasatinib, abemaciclib, and palbociclib. MIB/MS competition analyses of the cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors abemaciclib and palbociclib revealed overlapping and unique kinase targets. Competitive MIB/MS analysis of abemaciclib revealed 83 target kinases, and dose–response MIB/MS profiling revealed glycogen synthase kinase 3 alpha and beta (GSK3α and β) and Ca2+/calmodulin-dependent protein kinase II delta and gamma (CAMKII and ) as the most potently inhibited. Cell-based and in vitro kinase assays show that in contrast to palbociclib, abemaciclib directly inhibits GSK3α/β and CAMKII/ kinase activity at low nanomolar concentrations. GSK3β phosphorylates β-catenin to suppress WNT signaling, while abemaciclib (but not palbociclib or ribociclib) potently activates β-catenin-dependent WNT signaling. These data illustrate the power of competitive chemical proteomics to define kinase target specificities for kinase inhibitors, thus informing clinical efficacy, dose-limiting toxicities, and drug-repurposing efforts.

Implications: This study uses a rapid and quantitative proteomics approach to define inhibitor-target data for commonly administered therapeutics and provides a cell-based alternative to in vitro kinome profiling. Mol Cancer Res; 16(2); 333–44. ©2017 AACR.

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