Kinome

Abstract: Kinase inhibitors are a new class of therapeutics with a propensity to inhibit multiple targets. The biological consequences of multi-kinase activity are poorly defined, and an important step toward understanding the relationship between selectivity, efficacy and safety is the exploration of how inhibitors interact with the human kinome. We present interaction maps for 38 kinase inhibitors across a panel of 317 kinases representing >50% of the predicted human protein kinome. The data constitute the most comprehensive study of kinase inhibitor selectivity to date and reveal a wide diversity of interaction patterns. To enable a global analysis of the results, we introduce the concept of a selectivity score as a general tool to quantify and differentiate the observed interaction patterns. We further investigate the impact of panel size and find that small assay panels do not provide a robust measure of selectivity.

Abstract: We tested the interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome. Our data show that, as a class, type II inhibitors are more selective than type I inhibitors, but that there are important exceptions to this trend. The data further illustrate that selective inhibitors have been developed against the majority of kinases targeted by the compounds tested. Analysis of the interaction patterns reveals a class of 'group-selective' inhibitors broadly active against a single subfamily of kinases, but selective outside that subfamily. The data set suggests compounds to use as tools to study kinases for which no dedicated inhibitors exist. It also provides a foundation for further exploring kinase inhibitor biology and toxicity, as well as for studying the structural basis of the observed interaction patterns. Our findings will help to realize the direct enabling potential of genomics for drug development and basic research about cellular signaling.

Abstract: Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making.