A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia

Abstract

Despite incredible progress in cancer treatment, therapy resistance remains the leading limiting factor for long−term survival. During drug treatment, several genes are transcriptionally upregulated to mediate drug tolerance. Using highly variable genes and pharmacogenomic data for acute myeloid leukemia (AML), we developed a drug sensitivity prediction model for the receptor tyrosine kinase inhibitor sorafenib and achieved more than 80% prediction accuracy. Furthermore, by using Shapley additive explanations for determining leading features, we identified AXL as an important feature for drug resistance. Drug−resistant patient samples displayed enrichment of protein kinase C (PKC) signaling, which was also identified in sorafenib−treated FLT3−ITD−dependent AML cell lines by a peptide−based kinase profiling assay. Finally, we show that pharmacological inhibition of tyrosine kinase activity enhances AXL expression, phosphorylation of the PKC−substrate cyclic AMP response element binding (CREB) protein, and displays synergy with AXL and PKC inhibitors. Collectively, our data suggest an involvement of AXL in tyrosine kinase inhibitor resistance and link PKC activation as a possible signaling mediator.

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“…Recent research endeavors utilizing disease-centric and drug-specific biological attributes have exhibited notable predictive efficacy. 4 , 5 , 6 , 7 , 8 Nevertheless, these methodological approaches necessitate substantial investment in terms of time and resources and demand a synergetic collaboration between researchers in the fields of biology and data science to ensure the effectiveness and rationality of the strategy.…”

A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase

“…Additionally, the integration of machine learning (ML) in drug discovery has revolutionized the identification and prediction of kinase inhibitors; including those targeting FLT3. Nasimian et al [18] demonstrated the potential of a machine learning-based model in predicting drug sensitivity, revealing crucial insights into AXL dependency in AML. Janssen et al [19] introduced the Drug Discovery Maps (DDM) model, employing algorithms like t-SNE to visualize and predict interactions across the kinase family, leading to the discovery of potent FLT3 inhibitors.…”