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

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.

Automated summary

Despite progress in cancer treatment, therapy resistance remains a major obstacle for long-term survival. By using highly variable genes and pharmacogenomic data, a drug sensitivity prediction model for the tyrosine kinase inhibitor sorafenib was developed with over 80% accuracy. AXL was identified as an important feature for drug resistance and PKC signaling was found to be enriched in drug-resistant patient samples. Inhibition of tyrosine kinase activity enhanced AXL expression and phosphorylation of the PKC-substrate CREB protein, showing synergy with AXL and PKC inhibitors.