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A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia
Ahmad Nasimian1,2, Lina Al Ashiri1,2, Mehreen Ahmed1,2
1Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, 22381 Lund, Sweden.
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.
Insights
Therapy resistance in acute myeloid leukemia (AML) can be predicted using a novel drug sensitivity model. AXL receptor tyrosine kinase and protein kinase C (PKC) signaling are identified as key mediators of sorafenib resistance in AML.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacogenomics
Background:
- Therapy resistance is a major obstacle in achieving long-term survival for cancer patients.
- During treatment, genes are upregulated, leading to drug tolerance and treatment failure.
Purpose of the Study:
- To develop a predictive model for sorafenib drug sensitivity in acute myeloid leukemia (AML).
- To identify key molecular features associated with drug resistance in AML.
Main Methods:
- Utilized highly variable genes and pharmacogenomic data for AML.
- Developed a drug sensitivity prediction model for sorafenib.
- Employed Shapley additive explanations (SHAP) to identify resistance-driving features.
- Analyzed patient samples and cell lines using peptide-based kinase profiling.
Main Results:
- Achieved over 80% accuracy in predicting sorafenib sensitivity.
- Identified AXL as a critical feature contributing to drug resistance.
- Observed enrichment of protein kinase C (PKC) signaling in resistant samples and cell lines.
- Demonstrated that inhibiting tyrosine kinase activity enhances AXL expression and CREB phosphorylation.
Conclusions:
- AXL plays a significant role in tyrosine kinase inhibitor resistance in AML.
- PKC activation is implicated as a signaling mediator in sorafenib resistance.
- Combined inhibition of AXL and PKC may overcome treatment resistance.
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