A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia
Abstract
:1. Introduction
2. Results
2.1. Development of a TabNet Model to Predict Sorafenib Sensitivity in Leukemia
2.2. Shapley Additive Explanations (SHAP) Identify AXL and HTRA4 as Important Features
2.3. Sorafenib Activates Protein Kinase C Signaling
2.4. Inhibition of the Receptor Tyrosine Kinase FLT3 Results in Transcriptional Upregulation of AXL
2.5. An Inhibitor Targeting FLT3 Displays Synergy with AXL and PKC Inhibitors
3. Discussion
4. Materials and Methods
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Nasimian, A.; Al Ashiri, L.; Ahmed, M.; Duan, H.; Zhang, X.; Rönnstrand, L.; Kazi, J.U. A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia. Int. J. Mol. Sci. 2023, 24, 3830. https://doi.org/10.3390/ijms24043830
Nasimian A, Al Ashiri L, Ahmed M, Duan H, Zhang X, Rönnstrand L, Kazi JU. A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia. International Journal of Molecular Sciences. 2023; 24(4):3830. https://doi.org/10.3390/ijms24043830
Chicago/Turabian StyleNasimian, Ahmad, Lina Al Ashiri, Mehreen Ahmed, Hongzhi Duan, Xiaoyue Zhang, Lars Rönnstrand, and Julhash U. Kazi. 2023. "A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia" International Journal of Molecular Sciences 24, no. 4: 3830. https://doi.org/10.3390/ijms24043830