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Published on: September 20, 2016
Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction
Sara Latini1, Veronica Venafra1, Giorgia Massacci2
1Cellular and Molecular Biology, Department of Biology, University of Rome Tor Vergata, Rome, Italy.
Abstract:
Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
Insights
This study introduces a new computational approach for acute myeloid leukemia (AML) patients with FLT3-ITD mutations. The models predict treatment response and uncover resistance mechanisms, paving the way for precision medicine.
Area of Science:
- Computational Biology
- Oncology
- Systems Biology
Background:
- Personalized genomic analysis guides cancer therapy, but treatment matching fails for some acute myeloid leukemia (AML) patients with FLT3 internal tandem duplications (ITDs).
- Atypical FLT3-ITD mutations in the tyrosine kinase domain present a challenge for current precision medicine strategies in AML.
Purpose of the Study:
- To develop a systems-based strategy for personalized predictive modeling in AML with FLT3-ITD.
- To integrate multiparametric signaling pathway analysis, genomic, transcriptomic data, and prior network knowledge using Boolean formalism.
- To gain mechanistic insights into drug resistance and identify novel combinatorial treatment opportunities.
Main Methods:
- Developed a systems-based strategy integrating multiparametric signaling pathway analysis.
- Utilized patient-specific genomic and transcriptomic data with a prior knowledge signaling network.
- Employed a Boolean-based formalism to derive personalized predictive models for AML FLT3-ITD signaling.
Main Results:
- Derived personalized predictive models for the signaling landscape of AML FLT3-ITD cell lines and patients.
- Gained mechanistic insights into drug resistance mechanisms.
- Identified the JNK kinase pathway's crucial role in tyrosine kinase inhibitor response via cell cycle regulation in FLT3-ITD cells.
Conclusions:
- Patient-specific logic models can inform precision medicine approaches in AML.
- The developed strategy offers novel opportunities for combinatorial treatments in FLT3-ITD positive AML.
- Understanding signaling pathways like JNK is critical for overcoming drug resistance in AML.
