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Published on: May 1, 2015
Network-Based Analysis of Bortezomib Pharmacodynamic Heterogeneity in Multiple Myeloma Cells
Vidya Ramakrishnan1, Donald E Mager2
1Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York.
Abstract:
The objective of this study is to evaluate the heterogeneity in pharmacodynamic response in four in vitro multiple myeloma cell lines to treatment with bortezomib, and to assess whether such differences are associated with drug-induced intracellular signaling protein dynamics identified via a logic-based network modeling approach. The in vitro pharmacodynamic-efficacy of bortezomib was evaluated through concentration-effect and cell proliferation dynamical studies in U266, RPMI8226, MM.1S, and NCI-H929 myeloma cell lines. A Boolean logic-based network model incorporating intracellular protein signaling pathways relevant to myeloma cell growth, proliferation, and apoptosis was developed based on information available in the literature and used to identify key proteins regulating bortezomib pharmacodynamics. The time-course of network-identified proteins was measured using the MAGPIX protein assay system. Traditional pharmacodynamic modeling endpoints revealed variable responses of the cell lines to bortezomib treatment, classifying cell lines as more sensitive (MM.1S and NCI-H929) and less sensitive (U266 and RPMI8226). Network centrality and model reduction identified key proteins (e.g., phosphorylated nuclear factor-κB, phosphorylated protein kinase B, phosphorylated mechanistic target of rapamycin, Bcl-2, phosphorylated c-Jun N-terminal kinase, phosphorylated p53, p21, phosphorylated Bcl-2-associated death promoter, caspase 8, and caspase 9) that govern bortezomib pharmacodynamics. The corresponding relative expression (normalized to 0-hour untreated-control cells) of proteins demonstrated a greater magnitude and earlier onset of stimulation/inhibition in cells more sensitive (MM.1S and NCI-H929) to bortezomib-induced cell death at 20 nM, relative to the less sensitive cells (U266 and RPMI8226). Overall, differences in intracellular signaling appear to be associated with bortezomib pharmacodynamic heterogeneity, and key proteins may be potential biomarkers to evaluate bortezomib responses.
Insights
This study reveals that differences in intracellular signaling pathways explain varied responses to bortezomib in multiple myeloma cell lines. Key proteins identified may serve as biomarkers for predicting bortezomib efficacy.
Area of Science:
- Pharmacology
- Systems Biology
- Oncology
Background:
- Multiple myeloma treatment response varies significantly among patients.
- Bortezomib is a proteasome inhibitor used in multiple myeloma therapy.
- Understanding drug response heterogeneity is crucial for personalized medicine.
Purpose of the Study:
- To investigate heterogeneity in bortezomib pharmacodynamics across four multiple myeloma cell lines.
- To correlate cellular response differences with intracellular signaling protein dynamics.
- To identify key regulatory proteins governing bortezomib response using network modeling.
Main Methods:
- In vitro pharmacodynamic-efficacy studies using concentration-effect and cell proliferation assays.
- Development of a Boolean logic-based network model of myeloma cell signaling pathways.
- Quantification of key protein time-courses using the MAGPIX protein assay system.
- Network analysis to identify proteins critical for bortezomib pharmacodynamics.
Main Results:
- Differential sensitivity to bortezomib was observed, with MM.1S and NCI-H929 cells being more sensitive than U266 and RPMI8226 cells.
- Network modeling identified key proteins, including p-NF-κB, p-Akt, p-mTOR, Bcl-2, p-JNK, p53, p21, p-BAD, caspase 8, and caspase 9, as regulators of bortezomib response.
- More sensitive cell lines exhibited earlier and greater magnitude changes in the expression of these key proteins upon bortezomib treatment.
Conclusions:
- Intracellular signaling pathway differences are associated with bortezomib pharmacodynamic heterogeneity in multiple myeloma.
- Identified key signaling proteins may serve as potential biomarkers for predicting bortezomib response.
- Logic-based network modeling provides insights into drug response mechanisms and biomarker discovery.
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