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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and
Sergio Iadevaia1, Yiling Lu, Fabiana C Morales
1Department of Systems Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA. siadevai@mdanderson.org
Abstract:
Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse-phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, whereas nonoptimal combination of inhibitors increased phosphorylation of nontargeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.
Insights
Designing optimal cancer drug combinations requires understanding complex cell signaling networks. This study used computational modeling and experimental validation to identify effective therapeutic strategies for breast cancer, inhibiting proliferation and preventing resistance.
Area of Science:
- Oncology
- Systems Biology
- Computational Biology
Background:
- Targeted cancer therapies can be ineffective due to cellular bypass mechanisms and regulatory feedback loops.
- Optimizing therapeutic combinations necessitates a deep understanding of complex cellular signaling networks.
- The insulin-like growth factor (IGF-1) signaling pathway is implicated in breast cancer progression.
Purpose of the Study:
- To analyze the IGF-1 signaling network in MDA-MB231 breast cancer cells.
- To develop a computational model for predicting the effects of targeted therapies.
- To identify optimal drug combinations that minimize off-target effects and enhance therapeutic efficacy.
Main Methods:
- Utilized reverse-phase protein array to measure protein phosphorylation changes after IGF-1 stimulation.
- Developed a computational procedure integrating mass action modeling and particle swarm optimization.
- Trained the model against experimental data to infer unknown network parameters.
Main Results:
- The computational model predicted how targeting individual proteins affects the broader signaling network.
- Identified drug combinations that minimized off-target protein phosphorylation.
- Experimental validation confirmed that optimal combinations inhibited cell signaling and proliferation, while non-optimal ones led to resistance.
Conclusions:
- An integrative computational and experimental approach can optimize drug combinations for cancer therapy.
- This strategy can reveal novel pharmacologic targets and improve treatment outcomes.
- Understanding dynamic signaling networks is crucial for overcoming therapeutic resistance in cancer.
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