Identification of Optimal Drug Combinations Targeting Cellular Networks: Integrating Phospho-Proteomics and Computational Network Analysis

• Published in: Cancer research (Chicago, Ill.) Vol. 70; no. 17; pp. 6704 - 6714
• Main Authors: LADEVAIA, Sergio, YILING LU, MORALES, Fabiana C, MILLS, Gordon B, RAM, Prahlad T
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 01.09.2010
• Subjects: Antineoplastic agents; Antineoplastic Combined Chemotherapy Protocols - administration & dosage; Biological and medical sciences; Breast Neoplasms - drug therapy; Breast Neoplasms - metabolism; Cell Line, Tumor; Drug Delivery Systems - methods; Female; Humans; Insulin-Like Growth Factor I - metabolism; MAP Kinase Signaling System - drug effects; Medical sciences; Models, Biological; Pharmacology. Drug treatments; Phosphatidylinositol 3-Kinases - metabolism; Phosphoinositide-3 Kinase Inhibitors; Phosphorylation - drug effects; Protein Array Analysis - methods; Proteomics - methods; Proto-Oncogene Proteins c-akt - antagonists & inhibitors; Proto-Oncogene Proteins c-akt - metabolism; Receptor, IGF Type 1 - metabolism; Signal Transduction - drug effects; Tumors; Drug combination; Target; Targeting; Network analysis; Proteomics; Identification; Cell network; Computer aided analysis
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/0008-5472.can-10-0460

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.