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Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

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.

SUBMITTER: Iadevaia S

PROVIDER: S-EPMC2932856 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

Iadevaia Sergio S Lu Yiling Y Morales Fabiana C FC Mills Gordon B GB Ram Prahlad T PT

Cancer research 20100719 17

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 mea ...[more]

PMID: 20643779

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Project description:The FITExP profiling approach was used to identify protein hits from cells incubated with RAPTA-T or RAPTA-EA. Validation experiments with paclitaxel and cisplatin underlined the reliability of the method and hit lists for both test compounds (and?) gave physiologically viable anti-cancer mechanisms. The hit proteins for paclitaxel match with those published and the ones obtained for cisplatin were related to DNA repair, which is in line with the principal mechanism of cisplatin cytotoxicity involving the formation of nuclear DNA lesions. RAPTA-T treatment leads to upregulation of multiple hits suggesting a broad mechanism of action involving both metastasis and tumorigenicity. In contrast, hit proteins identified after incubation with RAPTA-EA are linked to regulation of the oxidative stress response and are therefore thought to be conferred by the EA moiety in the drug. From a therapeutic standpoint, RAPTA-EA could be explored in cancers where EA alone has shown potency, such as chronic lymphocytic leukemias,91 or where EA combined with another agent shows synergy, such as the combination of EA with afatinib, an irreversible epidermal growth factor receptor tyrosine kinase inhibitors for breast cancers.92 On the other hand, due to its broad mechanism of action, RAPTA-T could potentially be more useful if used concomitantly with drugs that target specific cancer pathways and could also play a role in therapies for later stage cancers due to its anti-metastatic properties. Here, application of the FITExP allowed us to gauge the mechanistic pathways involved in the action of two novel ruthenium(II) metallodrugs with very different phenotypic characteristics and from this knowledge infer their role in therapy. Potentially, future application of the FITExP approach in this manner could be useful for identification of cancer chemotherapy drug combinations, where commonly cancer clinical trials fail at phase II where drug combinations are first tried and tested.

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Dataset Information

Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

Publications

Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

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