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Genome Med. 2014 Apr 30;6(4):33. doi: 10.1186/gm550. eCollection 2014.

Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

Author information

1
Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth College, HB7937 One Medical Center Dr, Lebanon NH 03766, USA.
2
Department of Genetics, Geisel School of Medicine at Dartmouth College, HB7937 One Medical Center Dr, Lebanon NH 03766, USA ; Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth College, HB7937 One Medical Center Dr, Lebanon NH 03766, USA.

Abstract

BACKGROUND:

Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets.

METHODS:

We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18).

RESULTS:

We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment.

CONCLUSIONS:

To derive the greatest benefit from molecularly targeted drugs it is critical to design combination treatment strategies rationally. Incorporating knowledge of the tumor adaptation process into the design provides an opportunity to match targeted drugs to the evolving tumor phenotype and surmount resistance.

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