A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results

• Published in: PloS one Vol. 10; no. 6; p. e0129267
• Main Authors: Ligeti, Balázs, Pénzváltó, Zsófia, Vera, Roberto, Győrffy, Balázs, Pongor, Sándor
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.06.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Anthracycline; Anthracyclines; Anthracyclines - administration & dosage; Anticancer properties; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Arthritis; Bioinformatics; Breast cancer; Breast Neoplasms - drug therapy; Breast Neoplasms - metabolism; Bridged-Ring Compounds - administration & dosage; Cancer; Cancer therapies; Chemotherapy; Clinical trials; Clinical Trials as Topic; Combination drug therapy; Computer applications; Correlation analysis; Data mining; Databases, Pharmaceutical; Diabetes mellitus; Disease; Drug Interactions; Drug therapy; Drugs; Epidermal growth factor; ErbB-2 protein; Female; Genes; Genetic engineering; Humans; Hypertension; Information technology; Kinases; Medical prognosis; Medical research; Medical screening; Medical treatment; Monoclonal antibodies; Outcome Assessment (Health Care) - methods; Outcome Assessment (Health Care) - statistics & numerical data; Pediatrics; Pharmaceuticals; Product development; Propagation; Protein Interaction Maps - drug effects; Protein-tyrosine kinase receptors; Proteins; Receptor, ErbB-2 - metabolism; Semantics; Targeted cancer therapy; Taxanes; Taxoids - administration & dosage; Trastuzumab; Trastuzumab - administration & dosage; Tyrosine; Hungary; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0129267

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer.