title: A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results.
abstract: 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.
authors.affiliation: Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary.
authors.forename: Balázs
authors.initials: B
authors.lastname: Ligeti
authors.affiliation: MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary.
authors.forename: Zsófia
authors.initials: Z
authors.lastname: Pénzváltó
authors.affiliation: Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary; Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Vera
authors.affiliation: MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary; 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary; MTA-SE Pediatrics and Nephrology Research Group, Budapest, Hungary.
authors.forename: Balázs
authors.initials: B
authors.lastname: Győrffy
authors.affiliation: Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary; Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy.
authors.forename: Sándor
authors.initials: S
authors.lastname: Pongor
mesh_terms: D000465:Algorithms; D018943:Anthracyclines / Q000008:administration & dosage; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000378:metabolism; D001952:Bridged-Ring Compounds / Q000008:administration & dosage; D002986:Clinical Trials as Topic; D062313:Databases, Pharmaceutical; D004347:Drug Interactions; D005260:Female; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D060066:Protein Interaction Maps / Q000187:drug effects*; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism*; D043823:Taxoids / Q000008:administration & dosage; D000068878:Trastuzumab / Q000008:administration & dosage
chemical_list: D018943:Anthracyclines; D001952:Bridged-Ring Compounds; D018719:Erb-b2 Receptor Tyrosine Kinases; D043823:Taxoids; D000068878:Trastuzumab; C080625:taxane