title: DrugComboRanker: drug combination discovery based on target network analysis.
abstract: BACKGROUND
  Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities.
  
  
  RESULTS
  In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations.
  
  
  METHODS
  The program is available on request.
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USADepartment of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Huang
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Fuhai
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Jianting
authors.initials: J
authors.lastname: Sheng
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Xia
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Jinwen
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Zhan
authors.affiliation: Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong
mesh_terms: D000230:Adenocarcinoma / Q000378:metabolism; D000077192:Adenocarcinoma of Lung; D000465:Algorithms; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000494:pharmacology*; D001499:Bayes Theorem; D055808:Drug Discovery / Q000379:methods*; D023281:Genomics; D006801:Humans; D008175:Lung Neoplasms / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D012984:Software