title: Propagation on Molecular Interaction Networks: Prediction of Effective Drug Combinations and Biomarkers in Cancer Treatment.
abstract: Biomedical sciences use a variety of data sources on drug molecules, genes, proteins, diseases and scientific publications etc. This system can be best pictured as a giant data-network linked together by physical, functional, logical and similarity relationships. A new hypothesis or discovery can be considered as a new link that can be deduced from the existing connections. For instance, interactions of two pharmacons - if not already known - represent a testable novel hypothesis. Such implicit effects are especially important in complex diseases such as cancer.
  
  
  
  The method we applied was to test whether novel drug combinations or novel biomarkers can be predicted from a network of existing oncological databases. We start from the hypothesis that novel, implicit links can be discovered between the network neighborhoods of data items.
  
  
  
  We showed that the overlap of network neighborhoods is strongly correlated with the pairwise interaction strength of two pharmacons used in cancer therapy, and it is also well correlated with clinical data. In a second case study we employed this strategy to the discovery of novel biomarkers based on text analysis. In 2012 we prioritized 10 potential biomarkers for ovarian cancers, 2 of which were in fact described as such in the subsequent years.
  
  
  
  The strategy seems to hold promises for prioritizing new drug combinations or new biomarkers for experimental testing. Its use is naturally limited by the sparsity and the quality of experimental data, however both of these aspects are expected to improve given the development of current databases.
authors: Faculty of Information Technology, Pázmány Péter Catholic University 1083 Budapest, Práter str. 50/A, Budapest, Hungary.
authors: Balazs
authors: B
authors: Ligeti
authors: MTA TTK Lendulet Cancer Biomarker Research Group, Budapest, Hungary.
authors: Otilia
authors: O
authors: Menyhart
authors: Centre for Systems and Information Technologies, University of Nova Gorica, Nova Gorica, Slovenia.
authors: Ingrid
authors: I
authors: Petric
authors: MTA TTK Lendulet Cancer Biomarker Research Group, Budapest, Hungary.|2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary.
authors: Balazs
authors: B
authors: Gyorffy
authors: Faculty of Information Technology, Pázmány Péter Catholic University 1083 Budapest, Práter str. 50/A, Budapest, Hungary.
authors: Sandor
authors: S
authors: Pongor
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000737:chemistry / Q000494:pharmacology*; D014408:Biomarkers, Tumor / Q000037:antagonists & inhibitors*; D004338:Drug Combinations; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy*
chemical_list: D014408:Biomarkers, Tumor; D004338:Drug Combinations
keywords: Drug-drug combinations; breast cancer; combination chemotherapy; drug-drug interactions; ovarian cancer biomarkers