Ontology highlight
ABSTRACT: Objective
Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.Method
We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.Results
From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.Conclusions
This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.
SUBMITTER: Low YS
PROVIDER: S-EPMC6080645 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
Low Yen S YS Daugherty Aaron C AC Schroeder Elizabeth A EA Chen William W Seto Tina T Weber Susan S Lim Michael M Hastie Trevor T Mathur Maya M Desai Manisha M Farrington Carl C Radin Andrew A AA Sirota Marina M Kenkare Pragati P Thompson Caroline A CA Yu Peter P PP Gomez Scarlett L SL Sledge George W GW Kurian Allison W AW Shah Nigam H NH
Journal of the American Medical Informatics Association : JAMIA 20170501 3
<h4>Objective</h4>Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.<h4>Method</h4>We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pair ...[more]