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.