Synergistic drug combinations from electronic health records and gene expression

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.

Keywords: breast cancer; combination therapies; drug discovery; drug interactions; drug repurposing; electronic health records.

Figure 1.

Method overview of (A) scoring EHR-based synergistic drug pairs, (B) scoring gene expression–based synergistic drug pairs, and (C) gene set enrichment analysis–like analysis of enrichment of EHR-based drug class pairs among gene expression–based drug pairs.

Figure 2.

Odds ratios of factors (excluding pairwise interactions) most associated with 5-year mortality (see also Supplementary Table S3).

Figure 3.

Variables (nodes) that synergistically interact such that they are associated with lower mortality (blue edges) or higher mortality (red edges, also see Table 2). Variable nodes that tend to have synergistically beneficial interactions (blue edges) also tend to be factors associated with lower mortality (eg, Stage I), while those with synergistically risky interactions (red) tend to be risk factors on their own (eg, Stage IV). Nodes are grouped together (eg, by categorical level, ATC class) to facilitate visual comparison within a group (eg, Stages I and II have many synergistically beneficial interactions while Stages III and IV have many synergistically risky interactions). Case studies described in the Discussion section are highlighted with thicker edges.

Figure 4.

Enrichment analysis of EHR-based synergistic drug class pairs (A) anti-inflammatories/antirheumatics with lipid modifiers, (B) anti-inflammatories/antirheumatics with hormone antagonists, and (C) lipid modifiers and drugs for obstructed airways among gene expression–based synergistic drug pairs. All possible pairs of drugs from DrugBank v. 4.0 were scored on their association with genes differentially expressed in breast cancer (shaded area). A GSEA-based analysis was then performed to score the enrichment of pairs of drugs derived from the respective EHR-based classes (derived drug pairs represented by black vertical lines, running enrichment represented by red bold line) and compared to a randomly sampled null distribution (10 000 iterations) to assess significance and fold enrichment.