Computational drug repositioning of atorvastatin for ulcerative colitis

Abstract

Objective: Ulcerative colitis (UC) is a chronic inflammatory disorder with limited effective therapeutic options for long-term treatment and disease maintenance. We hypothesized that a multi-cohort analysis of independent cohorts representing real-world heterogeneity of UC would identify a robust transcriptomic signature to improve identification of FDA-approved drugs that can be repurposed to treat patients with UC.

Materials and methods: We performed a multi-cohort analysis of 272 colon biopsy transcriptome samples across 11 publicly available datasets to identify a robust UC disease gene signature. We compared the gene signature to in vitro transcriptomic profiles induced by 781 FDA-approved drugs to identify potential drug targets. We used a retrospective cohort study design modeled after a target trial to evaluate the protective effect of predicted drugs on colectomy risk in patients with UC from the Stanford Research Repository (STARR) database and Optum Clinformatics DataMart.

Results: Atorvastatin treatment had the highest inverse-correlation with the UC gene signature among non-oncolytic FDA-approved therapies. In both STARR (n = 827) and Optum (n = 7821), atorvastatin intake was significantly associated with a decreased risk of colectomy, a marker of treatment-refractory disease, compared to patients prescribed a comparator drug (STARR: HR = 0.47, P = .03; Optum: HR = 0.66, P = .03), irrespective of age and length of atorvastatin treatment.

Discussion & conclusion: These findings suggest that atorvastatin may serve as a novel therapeutic option for ameliorating disease in patients with UC. Importantly, we provide a systematic framework for integrating publicly available heterogeneous molecular data with clinical data at a large scale to repurpose existing FDA-approved drugs for a wide range of human diseases.

Keywords: drug repurposing; gene expression; multi-cohort analysis, ulcerative colitis, electronic health records; transcriptomics.

Figure 1.

Multi-cohort meta-analysis identifies a robust UC gene signature. (A) Overview of multi-cohort analysis to identify UC gene signature. (B) Heatmap of the UC gene signature across all eleven datasets. (C) Top 10 statistically significant pathways using the Reactome pathway database. Number on an edge connecting 2 pathways represent the number of genes shared between the 2 pathways.

Figure 2.

Disease–drug association analysis using LINCS perturbation database. (A) Schematic of the workflow for identifying candidate drugs that reverse UC signature. (B) Heatmap of the top 10 drug signatures inversely and positively correlated with disease signature. (C) Scatterplot of gene effect sizes in UC disease vs atorvastatin. (D) Pathway analysis using genes most significantly inverted between disease and drug signatures. FDR values are log10-scaled. (E) Sensitivity analysis of disease–drug correlations of FDA-approved small-molecule drugs. For each FDR and effect size threshold combination, a corresponding gene signature was generated. Pearson correlations were calculated between the disease signatures and each drug signature. Color represents log2-effect size threshold (0.6–1.2) and dot size represents FDR threshold (1%–20%).

Figure 3.

Kaplan-Meier curves of time to first colectomy of patients with UC who were on atorvastatin (yellow) or a comparator drug (green). (A) Stanford STARR cohort (n = 827). (B) Optum (n = 7821).