Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma

Jessica E Sagers,Chirag J Patel,Sasa Vasilijic,Isaac S Kohane,Lukas D Landegger,D Bradley Welling,Rebecca M Lewis,Roy H Perlis,Mehmet I Sahin,Konstantina M Stankovic,Adam S Brown

doi:10.1038/s41598-018-23609-7

Abstract

The computational repositioning of existing drugs represents an appealing avenue for identifying effective compounds to treat diseases with no FDA-approved pharmacotherapies. Here we present the largest meta-analysis to date of differential gene expression in human vestibular schwannoma (VS), a debilitating intracranial tumor, and use these data to inform the first application of algorithm-based drug repositioning for this tumor class. We apply an open-source computational drug repositioning platform to gene expression data from 80 patient tumors and identify eight promising FDA-approved drugs with potential for repurposing in VS. Of these eight, mifepristone, a progesterone and glucocorticoid receptor antagonist, consistently and adversely affects the morphology, metabolic activity, and proliferation of primary human VS cells and HEI-193 human schwannoma cells. Mifepristone treatment reduces VS cell viability more significantly than cells derived from patient meningiomas, while healthy human Schwann cells remain unaffected. Our data recommend a Phase II clinical trial of mifepristone in VS.

Highlights

The computational repositioning of existing drugs represents an appealing avenue for identifying effective compounds to treat diseases with no FDA-approved pharmacotherapies
To identify FDA-approved drugs with potential for repositioning in vestibular schwannoma (VS), we conducted a computational screen using the open-source drug repositioning platform ksRepo, developed to screen expression profiles from any microarray or sequencing platform against any available database of gene-drug interactions13. ksRepo uses a modified Kolmogorov-Smirnov statistic to compare a ranked list of differentially expressed genes (DEGs) characteristic of a given disease with transcriptional signatures associated with drugs known to interact with those genes, as publicly stored in the Comparative Toxicogenomics Database (CTD)[14]
We selected ksRepo for this specific analysis because it does not require input data to be generated via a specific platform; allows us to choose the database we use; affords us flexibility in selecting which drugs to test; and is compatible with gene-level meta-analysis. ksRepo was recently shown to be successful against a meta-analysis of DEGs from five independent prostate cancer datasets, from which this algorithm successfully predicted significance for five approved therapies in prostate cancer treatment[13]

Summary

Introduction

The computational repositioning of existing drugs represents an appealing avenue for identifying effective compounds to treat diseases with no FDA-approved pharmacotherapies. We apply an open-source computational drug repositioning platform to gene expression data from 80 patient tumors and identify eight promising FDA-approved drugs with potential for repurposing in VS. Of these eight, mifepristone, a progesterone and glucocorticoid receptor antagonist, consistently and adversely affects the morphology, metabolic activity, and proliferation of primary human VS cells and HEI-193 human schwannoma cells. A holistic evaluation of drug safety within the potential confounds of a new disease indication should never be neglected

Methods

Results

Conclusion