A multiparametric pharmacogenomic strategy for drug repositioning predicts therapeutic efficacy for glioblastoma cell lines

Abstract

Background: Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches. Methods: This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNA-seq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n = 117) were compared to "normal"frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n = 120) to find differentially expressed genes (DEGs). Using compound gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n = 66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to "reverse"the resultant disease signature for GBM and its subtypes. A multiparametric strategy was employed to stratify compounds capable of blood-brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO). Results: Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r = 0.37, P < 0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing. Conclusions: Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.