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

Ashish H Shah; Robert Suter; Pavan Gudoor; Tara T Doucet-O'Hare; Vasileios Stathias; Iahn Cajigas; Macarena de la Fuente; Vaidya Govindarajan; Alexis A Morell; Daniel G Eichberg; Evan Luther; Victor M Lu; John Heiss; Ricardo J Komotar; Michael E Ivan; Stephan Schurer; Mark R Gilbert; Nagi G Ayad

doi:10.1093/noajnl/vdab192

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

Neurooncol Adv. 2021 Dec 31;4(1):vdab192. doi: 10.1093/noajnl/vdab192. eCollection 2022 Jan-Dec.

Authors

Ashish H Shah¹, Robert Suter¹, Pavan Gudoor¹, Tara T Doucet-O'Hare², Vasileios Stathias³, Iahn Cajigas¹, Macarena de la Fuente⁴, Vaidya Govindarajan¹, Alexis A Morell¹, Daniel G Eichberg¹, Evan Luther¹, Victor M Lu¹, John Heiss⁵, Ricardo J Komotar¹, Michael E Ivan¹, Stephan Schurer¹, Mark R Gilbert², Nagi G Ayad¹

Affiliations

¹ Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miami, Florida, USA.
² Neuro-Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA.
³ Department of Molecular and Cellular Pharmacology, University of Miami, Miami, Florida, USA.
⁴ Department of Neurology, University of Miami Miller School of Medicine, Miami, Florida, USA.
⁵ Surgical Neurology Division, NINDS National Institute of Health, Bethesda, Maryland, USA.

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 < .001) and Cancer Therapeutic Response Portal (CTRP) databases (r = 0.35, 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.

Keywords: LINCS; bioinformatics; drug repositioning; glioblastoma.

Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology 2021.

Grants and funding

R25 NS108937/NS/NINDS NIH HHS/United States