Neuro-oncology advances · 2021
A multiparametric pharmacogenomic strategy for drug repositioning predicts therapeutic efficacy for glioblastoma cell lines.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shah Ashish H, Suter Robert, Gudoor Pavan, Doucet-O'Hare Tara T, Stathias Vasileios, Cajigas Iahn, de la Fuente Macarena, Govindarajan Vaidya, Morell Alexis A, Eichberg Daniel G, Luther Evan, Lu Victor M, Heiss John, Komotar Ricardo J, Ivan Michael E, Schurer Stephan, Gilbert Mark R, Ayad Nagi G
Аннотация
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.
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.