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
Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miami, Florida, USA.
Neuro-Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA.
Neurooncol Adv. 2021 Dec 31;4(1):vdab192. doi: 10.1093/noajnl/vdab192. eCollection 2022 Jan-Dec.
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.
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, = 117) were compared to "normal" frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, = 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, = 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).
Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r = 0.37, < .001) and Cancer Therapeutic Response Portal (CTRP) databases ( = 0.35, < 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.
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.
胶质母细胞瘤患者预后较差，且该肿瘤在分子和细胞水平上存在广泛的异质性，因此有必要通过基因组学驱动的方法开发新的个体化治疗模式。
本研究利用众多药物基因组学和组织数据库来研究胶质母细胞瘤的药物重新定位。将来自癌症基因组图谱（TCGA，n = 117）的胶质母细胞瘤肿瘤样本的RNA测序与来自基因型-组织表达门户（GTEX，n = 120）的“正常”额叶样本进行比较，以发现差异表达基因（DEG）。利用基于综合网络的细胞特征库（LINCS，n = 66,512种化合物）、CCLE（71种胶质瘤细胞系）和欧洲分子生物学实验室化学数据库（ChEMBL）平台的复合基因表达数据和药物活性数据，我们采用了一种汇总的逆转基因表达指标（sRGES）来“逆转”胶质母细胞瘤及其亚型的疾病特征。采用多参数策略对具有良好药代动力学特征（CNS-MPO）的血脑屏障穿透性化合物进行分层。
在ChEMBL（r = 0.37，P <.001）和癌症治疗反应门户（CTRP）数据库（r = 0.35，P < 0.001）中，均发现sRGES与胶质母细胞瘤细胞系中的药物疗效之间存在显著相关性。我们的多参数算法确定了两类具有最高sRGES和CNS-MPO的药物：组蛋白去乙酰化酶抑制剂（伏立诺他和恩替诺特）和适合药物重新定位的拓扑异构酶抑制剂。
我们的研究表明，胶质母细胞瘤疾病特征的逆转与各种胶质母细胞瘤亚型的药物效力相关。这种多参数方法可能通过有效识别能够逆转疾病特征并具有良好药代动力学和安全性的药物，为胶质母细胞瘤的早期个性化组学临床试验奠定基础。
