Cell and Molecular Biology Department, QIMR Berghofer Medical Research Institute, Sid Faithfull Brain Cancer Laboratory, Brisbane, QLD 4006, Australia.
Immunology Department, QIMR Berghofer Medical Research Institute, Gordon and Jessie Gilmour Leukaemia Research Laboratory, Brisbane, QLD 4006, Australia.
Cells. 2020 Jan 21;9(2):267. doi: 10.3390/cells9020267.
Glioblastoma (GBM) is a treatment-refractory central nervous system (CNS) tumour, and better therapies to treat this aggressive disease are urgently needed. Primary GBM models that represent the true disease state are essential to better understand disease biology and for accurate preclinical therapy assessment. We have previously presented a comprehensive transcriptome characterisation of a panel (n = 12) of primary GBM models (Q-Cell). We have now generated a systematic, quantitative, and deep proteome abundance atlas of the Q-Cell models grown in 3D culture, representing 6167 human proteins. A recent study has highlighted the degree of functional heterogeneity that coexists within individual GBM tumours, describing four cellular states (MES-like, NPC-like, OPC-like and AC-like). We performed comparative proteomic analysis, confirming a good representation of each of the four cell-states across the 13 models examined. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified upregulation of a number of GBM-associated cancer pathway proteins. Bioinformatics analysis, using the OncoKB database, identified a number of functional actionable targets that were either uniquely or ubiquitously expressed across the panel. This study provides an in-depth proteomic analysis of the GBM Q-Cell resource, which should prove a valuable functional dataset for future biological and preclinical investigations.
胶质母细胞瘤（GBM）是一种难治性中枢神经系统（CNS）肿瘤，迫切需要更好的治疗方法来治疗这种侵袭性疾病。能够代表真实疾病状态的原发性 GBM 模型对于更好地了解疾病生物学和准确的临床前治疗评估至关重要。我们之前已经对一组（n=12）原发性 GBM 模型（Q-Cell）进行了全面的转录组特征描述。现在，我们已经生成了 Q-Cell 模型在 3D 培养中生长的系统、定量和深度蛋白质组丰度图谱，代表了 6167 个人类蛋白质。最近的一项研究强调了单个 GBM 肿瘤内共存的功能异质性程度，描述了四种细胞状态（MES 样、NPC 样、OPC 样和 AC 样）。我们进行了比较蛋白质组学分析，证实了在 13 个被检查的模型中，每个细胞状态都有很好的代表性。京都基因与基因组百科全书（KEGG）途径分析确定了许多与 GBM 相关的癌症途径蛋白的上调。使用 OncoKB 数据库进行的生物信息学分析确定了许多功能上可靶向的靶标，这些靶标在面板中是独特或普遍表达的。本研究对 GBM Q-Cell 资源进行了深入的蛋白质组学分析，这应该为未来的生物学和临床前研究提供有价值的功能数据集。
