Conroy Siobhan, Kruyt Frank A E, Joseph Justin V, Balasubramaniyan Veerakumar, Bhat Krishna P, Wagemakers Michiel, Enting Roelien H, Walenkamp Annemiek M E, den Dunnen Wilfred F A
Department of Pathology and Medical Biology (Division of Pathology), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
PLoS One. 2014 Dec 29;9(12):e115687. doi: 10.1371/journal.pone.0115687. eCollection 2014.
Molecular signatures in Glioblastoma (GBM) have been described that correlate with clinical outcome and response to therapy. The Proneural (PN) and Mesenchymal (MES) signatures have been identified most consistently, but others including Classical (CLAS) have also been reported. The molecular signatures have been detected by array techniques at RNA and DNA level, but these methods are costly and cannot take into account individual contributions of different cells within a tumor. Therefore, the aim of this study was to investigate whether subclasses of newly diagnosed GBMs could be assessed and assigned by application of standard pathology laboratory procedures. 123 newly diagnosed GBMs were analyzed for the tumor cell expression of 23 pre-identified proteins and EGFR amplification, together allowing for the subclassification of 65% of the tumors. Immunohistochemistry (IHC)-based profiling was found to be analogous to transcription-based profiling using a 9-gene transcriptional signature for PN and MES subclasses. Based on these data a novel, minimal IHC-based scheme for subclass assignment for GBMs is proposed. Positive staining for IDH1R132H can be used for PN subclass assignment, high EGFR expression for the CLAS subtype and a combined high expression of PTEN, VIM and/or YKL40 for the MES subclass. The application of the proposed scheme was evaluated in an independent tumor set, which resulted in similar subclass assignment rates as those observed in the training set. The IHC-based subclassification scheme proposed in this study therefore could provide very useful in future studies for stratification of individual patient samples.
胶质母细胞瘤（GBM）中的分子特征已被描述，这些特征与临床结果和治疗反应相关。最一致确定的是神经干细胞样（PN）和间充质（MES）特征，但也报道了其他特征，包括经典型（CLAS）。分子特征已通过RNA和DNA水平的阵列技术检测到，但这些方法成本高昂，且无法考虑肿瘤内不同细胞的个体贡献。因此，本研究的目的是调查是否可以通过应用标准病理实验室程序来评估和分类新诊断的GBM亚类。对123例新诊断的GBM进行了分析，检测23种预先确定的蛋白质的肿瘤细胞表达以及表皮生长因子受体（EGFR）扩增情况，共对65%的肿瘤进行了亚分类。发现基于免疫组织化学（IHC）的分析与使用针对PN和MES亚类的9基因转录特征进行的基于转录的分析类似。基于这些数据，提出了一种新的、基于IHC的GBM亚类分类最小方案。异柠檬酸脱氢酶1（IDH1）R132H的阳性染色可用于PN亚类分类，EGFR的高表达用于CLAS亚型，PTEN、波形蛋白（VIM）和/或几丁质酶40（YKL40）的联合高表达用于MES亚类。在一个独立的肿瘤组中评估了所提出方案的应用，结果亚类分类率与训练组中观察到的相似。因此，本研究中提出的基于IHC的亚分类方案在未来个体患者样本分层研究中可能非常有用。
