Alternative classification of glioblastoma based on BUB1B-inhibition sensitivity

We created a computational framework with which to predict BUB1B-GLEBS sensitivity based on gene expression data from BUB1B-inhibition sensitive (BUB1BS) or resistant (BUB1BR) GBM stem cells (GSCs), astrocytes, and neural progenitors. Our classifier examines the expression of 838 genes comprising the BUB1BS signature. Applying this scheme to GBM patient tumor data stratified tumors into BUB1BS and BUB1BR subtypes, revealing that BUB1BS patients have a significantly worse prognosis regardless of their tumor's development subtype (i.e., classical, mesenchymal, neural, proneural). Applying the same scheme to drug sensitivity profiles predicts that BUB1BS cells to be more sensitive to Raf inhibitors as well as other drugs such as type I and II topoisomerase inhibitors among other drugs. Functional genomic profiling of BUB1BR versus BUB1BS GBM isolates revealed differentially reliance of genes enriched in the BUB1BS classifier, including those involved in mitotic cell cycle, microtubule organization, and chromosome segregation. Taken together, our results show that BUB1BR/S classification of GBM tumors, in addition to predicting BUB1B-inhibition sensitivity, may help predict cancer aggressiveness and sensitivity to multiple anti-cancer drugs. Examination of 9 GBM tumor tissue and the corresponding GSC.

本研究构建了一套计算框架，可基于BUB1B抑制敏感（BUB1B-inhibition sensitive, BUB1BS）或耐药（BUB1B-inhibition resistant, BUB1BR）的胶质母细胞瘤干细胞（Glioblastoma Stem Cells, GSCs）、星形胶质细胞（Astrocytes）及神经前体细胞（Neural Progenitors）的基因表达数据，预测BUB1B-GLEBS敏感性。本研究设计的分类器可检测构成BUB1BS特征基因集的838个基因的表达水平。将该分析方案应用于胶质母细胞瘤（Glioblastoma, GBM）患者的肿瘤数据后，可将肿瘤样本分层为BUB1BS与BUB1BR两个亚型。分析显示，无论肿瘤所属的发育亚型（即经典型、间质型、神经型、前神经元型）为何，BUB1BS亚型患者的预后均显著更差。将同一分析方案应用于药物敏感性谱数据，可预测BUB1BS细胞对Raf抑制剂以及Ⅰ型、Ⅱ型拓扑异构酶抑制剂等多种药物均具有更高的敏感性。对BUB1BR与BUB1BS型胶质母细胞瘤分离株的功能基因组谱分析表明，富集于BUB1BS分类器的基因存在差异依赖性，这些基因涉及有丝分裂细胞周期、微管组织及染色体分离过程。综上，本研究结果显示，对胶质母细胞瘤肿瘤进行BUB1BR/S分型，除可预测BUB1B抑制治疗敏感性外，还有助于预测肿瘤侵袭性及对多种抗癌药物的敏感性。本研究对9例胶质母细胞瘤肿瘤组织及其对应的胶质瘤干细胞进行了检测分析。