胶质母细胞瘤 (GBM) 是成人中最常见和致命的原发性脑肿瘤，由自我更新的胶质母细胞瘤干细胞 (GSC) 驱动，这些干细胞在治疗和种子治疗难治性复发肿瘤后仍持续存在。 GBM 肿瘤表现出高度的肿瘤内和肿瘤间异质性，这是靶向治疗策略的一个突出障碍。这种异质性延伸到存在于两种转录状态或称为发育和损伤反应的亚型之间的梯度上的 GSC。需要针对每种亚型的药物靶标才能有效靶向 GBM。为了识别大量异质 GSC 中保守的和亚型特异性的遗传依赖性，我们设计了 GBM5K 靶向 g​​RNA 库，并在总共 30 个患者来源的 GSC 培养物中进行了适应性筛选。重点 CRISPR 筛选确定了 GSC 中最保守的亚型特异性漏洞，并阐明了发育状态和损伤反应状态之间存在的功能依赖性梯度。发育特异性适应性基因因神经发育的转录调节因子而丰富，而损伤反应特异性适应性基因则因与整合素和粘着斑信号传导有关的几个基因而突出。这些特定于环境的漏洞赋予了对 β1 整合素、FAK、MEK 和 OLIG2 抑制剂的不同敏感性。有趣的是，筛选显示亚型特异性信号通路驱动亚型之间差异化的细胞周期蛋白 D（CCND1 与 CCND2）依赖性。这些数据提供了对 GBM 异质性的生物学见解和机制理解，并指出了精确定位已定义的 GBM 和 GSC 亚型以解决异质性的机会。

Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults and is driven by self-renewing glioblastoma stem cells (GSCs) that persist after therapy and seed treatment refractory recurrent tumors. GBM tumors display a high degree of intra- and inter-tumoral heterogeneity that is a prominent barrier to targeted treatment strategies. This heterogeneity extends to GSCs that exist on a gradient between two transcriptional states or subtypes termed developmental and injury-response. Drug targets for each subtype are needed to effectively target GBM. To identify conserved and subtype-specific genetic dependencies across a large and heterogeneous panel of GSCs, we designed the GBM5K targeted gRNA library and performed fitness screens in a total of 30 patient-derived GSC cultures. The focused CRISPR screens identified the most conserved subtype-specific vulnerabilities in GSCs and elucidated the functional dependency gradient existing between the developmental and injury-response states. Developmental-specific fitness genes were enriched for transcriptional regulators of neurodevelopment, whereas injury-response-specific fitness genes were highlighted by several genes implicated in integrin and focal adhesion signaling. These context-specific vulnerabilities conferred differential sensitivity to inhibitors of β1 integrin, FAK, MEK and OLIG2. Interestingly, the screens revealed that the subtype-specific signaling pathways drive differential cyclin D (CCND1 vs. CCND2) dependencies between subtypes. These data provide biological insight and mechanistic understanding of GBM heterogeneity and point to opportunities for precision targeting of defined GBM and GSC subtypes to tackle heterogeneity.