体能筛选绘制出特定状态的胶质母细胞瘤干细胞脆弱性图。

IF 12.5 1区 医学 Q1 ONCOLOGY
Graham MacLeod, Fatemeh Molaei, Shahan Haider, Maira P Almeida, Sichun Lin, Michelle Kushida, Haresh Sureshkumar, Jasmine K Bhatti, Jack Q Lu, Daniel Schramek, Peter B Dirks, Stephane Angers
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引用次数: 0

摘要

胶质母细胞瘤(GBM)是成人中最常见、最致命的原发性脑肿瘤,由自我更新的胶质母细胞瘤干细胞(GSCs)驱动,这些干细胞在治疗后仍会存活,并为难治性复发肿瘤播下种子。胶质母细胞瘤的瘤内和瘤间具有高度异质性,这是靶向治疗策略的一个突出障碍。这种异质性延伸到 GSC,它们存在于两种转录状态或亚型(称为发育型和损伤反应型)之间的梯度上。要有效针对 GBM,就需要针对每种亚型的药物靶点。为了在大量异质性 GSCs 中鉴定保守的和亚型特异的遗传依赖性,我们设计了 GBM5K 靶向 gRNA 文库,并在总共 30 个患者来源的 GSC 培养物中进行了适配性筛选。聚焦 CRISPR 筛选确定了 GSCs 中最保守的亚型特异性弱点,并阐明了发育和损伤反应状态之间存在的功能依赖梯度。神经发育的转录调控因子富集了发育特异性健壮基因,而损伤反应特异性健壮基因则由几个与整合素和病灶粘附信号转导有关的基因组成。这些特定环境下的脆弱性导致了对β1整合素、FAK、MEK和OLIG2抑制剂的不同敏感性。有趣的是,筛选结果显示,亚型特异性信号通路驱动亚型之间不同的细胞周期蛋白 D(CCND1 与 CCND2)依赖性。这些数据提供了对 GBM 异质性的生物学洞察和机理理解,并为精准靶向确定的 GBM 和 GSC 亚型以解决异质性问题提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fitness Screens Map State-Specific Glioblastoma Stem Cell Vulnerabilities.

Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults and is driven by self-renewing glioblastoma stem cells (GSC) that persist after therapy and seed treatment-refractory recurrent tumors. GBM tumors display a high degree of intra- and intertumoral 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 guide RNA 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, focal adhesion kinase, 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 a biological insight and mechanistic understanding of GBM heterogeneity and point to opportunities for precision targeting of defined GBM and GSC subtypes to tackle heterogeneity. Significance: CRISPR-Cas9 screens in a panel of patient-derived glioblastoma stem cells reveal heterogeneity in genetic vulnerabilities across subtypes that have important implications for targeted and combination treatment strategies for glioblastoma.

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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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