Dissecting the cellular hierarchies of malignant gliomas by single-cell functional genomics

通过单细胞功能基因组学剖析恶性胶质瘤的细胞层次

• 批准号: 10577821
• 负责人: Mario Luca Suva
• 金额: $ 38.39万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577821
• 关键词: Adult; Animals; Apoptosis; Automobile Driving; Biological Assay; Cancer Etiology; Cell model; Cells; Cessation of life; Characteristics; Chemoresistance; Child; Clinical; Computer Analysis; Disease; Disease Progression; Disease model; Evolution; Failure; Genetic; Glioma; Human; In Vitro; Invaded; Knowledge; Malignant Glioma; Malignant Neoplasms; Malignant neoplasm of brain; Methodology; Modeling; Natural regeneration; Patients; Phenotype; Proliferating; Research; Resolution; Sampling; Systems Biology; Testing; Therapeutic; cancer cell differentiation; cellular targeting; clinically relevant; functional genomics; in vivo; neurodevelopment; novel; novel therapeutics; self-renewal; single-cell RNA sequencing; stem cells; stemness; therapy resistant; tumor; tumor growth; tumor heterogeneity; tumor initiation; tumorigenesis; virtual

Abstract High-grade gliomas are a leading cause of cancer-related death in adults and children. They are highly heterogeneous diseases in which both inter- and intra-tumoral heterogeneity contribute to disease progression and therapeutic failure. In HGG, defined cellular states with key phenotypic characteristics are selected for during tumorigenesis, drive tumor evolution, and underlie resistance to therapy and invasion. In particular, HGG are thought to be driven by glioma stem cells (GSC), subpopulations of cells recapitulating aspects of neural development that have the preferential capacity to self-renew and to generate differentiated cancer cells. Traditional methodologies to identify GSC rely on functional assays with important caveats and thus do not allow a comprehensive characterization of cellular states in human patients. Additionally, while models of GSC and HGG are extensively used for research, very little is known about their capacity to comprehensively mirror the spectrum of cellular states present in patient samples; due to these limitations, vulnerabilities identified in models frequently do not translate to clinical settings. Accordingly, we propose that the range of cellular states that drive HGG should first be defined directly from patient samples, at single cell resolution, and subsequently be functionally tested in animal and cell-based models. More specifically, we will leverage single-cell RNA-sequencing and a comprehensive systems biology approach in order to (I) identify tumor subpopulations unbiasedly across different genetic clones in human HGG and in matched models of disease, at single-cell resolution; (II) functionally test the capacity of these subpopulations to initiate tumors and to re-generate the diversity of states present in patients; (III) identify faithful cell models that can recapitulate defined cellular states observed in patients and utilize them to experimentally identify regulators with potential utility in clinical settings. Successful completion of the research will fill a fundamental and large gap of knowledge in understanding brain cancer in patients and in models and will provide novel opportunities to target key cellular states that are driving these incurable malignancies. Furthermore, the proposed approach could be extended to other malignancies, and will provide a proof-of-concept for uncovering subpopulations that drive tumor growth directly from patient samples, and subsequently identifying regulators with potential clinical relevance.

摘要 高级别胶质瘤是成人和儿童癌症相关死亡的主要原因。他们高度重视 肿瘤间和瘤内异质性均导致疾病的异质性疾病 进展和治疗失败。在HGG中，定义了具有关键表型的细胞状态 在肿瘤形成过程中选择特征，驱动肿瘤演变，并为其奠定基础 抵抗治疗和侵袭。特别是，HGG被认为是由胶质瘤干细胞驱动的 (GSC)，重述神经发育方面的细胞亚群，具有优先 自我更新和产生分化癌细胞的能力。传统方法来确定 GSC依赖带有重要警告的功能分析，因此不允许全面的 人类患者细胞状态的表征。此外，虽然GSC和HGG的型号是 被广泛用于研究，人们对它们全面反映 患者样本中存在的细胞状态的光谱；由于这些限制，发现了漏洞 在模型中，通常不会转化为临床环境。因此，我们建议， 推动HGG的细胞状态应该首先在单个细胞上直接从患者样本中定义 分辨率，随后在动物和基于细胞的模型中进行功能测试。更多 具体地说，我们将利用单细胞RNA测序和全面的系统生物学 方法，以便(I)在不同的遗传克隆之间无偏见地识别肿瘤亚群 人类HGG和匹配的疾病模型中，在单细胞分辨率下；(Ii)从功能上测试 这些亚群启动肿瘤并重新生成存在的状态多样性的能力 患者；(Iii)确定可靠的细胞模型，可以概括在患者身上观察到的明确的细胞状态 并利用它们来实验性地识别在临床环境中具有潜在实用价值的调节剂。成功 这项研究的完成将填补认识上的一个根本和很大的空白 患者和模型中的脑癌，将提供新的靶向关键细胞的机会 导致这些无法治愈的恶性肿瘤的州。此外，拟议的方法可以是 扩展到其他恶性肿瘤，并将为揭示以下亚群提供概念验证 直接从患者样本中驱动肿瘤生长，随后确定具有潜力的调节剂 临床相关性。