Project 1: Deciphering the Dynamic Evolution of the Tumor-Neural Interface

项目1：破译肿瘤-神经界面的动态演化

• 批准号: 10729275
• 负责人: Nathalie YR Agar
• 金额: $ 47.3万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729275
• 关键词: AMPA Receptors; Affect; Aftercare; Animals; Antiepileptic Agents; Biological Process; Blood Vessels; Brain; Brain Neoplasms; Cell Communication; Cells; Classification; Clinical Trials; Clone Cells; Communication; Communities; Competence; Computer Models; Data; Dedications; Development; Electrophysiology (science); Excision; Exhibits; Extracellular Matrix; Favorable Clinical Outcome; Glioblastoma; Glioma; Glutamates; Heterogeneity; High Frequency Oscillation; Immunologics; Individual; Invaded; Levetiracetam; Location; Logistic Regressions; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Glioma; Maps; Measurement; Measures; Metabolic; Metabolic Pathway; Microscopic; Mitochondria; Modeling; Molecular; Multiomic Data; Nature; Neuroepithelial, Perineurial, and Schwann Cell Neoplasm; Neurons; Operative Surgical Procedures; Oxidative Phosphorylation; Pathway interactions; Patients; Phenotype; Proliferating; Radiation therapy; Recurrence; Resected; Resistance; Resolution; Resources; Route; Sampling; Signal Pathway; Signal Transduction; Slice; Specimen; Synapses; System; Systems Biology; Testing; Therapeutic; Time; Tissues; Treatment Efficacy; Tumor Markers; Tumor Promotion; Work; cancer proteomics; cell growth; cell motility; cell type; chemotherapy; clinically relevant; cohort; data integration; deep learning; design; dynamical evolution; experimental study; in vivo; inhibitor; innovation; multiple omics; neoplastic cell; neuronal tumor; novel; patient derived xenograft model; pharmacologic; pre-clinical; progenitor; random forest; response; single-cell RNA sequencing; standard of care; therapeutic development; therapy resistant; tumor; tumor metabolism; tumor progression; tumorigenesis; white matter

ABSTRACT – PROJECT 1 The central premise of our CSBC MIT/DFCI Center for Systems Biology in Glioblastoma is that high-content systems-level measurements at molecular, microscopic, and macroscopic scales with spatial resolution will enable the development of computational models to map and predict tumor dynamics leveraging data integration and deconvolution for computational modeling of the glioblastoma-microenvironment. The establishment of this novel GBM model will support the identification of critical signaling and metabolic pathways and networks regulating tumor progression and therapeutic resistance, while providing biomarkers of tumor state and efficacy for therapeutic developement. Project 1 will focus on elucidating networks coordinating the tumor-neuronal interface. Recent results uncovered the ability of subpopulations of glioblastoma cells to organize in brain tumor cell networks that include the formation of glutamatergic synapses formed between individual glioblastoma cells and neural cells from the normal brain. The establishment of synaptic connectivity was proposed to promote tumor cell movement along white matter, therefore implying that the mutually connected glioma cells may drive invasion of the normal brain, which is the primary mechanism of progression and aggressiveness of malignant glioma that ultimately renders these tumors incurable. We will now leverage key preclinical resources established by our labs, including an integrated computational-experimental framework, annotated GBM patient-derived xenografts (PDXs) for ex vivo and in vivo mechanistic experiments to derive a model of glioma-neuron interactions that drive the malignant nature of glioblastoma and how perturbation of this signaling network affects tumor proliferation, invasion, and therapeutic resistance. In Aim 1, we will use our innovative multi-omics platform with ex vivo slice culture models to investigate the ability of neurons to support tumor cell growth and invasion and affect cell state and develop and implement computational modeling strategies to model the dynamic evolution of different cell types and tumor cell clones over time and in response to stimulation. Aim 2 will allow further parametrization of the computational model with data acquired from in vivo orthotopic models tested for the effect of anti-epileptic drugs on tumor proliferation and invasion in the context of standard of care treatment with radiotherapy and recurrence. In Aim 3, we will analyze the impact of anti-epileptic therapeutics on the glioma-brain network in clinical trial tissue specimens with our multi-omics platform, allowing to test and optimize the model accuracy with clinically relevant data.

摘要-项目1 我们的CSBC MIT/DFCI胶质母细胞瘤系统生物学中心的中心前提是， 具有空间分辨率的分子、微观和宏观尺度的系统级测量将 使计算模型的开发能够利用数据集成来映射和预测肿瘤动力学 以及用于胶质母细胞瘤微环境的计算建模的去卷积。设立这一 一个新的GBM模型将支持关键信号和代谢途径和网络的识别 调节肿瘤进展和治疗抗性，同时提供肿瘤状态和功效的生物标志物 用于治疗发展。项目1将集中于阐明协调肿瘤-神经元 接口.最近的结果揭示了胶质母细胞瘤细胞亚群在脑肿瘤中组织的能力 包括单个胶质母细胞瘤细胞之间形成的突触的形成的细胞网络 和正常大脑的神经细胞。突触连接的建立被提出来促进 肿瘤细胞沿着白色物质运动，因此暗示相互连接的胶质瘤细胞可能驱动 正常脑的侵袭，这是恶性肿瘤进展和侵袭性的主要机制。 最终导致这些肿瘤无法治愈的神经胶质瘤。我们现在将利用已建立的关键临床前资源 我们的实验室，包括一个综合的计算实验框架，注释GBM患者衍生 异种移植物（PDX）用于离体和体内机制实验以获得神经胶质瘤-神经元模型 驱动胶质母细胞瘤恶性性质的相互作用以及这种信号网络的扰动如何影响 肿瘤增殖、侵袭和治疗抗性。在目标1中，我们将使用创新的多组学平台 用离体切片培养模型研究神经元支持肿瘤细胞生长和侵袭的能力 并影响细胞状态，开发和实施计算建模策略， 不同细胞类型和肿瘤细胞克隆随时间的演变和对刺激的响应。目标2将允许 利用从测试的体内原位模型获得的数据进一步参数化计算模型 在标准治疗的背景下，抗癫痫药物对肿瘤增殖和侵袭的影响 放疗和复发。在目标3中，我们将分析抗癫痫治疗对癫痫发作的影响。 神经胶质瘤-脑网络在临床试验组织标本与我们的多组学平台，允许测试和 利用临床相关数据优化模型精度。