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

基本信息

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
项目类别：
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

项目摘要

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系统生物学中心的中心前提是高含量具有空间分辨率的分子，微观和宏观尺度的系统级测量使计算模型的开发能够映射和预测利用数据整合的肿瘤动力学和用于胶质母细胞瘤微环境的计算建模的反卷积。建立这个新型GBM模型将支持识别临界信号传导和代谢途径和网络调节肿瘤进展和热耐药性，同时提供肿瘤状态和效率的生物标志物用于热发育。项目1将着重于阐明网络协调肿瘤神经元界面。最近的结果发现了胶质母细胞瘤细胞在脑肿瘤中组织的亚群的能力细胞网络包括形成单个胶质母细胞瘤细胞之间形成的谷氨酸能突触和正常大脑的神经细胞。提出了突触连通性的建立来促进肿瘤细胞沿白质运动，因此表明相互连接的神经胶质瘤细胞可能会驱动正常大脑的入侵，这是进展和侵略性的主要机制神经瘤最终使这些肿瘤无法治愈。现在，我们将利用建立的关键临床前资源由我们的实验室，包括集成的计算实验框架，注释的GBM患者衍生异种移植物（PDXS）用于体内和体内机械实验，以得出神经胶质瘤模型驱动胶质母细胞瘤的恶性本质以及该信号网络的扰动的相互作用如何影响肿瘤增殖，侵袭和热阻力。在AIM 1中，我们将使用我们的创新多派平台使用离体切片培养模型研究神经元支持肿瘤细胞生长和浸润的能力并影响细胞状态以及开发和实施计算建模策略，以建模动态随着时间的推移，不同细胞类型和肿瘤细胞克隆的演变以及响应刺激的演变。 AIM 2将允许计算模型的其他参数，该参数是从测试的体内原位模型中获取的数据在护理标准的情况下，抗癫痫药对肿瘤增殖和侵袭的影响放疗和复发。在AIM 3中，我们将分析抗癫痫疗法对临床试验组织标本中的神经胶质瘤 - 脑网络具有我们的多词平台，允许测试和通过临床相关数据优化模型的准确性。