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 CSBC MIT/DFCI 胶质母细胞瘤系统生物学中心的核心前提是高内涵具有空间分辨率的分子、微观和宏观尺度的系统级测量利用数据集成开发计算模型来绘制和预测肿瘤动态和反卷积用于胶质母细胞瘤微环境的计算建模。新型 GBM 模型将支持关键信号传导和代谢途径和网络的识别调节肿瘤进展和治疗耐药性，同时提供肿瘤状态和疗效的生物标志物项目 1 将重点阐明协调肿瘤神经元的网络。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。细胞网络，包括单个胶质母细胞瘤细胞之间形成的谷氨酸突触的形成和来自正常大脑的神经细胞之间的突触连接的建立被提出。肿瘤细胞沿着白质运动，因此意味着相互连接的神经胶质瘤细胞可能驱动侵袭正常脑，这是恶性病变进展和侵袭的主要机制最终使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。由我们的实验室提供，包括一个集成的计算实验框架，注释为 GBM 患者衍生的异种移植物（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本质的相互作用以及该信号网络的扰动如何影响在目标 1 中，我们将使用我们的创新多组学平台。利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并开发和实施计算建模策略来模拟动态不同细胞类型和肿瘤细胞克隆随着时间的推移以及对刺激的反应将允许进化。使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护理治疗背景下抗癫痫药物对肿瘤增殖和侵袭的影响在目标 3 中，我们将分析抗癫痫治疗对放疗和复发的影响。使用我们的多组学平台在临床试验组织标本中建立神经胶质瘤-脑网络，允许测试和利用临床相关数据优化模型准确性。