Oncogenic mechanisms, molecular stratification and therapeutic targets of brain tumors

脑肿瘤的致癌机制、分子分层和治疗靶点

• 批准号: 10729917
• 负责人: Antonio Iavarone
• 金额: $ 82.74万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729917
• 关键词: Oncogenic; mechanisms; molecular; stratification; therapeutic

Abstract The work to be pursued in this application will continue and expand the program pioneered by Dr. Iavarone to combine innovative computational tools and state-of-the-art experimental cancer models in vitro and in vivo to identify homogeneous subgroups of cancer patients in order to dissect the pathogenesis of cancer and design tailored and fully validated personalized therapeutic approaches. The application is focused on glioblastoma multiforme, one of the most lethal forms of human cancer. The investigation of glioblastoma has represented a long-standing effort of Dr. Iavarone’s laboratory, which in recent work has produced novel targeted therapeutic opportunities currently being tested in clinical studies. The proposal will also benefit from the organizational contexts recently set in motion by the large network operations coordinated by the PI. The research plan is articulated around the development of a novel and integrated computational-experimental framework for: i) the identification of homogeneous groups of tumors sharing activation of the same biological pathways; ii) the study of cancer heterogeneity at the single cell level to accurately inform tumor classifications; iii) the therapeutic prediction emerging from the identification of driver modules and synthetic lethal relationships of malignant glioma. We will develop and apply novel technologies for high-throughput transcriptomic and proteomic analysis of individual cells within malignant glioma tissues. These approaches, which we have pioneered in our laboratory at Columbia University during the last few years, will serve as the basis for the multifaceted computational analysis that will extract genes and proteins responsible for the phenotypic state of individual cells. Experimental validations will be selectively applied to the novel and most exciting molecular pathways and will be performed by our laboratory that has an array of experimental tools and sequence-annotated patient-derived models to pursue each individual question. As for the selection of oncogene-dependent and independent vulnerabilities identified by our previous work, the ability of our studies to identify novel driver phenotypes and master regulators of individual tumor cells will be geared towards routing the new mechanisms into pathway-based synthetic lethality that will inform specific drug sensitivities. The successful outcome of this proposal is an integrated computational-experimental pipeline that will be able to mechanistically identify the determinants of tumor genomes and phenotypes of solid tumors. This information will be of invaluable significance to decipher evolving tumor dependencies and provide the most accurate therapeutic predictions.

摘要 这项申请中将要进行的工作将继续并扩大Iavarone博士开创的计划，以 结合创新的计算工具和最先进的体内外实验癌症模型， 确定癌症患者的同质性亚群，以剖析癌症的发病机制并设计 量身定制和充分验证的个性化治疗方法。其应用重点是胶质母细胞瘤。 多形性癌症，人类癌症最致命的形式之一。胶质母细胞瘤的研究代表了一种 Iavarone博士的实验室的长期努力，在最近的工作中产生了新的靶向治疗 目前正在临床研究中测试机会。该提案还将受益于组织 由PI协调的大型网络运营最近启动的上下文。研究计划是 围绕开发一种新颖的综合计算-实验框架：i) 确定具有相同生物路径的同质肿瘤组的激活；ii)研究 在单细胞水平上的癌症异质性，以准确地为肿瘤分类提供信息；iii)治疗 从恶性肿瘤的驱动程序模块和合成致死关系的识别中产生的预测 神经胶质瘤。我们将开发和应用高通量转录和蛋白质组分析的新技术 恶性胶质瘤组织中的单个细胞。这些方法是我们在实验室中首创的 在哥伦比亚大学的过去几年里，将作为多方面计算的基础 将提取与单个细胞表型状态有关的基因和蛋白质的分析。实验 验证将被选择性地应用于新的和最令人兴奋的分子路径，并将被执行 由我们的实验室开发，该实验室拥有一系列实验工具和带序列注释的患者衍生模型 逐一追问每个问题。至于癌基因依赖和独立脆弱性的选择 通过我们之前的工作，我们的研究发现了识别新的司机表型和掌握调节器的能力 单个肿瘤细胞的新机制将被引导到基于途径的合成 致命性将告知特定的药物敏感性。这一提议的成功结果是一个综合的 将能够机械地识别肿瘤决定因素的计算-实验流水线 实体瘤的基因组和表型。这些信息将对破译进化具有无价的意义。 并提供最准确的治疗预测。