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

基本信息

批准号：
10493186
负责人：
Antonio Iavarone
金额：
$ 12.52万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-24 至 2022-09-26
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10493186
关键词：
Biological Brain Neoplasms Cancer Model Cancer Patient Cells Classification Clinical Research Computer Analysis Dependence Development Dissection Gene Proteins Genomics Glioblastoma Goals Human Individual Investigation Laboratories Malignant Glioma Malignant Neoplasms Modeling Molecular Motion Oncogenes Oncogenic Outcome Pathogenesis Pathway interactions Patients Phenotype Plant Roots Proteomics Research Resistance Route Solid Neoplasm Stratification Subgroup Testing Therapeutic Tissues Treatment Failure Universities Validation Work base cancer genome cancer heterogeneity computerized tools design drug sensitivity high throughput technology in vitro Model in vivo innovation neoplastic cell new technology new therapeutic target novel operation personalized therapeutic programs targeted treatment therapeutic target tool transcriptomics tumor

项目摘要

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.

抽象的本申请中要开展的工作将继续并将 Iavarone 博士开创的计划扩展到结合创新的计算工具和最先进的体外和体内实验癌症模型识别癌症患者的同质亚组，以剖析癌症的发病机制并进行设计该应用程序专注于胶质母细胞瘤。多形性，人类最致命的癌症之一，对胶质母细胞瘤的研究代表了一种。 Iavarone 博士实验室的长期努力，在最近的工作中产生了新型靶向疗法目前正在临床研究中测试的机会也将受益于组织。最近由 PI 协调的大型网络运营启动了研究计划。围绕开发一种新颖的综合计算实验框架进行阐述：i）鉴定共享相同生物途径激活的同质肿瘤组 ii) 研究；单细胞水平的癌症异质性，以准确告知肿瘤分类 iii) 治疗；从驱动模块和合成致死恶性细胞的识别关系中得出的预测我们将开发和应用高通量转录组和蛋白质组分析的新技术。这些方法是我们在实验室首创的。过去几年在哥伦比亚大学的研究将作为多方面计算的基础分析将提取负责单个细胞表型状态的基因和蛋白质。验证将有选择地应用于新颖且最令人兴奋的分子途径，并将进行我们的实验室拥有一系列实验工具和序列注释的患者衍生模型至于癌基因依赖型和独立型脆弱性的选择。通过我们之前的工作确定，我们的研究有能力识别新的驱动表型和主调节因子单个肿瘤细胞的研究将致力于将新机制转化为基于途径的合成该提案的成功结果是综合性的。能够机械地识别肿瘤决定因素的计算实验管道这些信息对于破译实体瘤的基因组和表型具有不可估量的意义。肿瘤依赖性并提供最准确的治疗预测。