Oncogenic mechanisms, molecular stratification and therapeutic targets of brain tumors

摘要 在这项应用中所追求的工作将继续并扩展Iavarone博士开创的计划， 联合收割机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， 确定癌症患者的同质亚组，以剖析癌症的发病机制，并设计 定制和充分验证的个性化治疗方法。该申请主要针对胶质母细胞瘤 多型癌是人类最致命的癌症之一。胶质母细胞瘤的研究代表了一个 Iavarone博士实验室的长期努力，在最近的工作中， 目前正在临床研究中测试的机会。该提案还将受益于组织 由PI协调的大型网络操作最近启动的上下文。研究计划是 阐述了一个新的和综合的计算实验框架的发展：i） 鉴定共享相同生物学途径激活的同质肿瘤组; ii）研究 在单细胞水平上的癌症异质性，以准确地告知肿瘤分类; iii）治疗 预测出现的驱动程序模块和恶性肿瘤的合成致死关系的识别 胶质瘤我们将开发和应用高通量转录组学和蛋白质组学分析的新技术 恶性神经胶质瘤组织中的单个细胞。我们在实验室中率先采用的这些方法 在哥伦比亚大学在过去的几年里，将作为基础的多方面计算 分析，将提取基因和蛋白质负责个别细胞的表型状态。实验 验证将选择性地应用于新的和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， 追求每个人的问题。至于癌基因依赖性和独立性脆弱性的选择 通过我们以前的工作确定，我们的研究能够识别新的驱动表型和主调节器 个体肿瘤细胞的研究将致力于将新机制引入基于途径的合成 致命性，将告知特定的药物敏感性。这一建议的成功结果是一个综合的 计算实验管道，将能够机械地确定肿瘤的决定因素 实体瘤的基因组和表型。这些信息对于破解进化论具有非常重要的意义 肿瘤依赖性，并提供最准确的治疗预测。

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.