Evolution and targeting of the functional states of glioblastoma

胶质母细胞瘤功能状态的进化和靶向

• 批准号: 10467181
• 负责人: Antonio Iavarone
• 金额: $ 25.65万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2022-09-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10467181
• 关键词: Address; Adult; Aftercare; Alkylating Agents; Automobile Driving; Bar Codes; Basic Science; Biological Markers; Cancer Patient; Cells; Classification; Clinic; Clinical; Clinical Data; Clinical Research; Communities; DNA-dependent protein kinase; Data; Dependence; Diagnosis; Disease; Elements; Evolution; Excision; Exhibits; Experimental Models; Favorable Clinical Outcome; Gene Mutation; Genetic; Genetic Variation; Genomics; Genotype; Glioblastoma; Glioma; Grant; Heritability; Human; In Vitro; Individual; Inter-tumoral heterogeneity; Intervention; Knowledge; Laboratories; Lesion; Link; Malignant Neoplasms; Metabolic; Mitochondria; Modeling; Multiomic Data; Neurons; Nodal; Operative Surgical Procedures; Organoids; Outcome; Oxidative Phosphorylation; Pathogenesis; Pathway interactions; Patients; Pharmacology; Pharmacotherapy; Phosphorylation Inhibition; Preclinical Testing; Prognosis; Protein Kinase; Proteomics; Radiation; Radiation therapy; Recurrence; Reporting; Research; Resistance; Resources; Reverse engineering; Signal Transduction; Stratification; Subgroup; Testing; Therapeutic; Therapeutic Intervention; Translating; Tumor Biology; Validation; Work; addiction; base; cancer cell; cancer proteomics; cohort; computerized tools; experimental study; genomic data; improved; in vitro Model; in vivo; inhibitor; innovation; metabolomics; mouse model; neoplastic cell; novel; novel strategies; patient subsets; phosphoproteomics; pre-clinical; pressure; progenitor; prognostic; programs; rational design; reconstruction; single cell analysis; single-cell RNA sequencing; success; targeted treatment; temozolomide; therapeutic evaluation; therapeutic target; tool; transcriptomics; tumor; tumor microenvironment

Project Summary The application is focused on glioblastoma multiforme (GBM), one of the most lethal forms of human cancer for which the massive knowledge generated by genomic data has provided little therapeutic improvement. One key element in the success of clinical studies for cancer patients is the selection of homogeneous groups of patients harboring tumors that share identifiable functional vulnerabilities rather than general biomarkers. In GBM, the lack of a functional classifier has hindered the targeting of fundamental cancer-driving mechanisms in well- defined patient subgroups, leading to discouraging results. The proposal is founded on a novel classification of GBM that we have recently proposed. Different from previously established marker-based classification, the new classifier is centered on functional activities of cancer cells that we identified by single cell transcriptomic analysis. The classifier was validated in several cohorts of bulk primary GBM and includes four subtypes, two linked to neurodevelopmental programs, neuronal and proliferative-progenitor, and two characterized by divergent metabolic activities, mitochondrial and glycolytic-plurimetabolic. Notably, the mitochondrial subtype is endowed with a distinct sensitivity to oxidative phosphorylation inhibition and is associated with a better survival, while the glycolytic-plurimetabolic subtype is characterized by redundant metabolic activities. Our preliminary analysis revealed that each functional GBM subtype is association with biologically coherent proteomic and phosphoproteomic features. In this application we will combine innovative computational tools and state-of-the- art experimental models in vitro and in vivo to study the impact of functional cell states of GBM in therapy resistance. We built the research plan with the following aims: i) examine and target the plasticity of the neurodevelopmental glioma states under therapy pressure and the cross-talk with signals from the microenvironment; ii) determine how mitochondrial cells adjust to therapy pressure when treated with mitochondrial inhibitors and the mechanism of induced resistance; iii) retrieve therapeutic intervention points from proteomic data focusing on DNA-PK and PKCd, two protein kinases active selectively in the proliferative/progenitor and glycolytic/plurimetabolic subtypes of GBM, respectively. Experimental validations will be applied to these nodal factors and will be performed by our laboratories, which in the course of many years have generated and perfected the array of experimental tools including sequence-annotated patient-derived models to pursue each question. By integrating novel computational and experimental platforms to study the evolution of distinct GBM subtypes, the proposal is conceptually and technically innovative. The successful outcome of this proposal will be the delivery of key information to decipher evolving tumor dependencies under treatment and accurate therapeutic strategies specifically tailored to distinct subgroups of GBM patients.

项目摘要 应用重点是多形性胶质母细胞瘤(GBM)，这是人类癌症中最致命的形式之一 而基因组数据产生的海量知识并没有提供多少治疗改进。一键 癌症患者临床研究成功的要素是选择同类患者组 具有共同的可识别的功能脆弱性的肿瘤，而不是通用的生物标志物。在GBM中， 缺乏功能分类器阻碍了对井中基本癌症驱动机制的靶向 定义了患者亚组，导致令人沮丧的结果。这项提议基于一种新的分类 我们最近提出的GBM。与以前建立的基于标记的分类不同，新的 分类器以我们通过单细胞转录鉴定的癌细胞的功能活动为中心 分析。该分类器在几个散装初级GBM队列中进行了验证，包括四个亚型，两个 与神经发育程序、神经元和增殖前体细胞有关，以及两个特征为 代谢活动不同，线粒体和糖酵解-多代谢。值得注意的是，线粒体亚型是 对氧化磷酸化抑制具有明显的敏感性，并与更好的生存相关， 而糖酵解-多代谢亚型的特点是代谢活动过剩。我们的预赛 分析表明，每种功能的GBM亚型都与生物一致性蛋白质组和 磷蛋白组学特征。在此应用程序中，我们将结合创新的计算工具和最新的 体外和体内ART实验模型研究GBM功能细胞状态在治疗中的影响 抵抗。我们制定的研究计划有以下几个目标：i)检查和定位材料的塑性 神经发育胶质瘤在治疗压力下的状态以及与来自 微环境；ii)确定线粒体细胞在接受治疗时如何适应治疗压力 线粒体抑制剂及其诱导抗性的机制；III)恢复治疗干预点 从以DNA-PK和PKCd为核心的蛋白质组学数据来看，有两种蛋白激酶选择性地活跃在 增殖/祖细胞亚型和糖酵解/多代谢亚型。实验验证将 应用于这些节点因素，并将由我们的实验室进行，在多年的过程中 生成并完善了一系列实验工具，包括序列注释患者派生的 模特们追问每一个问题。通过集成新的计算和实验平台来研究 由于不同的GBM亚型的演变，该提案在概念和技术上都是创新的。成功者 这项提案的结果将是提供关键信息，以破译在以下情况下不断演变的肿瘤依赖性 专门针对不同的GBM患者亚群量身定做的治疗和准确的治疗策略。