Project 4: A Discovery-Level Functional Proteomics/Metabolomics Platform for Resolving the Heterogeneity of GBM Tumors and Identifying Effective Therapy Combinations

项目4：用于解决GBM肿瘤异质性并识别有效治疗组合的发现级功能蛋白质组学/代谢组学平台

• 批准号: 9753719
• 负责人: James R. Heath
• 金额: $ 56.16万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753719
• 关键词: Apoptosis; Behavior; Biochemical Pathway; Biological; Biological Assay; Biological Process; Biology; Cancer Biology; Cancer Center; Cell physiology; Cells; Clinic; Code; Combined Modality Therapy; Data; Detection; Disease; Drug Combinations; Drug Design; Drug Targeting; Genetic; Genome; Genomics; Glioblastoma; Glycolysis; Heterogeneity; In Vitro; Kinetics; Malignant Neoplasms; Measurement; Metabolic; Modeling; Molecular; Molecular Profiling; Nanotechnology; Oxidative Phosphorylation; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Population; Positron-Emission Tomography; Proteins; Proteome; Proteomics; Reporter; Resected; Resistance; Resistance development; Science; Signal Transduction; Signaling Protein; Stains; System; Testing; Tissues; Tumor Volume; cancer cell; design; effective therapy; fluorodeoxyglucose; fluorodeoxyglucose positron emission tomography; high dimensionality; in vivo; individual patient; insight; metabolome; metabolomics; multilevel analysis; nanosystems; reconstruction; response; single cell analysis; subcutaneous; targeted treatment; tool; transcriptome; tumor; tumor heterogeneity

Molecular profiling of glioblastoma (GBM) - one of the most lethal of all cancers - has revealed a molecular landscape of altered signal transduction cascades that cluster along a set of druggable core pathways. Yet, drugs designed to target these pathways have failed in the clinic, presumably due to the genetic and functional heterogeneity of the tumor. Single cell analyses may resolve the heterogeneity of GBM in a manner that can point to rationale combination therapies for treating the disease and suppressing resistance. So far, those analyses have been limited to two classes. The first is single-cell genome and transcriptome analysis, which is a discovery-level analysis, but is also noisy and contains limited functional information. The second is single-cell functional proteomic analysis of the altered signaling networks from which drug targets emerge. This tool can provide powerful new ways to look at known biology, but it does not permit discovery. In principle, one wants to know, for each single cell, the molecular code of the cell (the genome), the functionality of that cell (the proteome and metabolome), and the connection between the two – the transcriptome. This requires single cell discovery science that extends from genomics to biological function. We hypothesize that such a deep and multi-level analysis can unveil how GBM tumors adapt or evolve to develop resistance against targeted therapies, thus guiding the design of successful combination therapies for GBM patients.

胶质母细胞瘤 (GBM) 是所有癌症中最致命的一种，其分子分析揭示了一种分子机制 改变的信号转导级联景观沿着一组可成药的核心通路聚集。然而， 旨在针对这些途径的药物在临床上失败了，大概是由于遗传和功能的原因 肿瘤的异质性。单细胞分析可以通过以下方式解决 GBM 的异质性： 指出治疗疾病和抑制耐药性的联合疗法的基本原理。到目前为止，那些 分析仅限于两类。第一个是单细胞基因组和转录组分析，即 发现级分析，但也充满噪音并且包含有限的功能信息。第二种是单细胞 对药物靶标出现的改变的信号网络进行功能蛋白质组学分析。这个工具 可以提供强大的新方法来观察已知的生物学，但它不允许发现。原则上，一 想要知道每个单细胞的分子密码（基因组）、该细胞的功能 （蛋白质组和代谢组），以及两者之间的联系——转录组。这需要单 细胞发现科学从基因组学延伸到生物功能。我们假设如此深刻且 多层次分析可以揭示 GBM 肿瘤如何适应或进化以产生针对靶向药物的耐药性 疗法，从而指导 GBM 患者成功的联合疗法的设计。