Revealing the tumor-immune network by mass cytometry

通过质谱流式技术揭示肿瘤免疫网络

• 批准号: 8780520
• 负责人: Matthew Spitzer
• 金额: $ 4.27万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2018-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8780520
• 关键词: Algorithms; Apoptosis; Behavior; Behavioral; Biochemical; Cell Cycle; Cells; Characteristics; Clinic; Clinical; Communities; Complex; Cytometry; DNA Damage; Data; Data Set; Decision Making; Development; Event; Gene Expression Regulation; Genetic; Genetically Engineered Mouse; Genomics; Goals; Histologic; Human; Immune; Immune response; Immune system; Immunotherapeutic agent; Immunotherapy; Intervention; Laboratories; Malignant - descriptor; Malignant Neoplasms; Maps; Measurement; Metric; Mitogen-Activated Protein Kinases; Modeling; Molecular; Nature; Neoplasm Metastasis; Outcome; Pattern; Population; Process; Proteomics; Publishing; Reading; Research; Resolution; STAT protein; Series; Signal Transduction; Signaling Protein; Solutions; Staging; System; Systems Biology; Technology; Time; Tumor Immunity; Tumor Promotion; cancer immunotherapy; cancer therapy; cell behavior; design; human FRAP1 protein; information processing; intercellular communication; interest; killings; mouse model; neoplastic cell; novel; programs; public health relevance; research study; response; tool; tumor; tumorigenesis

DESCRIPTION (provided by applicant): Despite significant efforts to elucidate the mechanisms by which immune cells are capable of recognizing and interacting with tumor cells, little is known about the decision-making process that distinguishes between tumor promotion and tumor rejection via anti-tumor immune responses. We hypothesize that the outcome of immunological tipping point results from the holistic behavior of the tumor-immune network. Therefore, discerning the characteristics of pro-tumor and anti-tumor network states will require a systems biology approach capable of simultaneously integrating measurements of the activity of different immune cell and tumor cell populations. One such approach developed in our laboratory is mass cytometry, a high-dimensional proteomic technology with single-cell resolution. Mass cytometry enables the identification and enumeration of cell populations as well as the interrogation of their cellular behavior, for example by quantifying the levels of actie cell signaling proteins. Therefore, we propose the application of mass cytometry to discern the principal features that govern the activity within the tumor-immune network. By utilizing genetically-engineered mouse models of cancer, which reflect histologic and genomic hallmarks of human tumors, we will determine which immune cell populations alter their behavior at the earliest stages of tumorigenesis. We will use biochemical cell signaling and cell cycle as a metric of the cellular state, as these processes form the core information processing and integration machinery of the cell. By interrogating the tumor-immune network during tumor formation via mass cytometry, we will identify the core functional modules that organize this complex cellular system. In order to discern these coordinated processes, we will apply novel single-cell analytical algorithms developed in our laboratory capable of displaying high-dimensional data in a 2D plane and categorizing cellular behavioral changes into co-regulated functional cassettes. The results of these experiments will determine which cellular programs govern immune reactivity to nascent tumors, results that we will further validate via genetic and pharmacological intervention. Additionally, new types of cancer therapies targeting the immune system, commonly referred to as immunomodulatory therapies, have recently generated significant interest. However, little remains known about the immune system states that these therapies induce in order to drive anti-tumor immunity. We will therefore extend our analysis of the tumor-immune network to a model of immunomodulatory therapy for cancer, revealing the state of the network that permits tumor rejection. These experiments will define a road map to an effective anti-tumor immune response, revealing and defining opportunities to consistently achieve these results in the clinical setting. We therefore believe that these studies have the potential to transform cancer therapy by discerning the behavioral features of the tumor- immune network that regulate the immunological tipping point against cancers.

描述（由申请人提供）：尽管在阐明免疫细胞能够识别肿瘤细胞并与肿瘤细胞相互作用的机制方面做出了重大努力，但对通过抗肿瘤免疫应答区分肿瘤促进和肿瘤排斥的决策过程知之甚少。我们假设免疫临界点的结果来自肿瘤免疫网络的整体行为。因此，辨别促肿瘤和抗肿瘤网络状态的特征将需要能够同时整合不同免疫细胞和肿瘤细胞群体的活性的测量的系统生物学方法。我们实验室开发的一种方法是质谱细胞术，一种具有单细胞分辨率的高维蛋白质组学技术。质谱流式细胞术能够鉴定和计数细胞群，以及询问它们的细胞行为，例如通过定量活性细胞信号蛋白的水平。因此，我们建议应用质谱细胞术来识别控制肿瘤免疫网络内活性的主要特征。通过利用反映人类肿瘤组织学和基因组特征的基因工程小鼠癌症模型，我们将确定哪些免疫细胞群在肿瘤发生的最早阶段改变其行为。我们将使用生化细胞信号和细胞周期作为细胞状态的度量，因为这些过程形成了细胞的核心信息处理和整合机制。通过在肿瘤形成期间通过质谱细胞术询问肿瘤免疫网络，我们将确定组织这个复杂细胞系统的核心功能模块。为了辨别这些协调的过程，我们将应用我们实验室开发的新型单细胞分析算法，该算法能够在2D平面上显示高维数据，并将细胞行为变化归类为共调节功能盒。这些实验的结果将确定哪些细胞程序控制对新生肿瘤的免疫反应性，我们将通过遗传和药理学干预进一步验证这些结果。此外，靶向免疫系统的新型癌症疗法，通常称为免疫调节疗法，最近引起了极大的兴趣。然而，关于这些疗法诱导以驱动抗肿瘤免疫的免疫系统状态知之甚少。因此，我们将我们的肿瘤免疫网络的分析扩展到癌症的免疫调节治疗模型，揭示了允许肿瘤排斥的网络状态。这些实验将为有效的抗肿瘤免疫反应确定路线图，揭示并确定在临床环境中持续实现这些结果的机会。因此，我们相信，这些研究有可能通过识别调节针对癌症的免疫临界点的肿瘤免疫网络的行为特征来改变癌症治疗。