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）：尽管人们付出了巨大的努力来阐明免疫细胞能够识别肿瘤细胞并与其相互作用的机制，但人们对通过抗肿瘤免疫区分肿瘤促进和肿瘤排斥的决策过程知之甚少。回应。我们假设免疫临界点的结果是由肿瘤免疫网络的整体行为造成的。因此，辨别促肿瘤和抗肿瘤网络状态的特征将需要一种能够同时整合不同免疫细胞和肿瘤细胞群活性测量的系统生物学方法。我们实验室开发的一种此类方法是质量细胞术，这是一种具有单细胞分辨率的高维蛋白质组技术。质谱流式分析能够识别和计数细胞群，并询问其细胞行为，例如通过量化活性细胞信号蛋白的水平。因此，我们建议应用质谱流式细胞术来辨别控制肿瘤免疫网络内活动的主要特征。通过利用反映人类肿瘤的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了它们的行为。我们将使用生化细胞信号传导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。通过使用质谱流式细胞仪询问肿瘤形成过程中的肿瘤免疫网络，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 平面中显示高维数据，并将细胞行为变化分类到共同调节的功能盒中。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，我们将通过遗传和药理学干预进一步验证这些结果。此外，针对免疫系统的新型癌症疗法（通常称为免疫调节疗法）最近引起了人们的极大兴趣。然而，人们对这些疗法诱导的免疫系统状态知之甚少，以驱动抗肿瘤免疫。因此，我们将对肿瘤免疫网络的分析扩展到癌症免疫调节治疗模型，揭示允许肿瘤排斥的网络状态。这些实验将确定有效的抗肿瘤免疫反应的路线图，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癌症免疫临界点的肿瘤免疫网络的行为特征来改变癌症治疗。