Revealing the tumor-immune network by mass cytometry

描述（由申请人提供）：尽管在阐明免疫细胞识别肿瘤细胞和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反应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是肿瘤免疫网络整体行为的结果。因此，识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方法是大规模细胞术，这是一种具有单细胞分辨率的高维蛋白质组学技术。大量细胞术能够鉴定和枚举细胞群，以及对其细胞行为的询问，例如通过量化活性细胞信号蛋白的水平。因此，我们建议应用细胞计数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早期阶段改变了它们的行为。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。通过大规模细胞术研究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，该算法能够在二维平面上显示高维数据，并将细胞行为变化分类为协同调节的功能盒。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，我们将通过遗传和药理干预进一步验证这些结果。此外，针对免疫系统的新型癌症治疗，通常被称为免疫调节疗法，最近引起了极大的兴趣。然而，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，我们将把我们对肿瘤免疫网络的分析扩展到癌症免疫调节治疗的模型，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，揭示和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肿瘤免疫网络的行为特征来改变癌症治疗，这些网络调节着对抗癌症的免疫临界点。

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.