Project 1: Using CyTOF to identify phenotypic and functional biomarkers predicting time to HIV rebound after treatment interruption

项目摘要 人们正在寻求各种治疗策略，试图根除或限制潜在的艾滋病毒宿主 以便在停止抗逆转录病毒治疗时不会出现高水平的病毒反弹。测试 这些策略的有效性目前需要分析治疗中断(ATI)，在此期间， 被抑制的患者被取消抗逆转录病毒治疗，并仔细监测病毒反弹。具有可靠的生物标志物 这可以准确地预测根据反弹时间评估的治疗方法的有效性 治疗中断后将大大加快艾滋病毒治愈研究的步伐，减少 昂贵且在后勤方面具有挑战性的ATI试验，并为参与这些试验的患者提供更大的保护 审判。从可翻译的角度来看，这些生物标志物应该很容易在血液中检测到，而不是 需要做组织活检。尽管病毒库大小是急性心肌梗死后反弹时间的预测指标，但它不是 强健的生物标志物。目前，尚无已知的免疫生物标记物可以预测急性心肌梗死的发生。 病毒控制的持续时间。在这个项目中，预测反弹时间更短或更长的生物标志物将是 在接受中断治疗的抑制良好的艾滋病毒感染患者中发现。具体地说，质量 细胞术或飞行时间细胞术(CyTOF)将用于鉴定表型和功能 预测病毒反弹时间的生物标记物。在ATI时采集的血液样本，来自以下患者 在慢性和急性感染期间接受治疗，将通过细胞荧光深度表型分析 CD4T细胞、CD8T细胞、B细胞、单核细胞、中性粒细胞、常规树突状细胞、 浆细胞样树突状细胞和NK细胞预测反弹时间。七个面板，每个面板有大约40个参数 使用。这些特征不仅包括细胞表型，还包括这些细胞在 对体外刺激的反应，包括潜伏期反转剂(LRA)治疗。相关性将是 由Citrus算法分析，该算法识别与以下显著关联的细胞子集的特征 疾病结局，在这种情况下是反弹的时间。最后，这些患者体内的生产性感染细胞 病毒反弹的时间特征将有助于绘制这些患者中潜在的可再激活的储存库。 这个项目中的目标将利用高维细胞TOF表型鉴定的能力和它的 分析工具，以确定新的生物标记物，预测治疗中断后病毒反弹的时间。是这样的 生物标记物最终可能会对各种治疗方法的评估有所帮助。

Project Summary A variety of therapeutic strategies are being pursued to try to eradicate or constrain the latent HIV reservoir such that high-level viral rebound does not occur when antiretroviral therapy is discontinued. Testing the efficacy of these strategies currently requires analytical treatment interruption (ATI), during which a suppressed patient is taken off ART and carefully monitored for viral rebound. Having reliable biomarkers that could accurately predict the effectiveness of the therapeutic approach as assessed by time to rebound after treatment interruption would greatly accelerate the pace of HIV cure research, reduce the number of costly and logistically challenging ATI trials, and provide greater protection to patients participating in these trials. From a translatable perspective, these biomarkers should be readily detectable in blood and not require tissue biopsies. Although viral reservoir size is a predictor of time-to-rebound post-ATI, it is not a robust biomarker. Currently, there are no known immunological biomarkers at the time of ATI that can predict the duration of viral control. In this project, biomarkers predicting shorter or longer times to rebound will be identified in well-suppressed HIV-infected patients undergoing treatment interruption. Specifically, mass cytometry, or cytometry by time-of-flight (CyTOF), will be used to identify phenotypic and functional biomarkers predicting time to viral rebound. Blood samples obtained at the time of ATI, from patients who were treated during chronic as well as acute infection, will be analyzed by CyTOF deep-phenotyping for immunological signatures of CD4+ T cells, CD8+ T cells, B cells, monocytes, neutrophils, conventional DCs, plasmacytoid DCs, and NK cells that predict time-to-rebound. Seven panels of ~40 parameters each will be used. These signatures will include not only cellular phenotype but also the functional activity of these cells in response to ex vivo stimulations including treatment with latency reversal agents (LRAs). Correlations will be analyzed by the Citrus algorithm that identifies features of cellular subsets that significantly associate with disease outcome, in this case time-to-rebound. Finally, productively infected cells in these patients at the time of viral rebound will be characterized to help chart the latent reactivatable reservoir in these patients. The aims in this project will take advantage of the powers of high-dimensional CyTOF phenotyping and its analysis tools to identify novel biomarkers predicting time to viral rebound after treatment interruption. Such biomarkers might ultimately prove helpful in the evaluation of various cure therapies.