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

项目摘要 目前正在采取各种治疗策略，试图根除或限制潜伏的艾滋病毒储存库 这样当抗逆转录病毒治疗停止时不会发生高水平的病毒反弹。测试 这些策略的有效性目前需要分析性治疗中断（ATI），在此期间， 受抑制的患者停止抗逆转录病毒治疗，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，减少艾滋病患者的数量， 成本高昂且后勤上具有挑战性的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容易在血液中检测到，而不是在血液中检测到。 需要组织活检虽然病毒库大小是ATI后反弹时间的预测因子，但它不是ATI后反弹时间的预测因子。 稳健的生物标志物。目前，还没有已知的免疫学生物标志物在ATI的时间，可以预测 病毒控制的持续时间。在这个项目中，生物标志物预测更短或更长的时间反弹将是 在受到良好抑制的HIV感染患者接受治疗中断。具体来说，质量 将使用流式细胞术或飞行时间流式细胞术（CyTOF）来鉴定表型和功能性 预测病毒反弹时间的生物标志物。ATI时从以下患者中获得的血液样本 在慢性和急性感染期间接受治疗，将通过CyTOF深度表型分析， CD4+ T细胞、CD8+ T细胞、B细胞、单核细胞、嗜中性粒细胞、常规DC 浆细胞样DC和预测反弹时间的NK细胞。将对每个约40个参数的7个面板进行 采用这些特征将不仅包括细胞表型，而且包括这些细胞在细胞内的功能活性。 对离体刺激的反应，包括用潜伏期逆转剂（LRA）治疗。相关性将是 通过Citrus算法进行分析，该算法识别与以下显著相关的细胞子集的特征： 疾病结果，在这种情况下，反弹时间。最后，在这些患者中， 病毒反弹的时间将被表征，以帮助绘制这些患者中潜在的可再活化储库。 该项目的目标将利用高维度CyTOF表型分析的能力及其 分析工具，以确定预测治疗中断后病毒反弹时间的新生物标志物。等 生物标志物最终可能被证明有助于评估各种治愈疗法。

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.