Exploiting the Host-HIV Interface To Identify Biomarkers Predicting Time to Viral Rebound after Treatment Interruption

项目摘要/摘要 开发和测试潜在的艾滋病毒治愈疗法将大大加快 预测其临床疗效的生物标志物。可以作为替代终点的生物标记物仍然存在 身份不明。这样的生物标志物将：1)加速艾滋病毒治疗领域的进展，就像血浆病毒载量一样 测试推动了抗病毒药物的开发；2)允许患者参与分析治疗中断 (ATI)试验通过减少试验数量提供更高程度的临床保护；3)提供生物 控制潜在储集层的分子和生化途径的线索；以及4)就像磁铁一样 以吸引生物技术和制药公司更积极地从事艾滋病毒治疗研究。生物标记计划 项目团队(Warner Greene、Gilad Doitsh、Garry Nolan、Katie Pollard、Satih Pillai、Nadia Roan和Robert Siliciano)将寻找能够准确预测治疗后反弹时间的强大生物标志物 中断。这些生物标记物将对治疗领域具有重要价值，因为它们将允许临床医生预测 患者可以停止抗逆转录病毒治疗而不会复发的时间。来自125的血细胞和血浆 HIV感染志愿者参加了在ATI之前和病毒感染时进行的四项不同的ATI试验 将对反弹进行分析。这些患者包括30名在急性感染期间接受治疗的患者 预计会出现较慢的反弹时间。为了同时识别病毒和宿主衍生的生物标记物，该团队将1) 部署一种令人兴奋的“一流”数字液滴聚合酶链式反应分析，选择性地检测和定量完整的 水库中的前病毒DNA(IPDA)--因为正是这一关键的一小部分前病毒种群 包含介导反弹的感染性前病毒，少量完整的前病毒可能会出现 强大的生物标记物预测病毒反弹的时间更长；2)利用下一代超深度测序 在CD4T和其他免疫细胞中分析细胞RNA和miRNAs，并与序列DNA、RNA和 MiRNA在血浆(和有限一部分受试者的脑脊液)中自由循环或结合为 细胞外小泡中的货物以确定病毒反弹时间的预测因素；3)使用7个经过验证的CyTOF面板 包含超过&gt；200个参数，用于研究两者下的CD4T细胞和其他免疫细胞 静息和刺激条件，以确定病毒反弹的单细胞签名时间；4)评估变化 在循环中的抗HIV抗体或淋巴组织炎症标志物(包括 焦下垂的产物)作为所表达的储集物的大小的指示器，这可以作为预测 病毒反弹的时间到了。这些研究将产生大量的高维数据，这些数据将被汇编， 在BioMark的生物信息学和生物统计学核心中进行策划和分析。几种生物统计学方法 将被用来识别这些生物标志物并执行更大的荟萃分析(见核心描述)。在……里面 总结，BioMark提出了一种创新和全面的方法来填补艾滋病毒治疗方面的主要空白 由于缺乏预测治疗中断后病毒反弹时间的关键生物标记物而产生的研究。

Project Summary/Abstract The development and testing of potential HIV cure therapeutics would be greatly expedited by a robust set of biomarkers predicting their clinical effectiveness. Biomarkers that can serve as surrogate endpoints remain unidentified. Such biomarkers will: 1) accelerate progress in the HIV cure arena much like plasma viral load testing propelled antiviral drug development; 2) afford patients participating in analytical treatment interruption (ATI) trials a higher degree of clinical protection by both reducing the number of trials; 3) provide biological clues into the molecular and biochemical pathways that control the latent reservoir; and 4) serve as a magnet for attracting Biotech and Pharma to more vigorously engage in HIV cure research. The BioMark program project team (Warner Greene, Gilad Doitsh, Garry Nolan, Katie Pollard, Satish Pillai, Nadia Roan, and Robert Siliciano) will search for strong biomarkers that accurately predict time to rebound following treatment interruption. Such biomarkers would be of great value for the cure field as they would allow clinicians to predict the period of time a patient can remain off ART without viral recrudescence. Blood cells and plasma from 125 HIV-infected volunteers participating in four different ATI trials obtained before ATI and at the time of viral rebound will be analyzed. These patients include 30 individuals treated during acute infection who are expected to exhibit slower rebound times. To identify both virus- and host-derived biomarkers, the team will 1) deploy an exciting “first in class” digital droplet PCR assay that selectively detects and quantitates intact proviral DNAs (IPDA) in the reservoir––because it is this key small fraction of the total provirus population that contains the infectious proviruses mediating rebound, a low number of intact proviruses might emerge as a strong biomarker predicting a longer time to viral rebound; 2) utilize next-generation ultra-deep sequencing to profile cellular RNAs and miRNAs in CD4 T and other immune cells and in parallel to sequence DNA, RNA and miRNA circulating free in plasma (and in cerebrospinal fluid in a limited subset of subjects) or bound as cargoes in extracellular vesicles to identify predictors of time of viral rebound; 3) use 7 validated CyTOF panels comprising over >200 parameters to phenotypically study CD4 T cells and other immune cells under both resting and stimulated conditions to identify single-cell signatures of time to viral rebound; 4) assess changes in the titer and avidity of circulating anti-HIV antibodies or markers of lymphoid tissue inflammation (including products of pyroptosis) as indicators of the size of the expressed reservoir, which can serve as predictors of time to viral rebound. These studies will generate large bodies of high-dimensional data that will be compiled, curated, and analyzed in BioMark's Bioinformatics and Biostatistics Core. Several biostatistical approaches will be employed to identify these biomarkers and to perform the larger meta analysis (see Core description). In summary, BioMark proposes an innovative and comprehensive approach to fill a major gap in HIV cure research produced by a lack of key biomarkers predicting time to viral rebound after treatment interruption.