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

利用宿主-HIV 界面识别生物标志物，预测治疗中断后病毒反弹的时间

• 批准号: 9754763
• 负责人: Warner C. Greene
• 金额: $ 168.71万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-08 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9754763
• 关键词: AIDS clinical trial group; Aftercare; Anti-Retroviral Agents; Antibodies; Antibody Avidity; Antibody titer measurement; Antiviral Agents; Avidity; Berlin; Biochemical Pathway; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Biostatistics Core; Biotechnology; Blood Cells; Boston; CD4 Positive T Lymphocytes; Cells; Cerebrospinal Fluid; Clinic; Clinical; Clinical effectiveness; Complex; Computer software; Consent; Coupled; DNA; DNA Sequence; Dangerousness; Data Analyses; Data Set; Development; Disease remission; Exhibits; Expression Profiling; Gene Expression; Genes; Gold; HIV; HIV Antibodies; Immune; Immunize; Individual; Inflammation; International; Interruption; Laboratories; Lymphoid Tissue; Measurement; Mediating; Meta-Analysis; MicroRNAs; Molecular; NIH Office of AIDS Research; Nature; Participant; Patient risk; Patients; Phenotype; Plasma; Population; Proviruses; RNA; Recrudescences; Research; Rest; SLEB2 gene; Sampling; Surrogate Endpoint; T-Lymphocyte; Technology; Testing; Therapeutic; Time; Viral; Viral Load result; Virus; Work; acute infection; circulating microRNA; cost; curative treatments; cytokine; deep sequencing; digital; drug development; exhaustion; exosome; experience; extracellular vesicles; falls; high dimensionality; inflammatory marker; innovation; miRNA expression profiling; microRNA biomarkers; multidimensional data; next generation; novel; predictive marker; programs; specific biomarkers; therapeutic candidate; viral DNA; viral rebound; volunteer

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.

项目摘要/摘要 一组健壮的艾滋病毒治疗疗法的开发和测试将大大加快 生物标志物可以预测其临床有效性。可以用作替代终点的生物标志物仍然存在 身份不明。这样的生物标志物将：1）像等离子体病毒载荷一样加速HIV治疗领域 测试推进的抗病毒药物开发； 2）让参加分析治疗中断的患者 （ATI）试验通过减少试验数量来更高程度的临床保护； 3）提供生物学 进入控制潜在储层的分子和生化途径的线索； 4）用作磁铁 吸引生物技术和制药以更积极地从事HIV治疗研究。生物标志计划 项目团队（Warner Greene，Gilad Doitsh，Garry Nolan，Katie Pollard，Satish Pillai，Nadia Roan和Robert Siliciano）将寻找强大的生物标志物，这些标志物可以准确预测治疗后反弹的时间 中断。这种生物标志物对于治疗领域将具有很大的价值，因为它们将允许临床医生预测 患者可以在没有病毒复发的情况下脱离艺术的时间。来自125的血细胞和血浆 HIV感染的志愿者参加了ATI之前获得的四个不同ATI试验 反弹将进行分析。这些患者包括30名在急性感染期间接受治疗的人 预计会揭示较慢的反弹时间。为了识别病毒和宿主衍生的生物标志物，团队将1） 部署令人兴奋的“班级”数字液滴PCR分析，该测定有选择地检测和定量完整 储层中的病毒DNA（IPDA） - 因为这是省人口总人口的关键一小部分 包含介导反弹的传染病病毒，完整数量的病毒可能会出现在 强大的生物标志物预测病毒反弹的时间更长。 2）利用下一代超深测序 CD4 T和其他免疫细胞中的细胞RNA和miRNA，并平行于序列DNA，RNA和 miRNA在血浆中无循环（在受试者的有限子集中脑脊液中）或结合为 细胞外蔬菜中的货物，以识别病毒反弹时间的预测因子； 3）使用7个经过验证的细胞面板 在两者下完成超过200个参数以表型研究CD4 T细胞和其他免疫细胞 休息和刺激条件以识别病毒反弹的单细胞签名； 4）评估变化 在循环抗HIV抗体或淋巴组织注射的标志物的滴度和亲和力中（包括 热吞作用的产物）作为表达储层大小的指标，可以作为预测指标 是时候反弹了。这些研究将产生大量的高维数据，这些数据将被编译， 在Biomark的生物信息学和生物统计学核心中进行了策划并进行了分析。几种生物统计方法 将被用来识别这些生物标志物并执行更大的元分析（请参阅核心描述）。 摘要，Biomark提出了一种填补艾滋病毒治疗的主要空白的创新和全面的方法 缺乏关键生物标志物预测治疗中断后病毒反弹的时间进行的研究。