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.

项目概要/摘要 一套强有力的方法将大大加快潜在艾滋病毒治疗方法的开发和测试 预测其临床有效性的生物标志物仍然可以作为替代终点。 此类生物标志物将：1）加速艾滋病毒治疗领域的进展，就像血浆病毒载量一样。 2) 为患者提供参与分析治疗中断的费用 (ATI) 通过减少试验次数来提供更高程度的临床保护；3) 提供生物试验； 控制潜在储存库的分子和生化途径的线索，4) 作为磁铁； 吸引生物技术和制药公司更积极地参与 HIV 治疗研究。 项目团队（Warner Greene、Gilad Doitsh、Garry Nolan、Katie Pollard、Satish Pillai、Nadia Roan 和 Robert Siliciano）将寻找能够准确预测治疗后反弹时间的强生物标志物 这种生物标志物对于治疗领域具有很大的价值，因为它们可以让内部人士进行预测。 患者可以停止接受 ART 而不会出现病毒复发的时间 125。 参与 ATI 之前和病毒感染时获得的四项不同 ATI 试验的 HIV 感染志愿者 这些患者包括 30 名在急性感染期间接受治疗的患者。 预计会表现出较慢的反弹时间 为了识别病毒和宿主衍生的生物标志物，该团队将 1) 部署令人兴奋的“一流”数字液滴 PCR 检测，可选择性检测和定量完整片段 储存库中的原病毒 DNA (IPDA)——因为正是原病毒总数中的这一关键的一小部分 含有介导反弹的传染性原病毒，少量完整的原病毒可能会出现 强有力的生物标志物预测病毒反弹需要更长的时间；2）利用下一代超深度测序 分析 CD4 T 和其他免疫细胞中的细胞 RNA 和 miRNA，并同时对 DNA、RNA 和 DNA 进行测序 miRNA 在血浆中（以及在有限的受试者亚群的脑脊液中）自由循环或以结合形式存在 细胞外囊泡中的货物以确定病毒反弹时间的预测因子；3) 使用 7 个经过验证的 CyTOF 组合 包含超过 200 个参数，用于在两种条件下对 CD4 T 细胞和其他免疫细胞进行表型研究 静息和刺激条件下确定病毒反弹时间的单细胞特征；4) 评估变化； 循环抗 HIV 抗体或淋巴组织炎症标志物的滴度和亲合力（包括 细胞焦亡的产物）作为表达库大小的指标，可以作为预测因子 这些研究将产生大量高维数据，并进行汇编， 在 BioMark 的生物信息学和生物统计学核心中策划和分析了几种生物统计学方法。 将用于识别这些生物标志物并进行更大的荟萃分析（参见核心描述）。 总之，BioMark 提出了一种创新且全面的方法来填补艾滋病毒治疗的重大空白 由于缺乏预测治疗中断后病毒反弹时间的关键生物标志物而进行的研究。