Precision immunoprofiling to reveal diagnostic biomarkers of latent TB infection

精确免疫分析揭示潜伏结核感染的诊断生物标志物

• 批准号: 10247473
• 负责人: Ryan C Bailey
• 金额: $ 74.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247473
• 关键词: Affect; Algorithms; American; Antibiotic Therapy; Antibiotics; Antigens; Bioinformatics; Biological Assay; Biological Markers; Clinical; Clinical Treatment; Collaborations; Complex; Cytokine Network Pathway; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Management; Eligibility Determination; Generations; Goals; Gold; Health Personnel; Immune; Immune response; Immunocompetent; Immunologic Markers; Immunologic Memory; Immunologic Monitoring; Individual; Infection; Inflammatory; Informatics; Interferons; International; Location; Machine Learning; Measurement; Measures; Modeling; Patients; Peripheral; Peripheral Blood Mononuclear Cell; Plasma; Population; Predictive Value; Prevention strategy; Regimen; Residual state; Risk; Sampling; Schedule; Silicon; Specificity; Stratification; Technology; Testing; Therapeutic Intervention; Translations; Tuberculin Test; Tuberculosis; Whole Blood; antigen challenge; base; bioinformatics tool; biomarker signature; clinical Diagnosis; clinical practice; cytokine; data streams; diagnostic accuracy; diagnostic biomarker; feature selection; high risk; immune function; immunoregulation; improved; individual variation; latent infection; machine learning algorithm; model development; monocyte; mortality; novel diagnostics; novel strategies; patient stratification; personalized approach; personalized diagnostics; photonics; precision medicine; predictive marker; predictive modeling; prevent; prognostic; prospective; response; screening; side effect; targeted treatment; tool; treatment strategy; tuberculosis treatment

PROJECT SUMMARY Tuberculosis (TB) is among the leading causes of mortality worldwide with an estimated 2 billion individuals currently infected. Latent tuberculosis infection (LTBI) is the most common form of TB infection affecting 13 million Americans. While many with LTBI remain asymptomatic, an estimated 10% of immunocompetent patients with LTBI will reactivate to active TB, and will become infectious. LTBI is treatable with a prolonged antibiotic treatment; however, potential side effects motivate the development of new diagnostic approaches that can identify with high specificity patients at the highest risk of reactivation, for who therapy would be most beneficial. The tuberculin skin test (TST) and interferon-γ release assays (IGRAs) are commonly used for TB and LTBI screening. Both tests provide good measures of TB exposure; however, neither is effective at diagnosing LTBI (positive predictive values <5%). Moreover, neither provide any prognostic stratification based upon reactivation risk. Both the TST and IGRAs probe immunological memory to TB-related antigen challenges and we hypothesize that a more nuanced and personalized approach to monitoring immune responses to both TB- specific and non-specific antigens might reveal new approaches to LTBI diagnosis and patient stratification. Enabling a new, individualized approach to LTBI diagnostics, we propose to combine high throughput, multiplexed inflammatory biomarker detection strategies and powerful bioinformatics tools that allow for the identification of previously obscured multi-marker diagnostic signatures of LTBI status and reactivation risk. Silicon photonic microring resonators are an enabling technology for biomarker analysis due to their intrinsic scalability and multiplexing capabilities. Applied to the detection of cytokine panels, this technology supports the rapid immune profiling of individual samples under both TB-specific and non-specific antigen stimulation conditions. Machine learning algorithms will be utilized to analyze the resulting dense data streams to facilitate selection of key diagnostic signatures forming the basis for predictive model development and deployment. This powerful analytical combination is supplemented by deep expertise in clinical diagnosis and treatment of TB and LTBI, and an enabling collaboration and connection to subjects from an international location with high TB burden and exposure in a healthcare worker population subjected to regularly-scheduled and repeated LTBI screening. The resulting diagnostic workflow and machine learning feature selection approaches will reveal multiplexed biomarker signatures that have strong positive predictive correlation with LTBI status (+ or -). This approach will also further stratify LTBI+ subjects on the basis of reactivation potential, thus providing a fundamentally new approach to identifying subjects that are most likely to benefit from therapeutic intervention. The end result of this project will be a new precision medicine-based diagnostic strategy that is vastly superior to the current state-of-the-art and offers the potential to transform current clinical practice.

项目摘要 结核病（TB）是全世界死亡的主要原因之一，估计有20亿人 目前感染。潜伏性结核病感染（LTBI）是最常见的结核病感染形式， 百万美国人。虽然许多LTBI患者仍无症状，但估计有10%的免疫功能正常的患者 LTBI会重新激活为活动性结核病，并具有传染性。LTBI可通过长期抗生素治疗 然而，潜在的副作用促使开发新的诊断方法， 以高特异性确定处于最高再激活风险的患者，因为治疗将是最有益的。 结核菌素皮肤试验（TST）和γ-干扰素释放试验（IGRA）通常用于TB和LTBI 筛选这两种测试都提供了很好的测量结核病暴露的方法;但是，在诊断LTBI方面都不是有效的 （阳性预测值<5%）。此外，也没有提供任何基于再激活的预后分层 风险TST和IGRA都探测了对TB相关抗原攻击的免疫记忆， 假设一种更细致和个性化的方法来监测对结核病和结核病的免疫反应， 特异性和非特异性抗原可能揭示LTBI诊断和患者分层的新方法。 实现一种新的，个性化的LTBI诊断方法，我们建议联合收割机高通量， 多重炎症生物标志物检测策略和强大的生物信息学工具， 识别先前模糊的LTBI状态和再激活风险的多标记诊断特征。 硅光子微谐振器是一种用于生物标志物分析的使能技术，这是由于其固有的特性。 可扩展性和多路复用能力。应用于细胞因子面板的检测，该技术支持 在TB特异性和非特异性抗原刺激下对单个样品进行快速免疫分析 条件将利用机器学习算法来分析由此产生的密集数据流， 选择关键的诊断特征，形成预测模型开发和部署的基础。这 强大的分析组合加上结核病临床诊断和治疗方面的深厚专业知识， LTBI，以及与来自结核病负担高的国际地区的受试者的合作和联系 和暴露在一个医疗保健工作者群体进行定期安排和重复LTBI筛选。 由此产生的诊断工作流程和机器学习特征选择方法将揭示多重 与LTBI状态（+或-）具有强阳性预测相关性的生物标志物特征。这种方法还将 根据再激活潜力进一步对LTBI+受试者进行分层，从而提供了一种全新的方法， 确定最有可能受益于治疗干预的受试者。该项目的最终结果将是 一种新的基于精确医学的诊断策略，大大上级当前最先进的技术水平， 改变当前临床实践的潜力。