Precision immunoprofiling to reveal diagnostic biomarkers of latent TB infection

项目总结 结核病(TB)是全球死亡的主要原因之一，估计有20亿人 目前已被感染。潜伏性结核病感染(LTBI)是最常见的结核病感染形式，影响13 百万美国人。虽然许多LTBI患者仍然没有症状，但估计有10%的免疫功能正常的患者 LTBI将重新激活为活动性结核病，并将变得具有传染性。LTBI可以用长期的抗生素治疗 治疗；然而，潜在的副作用促使新的诊断方法的发展，这些方法可以 确定具有高特异度、再激活风险最高的患者，对谁的治疗最有益。 结核菌素皮肤试验(TST)和干扰素-γ释放试验(IGRA)通常用于结核病和肺损伤 放映。这两种测试都提供了很好的结核病暴露测量；然而，在诊断LTBI方面，两者都不是有效的 (阳性预测值&lt；5%)。此外，两者都没有提供任何基于重新激活的预后分层 风险。TST和IGRAs探针免疫记忆对结核相关抗原的挑战和我们 假设一种更细微和个性化的方法来监测对结核病的免疫反应- 特异性和非特异性抗原可能为LTBI的诊断和患者分层提供新的途径。 启用一种新的个性化LTBI诊断方法，我们建议将高吞吐量、 多元化的炎性生物标志物检测策略和强大的生物信息学工具，使 识别先前模糊的LTBI状态和再激活风险的多标记物诊断特征。 硅光子微环谐振器由于其固有的特性，成为生物标志物分析的一种使能技术 可伸缩性和多路复用功能。应用于细胞因子面板的检测，这项技术支持 结核病特异性和非特异性抗原刺激下个体样本的快速免疫分析 条件。将利用机器学习算法来分析产生的密集数据流，以便于 选择关键诊断特征，形成预测模型开发和部署的基础。这 强大的分析组合辅之以结核病和结核病临床诊断和治疗方面的深厚专业知识 LTBI，以及能够与来自国际地点的高结核病负担的对象进行协作和连接 以及接受定期和重复LTBI筛查的医护人员人群中的暴露情况。 由此产生的诊断工作流和机器学习特征选择方法将揭示多路传输 与LTBI状态(+或-)有很强正向预测相关性的生物标记物签名。这种方法还将 根据重新激活潜力进一步对LTBI+受试者进行分层，从而提供了一种全新的方法 确定最可能从治疗干预中受益的受试者。这个项目的最终结果将是 基于精确医学的新诊断策略大大优于当前最先进的诊断策略，并提供 有可能改变目前的临床实践。

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.