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Massively high-throughput profiling of the circulating antibody pool for identification of diagnostic signatures with utility for stroke triage

对循环抗体库进行大规模高通量分析，用于识别诊断特征并用于中风分类

基本信息

批准号：
10457459
负责人：
Grant C O'Connell
金额：
$ 24.15万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-27 至 2024-06-30
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT: Stroke is currently the third leading cause of death and leading cause of permanent disability in the United States. Due to the time-efficacy relationship associated with acute stroke interventions, tools which allow for accurate stroke diagnosis during triage have the potential to streamline care and improve patient outcomes. Early transport, transfer, and referral decisions are often made by emergency medical services personnel, triage nurses, and emergency physicians with limited neurological expertise using symptom-based stroke recognition scales. Unfortunately, these assessments exhibit limited accuracy in triage scenarios, and it is currently estimated that up to 30% of patients experiencing stroke are misdiagnosed at first clinician contact, leading to life threatening delays in care. As a result, there has been a push for the identification of stroke-specific blood biomarkers which could be rapidly measured at the point-of-care to help clinicians without extensive neurological expertise make better-informed early triage decisions. It is becoming increasingly evident that the peripheral immune system is intricately involved in stroke pathology, and may be targetable for the development of stroke diagnostics. Not only is there a rapid systemic inflammatory response to the acute injury, but emerging evidence suggests that peripheral immune changes may precede symptom onset and in some cases trigger the acute event itself. The peripheral blood contains up to 1018 unique antibodies targeting antigens associated with nearly every adaptive immune response an individual has experienced in their lifetime, and the repertoire of antibodies found in an individual’s blood can serve as a detailed molecular fingerprint of their immune history as well as current immune status. In the proposed investigation, we aim to identify stroke-associated alterations to the circulating antibody pool which could be used to aid in stroke recognition during triage. To address this aim, peripheral blood will be sampled from a group of consecutive patients suspected of stroke at emergency department admission. Upon final clinical diagnosis, patients will be divided into either a confirmed stroke group or a stroke mimic group. Peptide arrays comprised of 330,000 unique probes will be used to comprehensively assess the binding characteristics of each patient’s peripheral blood antibody pool, and a machine-learning approach will be used to identify a pattern of binding which can optimally discriminate between groups. This work will be the first ever to take a comprehensive approach to profiling the circulating antibody pool in stroke to globally search for disease-specific patterns of alterations; the level of throughput, in combination with the use of powerful machine-learning methods, will increase the odds of identifying diagnostically robust biomarker profiles. Furthermore, diagnostically useful probes identified via peptide array can be readily adapted for use at the point-of-care, providing a clear path to clinical use. This novel, innovative, and highly translational workflow will address an area of dire clinical need; we fully expect to identify a set of candidate peptide probes which will provide the immediate foundation for the development of a rapid point-of-care stroke triage diagnostic.

项目总结/摘要：中风目前是美国第三大死亡原因和永久性残疾的主要原因。由于与急性卒中干预相关的时效关系，在分诊期间进行中风诊断有可能简化护理并改善患者的预后。早期运输、转移和转诊决定通常由紧急医疗服务人员做出，护士和急诊医生，神经专业知识有限，鳞片不幸的是，这些评估在分流方案中表现出有限的准确性，并且目前据估计，多达30%的中风患者在第一次与临床医生接触时被误诊，危及生命的护理延误。因此，一直在推动中风特异性血液的鉴定可以在护理点快速测量的生物标志物，以帮助临床医生，而无需广泛的神经系统疾病专业知识可以更好地做出早期分类决策。越来越明显的是，免疫系统复杂地参与中风病理，并且可能是中风发展的靶点诊断不仅急性损伤会引起快速的全身炎症反应，提示外周免疫变化可能先于症状发作，在某些情况下触发急性事件本身。外周血中含有多达1018种独特的抗体，这些抗体靶向与近个体一生中经历的每一次适应性免疫反应以及抗体库可以作为他们免疫史的详细分子指纹，目前的免疫状态。在拟议的调查中，我们的目标是确定中风相关的改变，循环抗体库，可用于在分诊期间帮助识别中风。为了实现这一目标，将从一组疑似卒中的连续患者中采集外周血样本部门录取。在最终临床诊断后，患者将被分为确诊卒中组或中风模拟组。由330，000个独特探针组成的肽阵列将用于全面评估每个患者的外周血抗体池的结合特征，以及机器学习方法将被用来确定一种结合模式，可以最佳地区分组之间。这项工作将是有史以来第一个采取全面的方法来分析中风中的循环抗体库，全球搜索特定于疾病的改变模式;吞吐量水平与使用相结合强大的机器学习方法，将增加识别诊断上强大的生物标志物的可能性数据区.此外，通过肽阵列鉴定的诊断上有用的探针可以容易地适用于即时护理，为临床使用提供了明确的途径。这种新颖、创新和高度转化的工作流程将解决一个迫切的临床需求领域;我们完全期望确定一组候选肽探针，为快速护理点卒中分类诊断的发展提供直接基础。