Development of a multi-RNA signature in blood towards a rapid diagnostic test to robustly distinguish patients with acute myocardial infarction

开发血液中的多 RNA 特征以进行快速诊断测试，以强有力地区分急性心肌梗死患者

• 批准号: 10603548
• 负责人: Timothy E Sweeney
• 金额: $ 29.98万
• 依托单位: INFLAMMATIX, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603548
• 关键词: Accident and Emergency department; Acute myocardial infarction; Address; Affect; Bayesian Method; Biological Markers; Blood; Blood specimen; Chest Pain; Clinical; Clinical Sensitivity; Cohort Analysis; Collaborations; Data; Data Set; Development; Diagnosis; Diagnostic; Diagnostic tests; Electrocardiogram; Emergency Department patient; Emergency department visit; Engineering; Gene Expression; Generations; Genes; Geography; Goals; Grant; Health Care Costs; Healthcare; Heterogeneity; Immune response; Letters; Libraries; Life; Logistic Regressions; Machine Learning; Marketing; Measurement; Measures; Messenger RNA; Meta-Analysis; Methods; Modeling; Myocardial Infarction; Outcome; Patient Triage; Patient-Focused Outcomes; Patients; Performance; Phase; Phenotype; Population; Preparation; Process; Prospective Studies; RNA; Rapid diagnostics; Research; Retrospective cohort; Sampling; Sensitivity and Specificity; Sepsis; Small Business Innovation Research Grant; System; Target Populations; Testing; Time; Training; Translating; Triage; Troponin; Validation; Work; acute infection; acute symptom; analysis pipeline; bioinformatics pipeline; blind; clinical diagnostics; clinically actionable; clinically relevant; cohort; combat; data integration; genetic signature; heterogenous data; improved; instrument; machine learning classifier; machine learning method; machine learning model; machine learning pipeline; molecular diagnostics; multilayer perceptron; peripheral blood; personalized diagnostics; point of care; point of care testing; point-of-care diagnostics; product development; prototype; real world application; research clinical testing; response; risk stratification; success; support vector machine; transcriptome sequencing; transcriptomics

ABSTRACT Chest pain, the main symptom of acute myocardial infarction (AMI), accounts for ~5% of all emergency department (ED) visits. In the absence of ECG abnormalities, diagnostic gold standard for AMI relies on serial troponin (cTn) measurements which are inconclusive in 20-40% of patients, requiring additional testing and prolonged observation in the ED. A missed diagnosis of AMI without proper treatment is life threatening and thus rule-out diagnosis requires very high sensitivity. Hence, a rapid point of care (POC) test utilized as an adjunct to cTn with enhanced diagnostic performance would be revolutionary for risk stratification and timely and safe triaging of patients with suspected MI in ED. Inflammatix is a molecular diagnostics company focused on developing and bringing to market best in class, immune response based, data-driven testing. We developed a point-of-care instrument, MyRNA™, capable of quantitating up to 64 mRNAs in under 30 minutes (with <2 minutes operator time), directly from patients’ blood, in a fully disposable cartridge. We specialize in use of state-of-art multi-cohort analysis and machine learning (ML) to identify and validate robust biomarkers that generalize across real-world data heterogeneity, in diverse clinical contexts. Previous work demonstrated the potential of blood gene expression as a biomarker for MI, however a clinical test based on immune response in blood gene expression is yet to be developed. We applied our analytical framework to 6 publicly available datasets and identified a multi-gene AMI signature in peripheral blood that allows us to differentiate patients with AMI from clinically relevant controls with AUC ~ 0.95. In this project, we propose to take the AMI signature from preliminary results through research and initial development stages, up to formal clinical diagnostic development. We will generate a significant amount of independent data, leverage Inflammatix ML capabilities to further refine the mRNA signature and deliver a robust classifier ready for validation in prospective studies. In Specific Aim 1, we will generate, process, and analyze RNA-seq data for 900 blood samples from retrospective cohorts closely representing the target test population. In Specific Aim 2, we will first refine, optimize, and validate the mRNA signature; and then develop a prototype ML classifier. Specifically, we will 1) integrate expression data from all cohorts while minimizing bias; 2) apply Bayesian multi-cohort framework for final gene set selection with 300 new samples; 3) develop and evaluate discriminatory performance of AMI classifier prototype; and 4) validate the AMI classifier prototype on 600 unseen samples. These steps will produce: i) a validated set of genes for AMI; ii) an integrated dataset; and iii) a classifier prototype (AUC > 0.90), ready for clinical validation via prospective studies in Phase 2 research. This test, when developed as a cartridge on Inflammatix’s POC instrument, MyRNA™, will facilitate clinician’s triaging decisions of patients with suspected MI, improve patient outcomes, and reduce healthcare costs.

抽象的 胸痛是急性心肌梗塞（AMI）的主要症状，占所有紧急情况的5％ 部门（ED）访问。在没有心电图异常的情况下，AMI的诊断金标准依赖于序列 肌钙蛋白（CTN）测量值在20-40％的患者中尚无定论，需要进行额外的检查和 ED长期观察。未经适当治疗的未经适当治疗的AMI的诊断是威胁生命和 因此，排除诊断需要非常高的灵敏度。因此，一种快速的护理点（POC）测试用作 CTN的辅助性具有增强的诊断性能将在风险分层和及时和及时和 在ED中，可疑MI的患者的安全分类。 Inflammatix是一家分子诊断公司 基于免疫反应，数据驱动的测试。我们开发了一种保健工具Myrna™，能够 直接从患者的血液中定量30分钟内最多64个mRNA（操作员时间<2分钟）， 在完全一次性的墨盒中。我们专注于使用最先进的多局部分析和机器学习 （ML）在潜水员中识别和验证跨实际数据异质性概括的鲁棒生物标志物 临床环境。先前的工作证明了血液基因表达作为MI的生物标志物的潜力 然而，基于血液基因表达的免疫增强响应的临床测试尚未开发。我们申请了 我们针对6个公开数据集的分析框架，并在外围确定了多基因AMI签名 血液使我们能够将AMI患者与AUC 〜0.95的临床相关对照区分开。 在这个项目中，我们建议通过研究和初步研究从初步结果中获取AMI签名 开发阶段，直到正式的临床诊断发展。我们将产生大量 独立数据，利用炎症ML功能来进一步完善mRNA签名并提供强大的功能 分类器准备在前瞻性研究中进行验证。在特定目标1中，我们将生成，处理和分析 回顾性同类的900个血液样本的RNA-seq数据非常代表目标测试人群。 在特定的目标2中，我们将首先完善，优化和验证mRNA签名；然后开发原型 ML分类器。具体而言，我们将1）从所有队列中综合表达数据，同时最大程度地减少偏差； 2）申请 最终基因集选择的贝叶斯多核心框架，有300个新样品； 3）开发和评估 AMI分类器原型的歧视性能； 4）验证600上的AMI分类器原型 看不见的样本。这些步骤将产生：i）AMI的一组经过验证的基因； ii）集成数据集；和iii） 分类器原型（AUC> 0.90），可以通过2阶段研究中的前瞻性研究进行临床验证。这 测试在炎症的POC仪器上作为墨盒开发时，MyRNA™将有助于临床分类 怀疑MI的患者的决定，改善患者预后并降低医疗保健成本。