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.

摘要 胸痛是急性心肌梗死（AMI）的主要症状，约占所有急诊的5 部门（艾德）探访。在没有ECG异常的情况下，AMI的诊断金标准依赖于连续的 20-40%的患者的肌钙蛋白（cTn）测量结果不确定，需要进行额外的测试， 在急诊室进行长期观察。如果没有适当的治疗而漏诊AMI， 因此排除诊断需要非常高的灵敏度。因此，快速护理点（POC）测试用作 具有增强诊断性能的cTn辅助治疗对于风险分层和及时 ED中疑似MI患者的安全分诊。 Inflammatix是一家分子诊断公司，专注于开发并将一流的产品推向市场， 基于免疫反应的数据驱动测试。我们开发了一种即时检测仪器，MyRNA™，能够 在30分钟内（操作时间<2分钟）直接从患者血液中定量多达64种mRNA， 在完全一次性的盒中。我们专注于使用最先进的多队列分析和机器学习 (ML)识别和验证强大的生物标志物，这些生物标志物在现实世界的数据异质性中普遍存在， 临床背景。先前的工作证明了血液基因表达作为MI生物标志物的潜力， 然而，基于血液基因表达中的免疫应答的临床测试还有待开发。我们应用 我们的分析框架，6公开可用的数据集，并确定了多基因AMI签名在外周血淋巴细胞， 血液，使我们能够区分AMI患者与临床相关的控制AUC ~ 0.95。 在这个项目中，我们建议通过研究和初步的初步结果， 发展阶段，直到正式的临床诊断发展。我们将产生大量的 独立数据，利用Inflammatix ML功能进一步完善mRNA签名，并提供强大的 分类器准备在前瞻性研究中进行验证。在具体目标1中，我们将生成，处理和分析 来自回顾性队列的900份血液样本的RNA-seq数据，接近代表目标测试人群。 在具体目标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。具体来说，我们将1）整合所有队列的表达数据，同时最大限度地减少偏倚; 2）应用 贝叶斯多队列框架，用于300个新样本的最终基因集选择; 3）开发和评估 AMI分类器原型的区分性能;以及4）在600 看不见的样本这些步骤将产生：i）经验证的AMI基因集; ii）综合数据集;以及iii） 分类器原型（AUC > 0.90），准备通过2期研究中的前瞻性研究进行临床验证。这 当在Inflammatix的POC仪器MyRNA™上开发一个检测盒时，将有助于临床医生的分类 这将有助于患者做出更好的决策，改善患者的预后，降低医疗成本。