Reclassifying Pulmonary Arterial Hypertension Into Immune Phenotypes Using Machine Learning

使用机器学习将肺动脉高压重新分类为免疫表型

• 批准号: 10192823
• 负责人: Andrew John Sweatt
• 金额: $ 19.35万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10192823
• 关键词: Address; Bioinformatics; Biological; Biological Markers; Biology; Biometry; Blood; Cells; Classification; Classification Scheme; Clinical; Clinical Trials; Clinical Trials Design; Collaborations; Complex; Computing Methodologies; Critical Care; DNA Sequence Alteration; Data; Development; Disease; Disease Progression; Evolution; Foundations; Functional disorder; Genes; Genetic; Growth Factor; Immune; Immune Targeting; Immunity; Immunology; Immunomodulators; Inflammation; Inflammatory; Investigation; Lead; Link; Lung; Machine Learning; Mentored Patient-Oriented Research Career Development Award; Mentors; Molecular; Mononuclear; Morbidity - disease rate; Network-based; Outcome; Pathway Analysis; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Physicians; Plasma; Public Health; Rare Diseases; Research; Research Personnel; Rest; Role; Sampling; Selection for Treatments; Severities; Severity of illness; Signal Pathway; System; Systemic disease; Testing; Therapeutic; Time; Toxin; Transcript; Translating; Universities; Variant; Work; base; biobank; biomarker discovery; career; chemokine; clinical phenotype; clinical subtypes; cohort; computerized tools; cytokine; data repository; design; differential expression; disorder risk; experience; improved; innovation; insight; molecular phenotype; mortality; multidisciplinary; novel; overexpression; patient subsets; precision medicine; prospective; protein complex; public repository; pulmonary arterial hypertension; response; targeted treatment; therapeutic target; transcriptomics; trend

This is a K23 award application for Dr. Andrew Sweatt, a pulmonary/critical care physician and young investigator at Stanford University who is establishing a niche in pulmonary arterial hypertension (PAH) precision phenotyping. His work centers on using machine learning to reclassify PAH, where hidden patterns are detected in high-throughput molecular data to uncover new phenotypes. The existing PAH clinical classification does not inform therapy decisions, and outcomes are overall poor with a ‘one-size-fits-all’ treatment approach. There is a critical need for molecular phenotyping efforts, to develop classification schemes that sit closer to pathobiology and identify therapeutically-targetable patient subsets. Dr. Sweatt’s K23 builds on an innovative foundational study where he used machine learning to cluster PAH patients based on blood immune profiling, without guidance from clinical features. This agnostic approach uncovered 4 immune phenotypes with distinct cytokine profiles that are independent of clinical subtypes and stratify disease risk. These findings indicate that inflammation is a viable platform for PAH reclassification. Extensive research has implicated inflammation in PAH and multiple immune-targeting therapies are under active investigation, but these studies rest on the assumption that a common pathophenotype exists. The objective of Dr. Sweatt’s K23 is to better understand PAH immune phenotypes in terms of their longitudinal evolution, mechanistic underpinnings, and therapeutic implications. First, he will perform serial cytokine profiling in two observational cohorts (Stanford, USA; Sheffield, UK) to reassess immune phenotypes during the disease course (Aim 1). Based on preliminary data, dynamic phenotype switches may occur in some patients and reflect changes in clinical disease severity. Next, he will integrate blood transcriptomic profiling and apply sophisticated computational tools to provide phenotype-specific mechanistic insights (Aim 2). He postulates that distinct transcriptomic profiles will link phenotypes to specific signaling pathways and immune cell subsets. Findings will be validated using multi-cohort data from public repositories. Finally, he will perform post-hoc cytokine profiling in two recent PAH trial cohorts where immune modulators were tested, to assess if therapy responses differ across phenotypes (Aim 3). His research could help identify patients who will respond to specific therapies, inform clinical trial designs, lead to biomarker discovery, and define novel biology in PAH. The K23 will provide Dr. Sweatt with the critical support needed to transition to an independent research career and be a leader in PAH precision phenotyping. His K23 objectives are to gain experience in PAH clinical phenotyping/cohort building, expand expertise in bioinformatics, cultivate collaboration, and translate findings to new hypotheses for R01 development. He will be guided by a committed team of multidisciplinary mentors (Roham Zamanian [expert in PAH clinical trial design/biomarkers], Marlene Rabinovitch [leader in translational PAH research], and Purvesh Khatri [pioneer in bioinformatics]) and scientific advisors (Mark Nicolls [translational PAH immunology], PJ Utz [immunology], and Manisha Desai [biostatistics]).

这是一个K23奖申请安德鲁Sweatt博士，肺/重症监护医生和年轻的研究者 在斯坦福大学，他正在建立一个肺动脉高压（PAH）精确度的利基市场， 表型分析他的工作集中在使用机器学习来重新分类PAH，其中隐藏的模式被检测到 在高通量分子数据中发现新的表型。现有PAH临床分类不 知情的治疗决定，结果是整体较差的“一刀切”的治疗方法。有一个 迫切需要分子表型的努力，发展分类方案，坐在更接近病理生物学 并识别可治疗的患者亚群。Sweatt博士的K23建立在一个创新的基础上， 在这项研究中，他使用机器学习根据血液免疫分析对PAH患者进行聚类， 从临床特点指导。这种不可知的方法发现了4种具有不同细胞因子的免疫表型 独立于临床亚型和分层疾病风险的特征。这些发现表明 炎症是PAH重新分类的可行平台。广泛的研究表明， PAH和多种免疫靶向治疗正在积极研究中，但这些研究依赖于 假设存在共同的病理表型。Sweatt博士的K23的目标是更好地理解 PAH免疫表型的纵向演变、机制基础和治疗 影响首先，他将在两个观察组群（斯坦福大学，美国;谢菲尔德， UK）重新评估疾病过程中的免疫表型（目的1）。根据初步数据，动态 表型转换可能发生在一些患者中，并反映临床疾病严重程度的变化。接下来，他将 整合血液转录组分析并应用复杂计算工具来提供表型特异性 机械的洞察力（目标2）。他假设不同的转录组特征将表型与特定的 信号通路和免疫细胞亚群。将使用来自公众的多队列数据验证结果 储存库。最后，他将在最近的两个PAH试验队列中进行事后细胞因子分析， 测试了调节剂，以评估治疗应答是否在表型之间不同（目的3）。他的研究可以 帮助识别对特定疗法有反应的患者，为临床试验设计提供信息， 发现和定义PAH的新生物学。K23将为Sweatt博士提供所需的关键支持， 过渡到独立的研究生涯，并成为PAH精确表型分析的领导者。K23目标 获得PAH临床表型/队列建立的经验，扩展生物信息学专业知识，培养 合作，并将研究结果转化为R 01发展的新假设。他将被一个坚定的 多学科导师团队（Roham Zamanian [PAH临床试验设计/生物标志物专家]，Marlene Rabinovitch [翻译PAH研究的领导者]和Purvesh Khatri [生物信息学的先驱]）和科学 顾问（Mark Nicolls [翻译PAH免疫学]、PJ乌茨[免疫学]和Manisha Desai [生物统计学]）。