Improving Pulmonary Fibrosis Classification with Genomics Informed Phenotypic Clusters

利用基因组学表型簇改善肺纤维化分类

• 批准号: 10581653
• 负责人: Ayodeji Adegunsoye
• 金额: $ 16.24万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581653
• 关键词: Address; Advisory Committees; Affect; African; American; Asian; Autoimmune; Big Data; Blood specimen; Cessation of life; Characteristics; Chest; Chicago; Cicatrix; Classification; Classification Scheme; Clinical; Clinical Data; Clinical Trials; Cluster Analysis; Cohort Studies; Collaborations; DNA; Data; Data Analyses; Dedications; Deterioration; Development Plans; Diagnosis; Diagnostic; Disease; Disease Outcome; Enrollment; Epidemiology; European; Exclusion; Foundations; Frequencies; Future; Genetic; Genetic study; Genomics; Genotype; Goals; Health Sciences; Heterogeneity; Hispanic; Hospitals; Institution; Interstitial Lung Diseases; Investigation; Journals; K-Series Research Career Programs; Length; Leukocytes; MUC5B gene; Master of Science; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Minority; Modeling; Outcome; Patient-Focused Outcomes; Patients; Pharmacogenomics; Pharmacotherapy; Phenotype; Physicians; Population; Population Heterogeneity; Prevalence; Prognosis; Public Health; Publishing; Pulmonary Fibrosis; Quality of life; Race; Registries; Research; Resources; Sampling; Scientist; Single Nucleotide Polymorphism; Societies; Solid; Subgroup; TOLLIP gene; Testing; Time; Training; Translations; Treatment outcome; Universities; Validation; Variant; Work; biobank; career; career development; clinical decision-making; clinical implementation; clinical phenotype; clinical predictive model; cohort; disease classification; diverse data; ethnic minority; genetic association; genetic variant; genomic biomarker; genomic data; genomic predictors; idiopathic pulmonary fibrosis; improved; multidisciplinary; outcome prediction; patient oriented research; patient registry; peripheral blood; personalized medicine; phenotypic data; precision medicine; predictive modeling; predictive tools; prognostic; prognostication; prospective; pulmonary function; racial diversity; racial minority; racial population; respiratory; risk stratification; skill acquisition; skills; stem; success; survival prediction; telomere; tool; treatment effect; treatment response

PROJECT SUMMARY/ABSTRACT Pulmonary fibrosis (PF) is a destructive interstitial lung disease (ILD) characterized by profound scarring. In severe PF, death generally ensues within 3-5 years. While current classification criteria guide diagnosis, prognosis and treatment of PF, substantial heterogeneity in PF phenotypes limit the utility of these criteria. Many patients classified as idiopathic PF may belong to alternative PF subclasses, and a significant minority of patients are unclassifiable. Recent genomic advances have identified factors that influence heterogeneity in PF however, exclusion of major racial groups from these genetic studies limits the generalizability of their findings. In this proposal, I aim to improve disease classification and outcome prediction in patients with PF. I will do this using cutting-edge statistical tools and DNA samples collected from patients across diverse races. My central hypothesis is that inclusion of genomic biomarkers from diverse races into a cluster-based model will lead to better PF classification. I will first perform targeted genotyping for PF-associated gene variants and measure telomere lengths to determine their variation across US racial groups with PF. I will then derive and validate a PF cluster model using clinical data from racially diverse PF populations and determine the additional value of genomic data on outcome prediction. Finally, using this model, I will determine heterogeneity of treatment effect on outcomes across patients prospectively enrolled in national PF registries. Successful completion of this proposal will result in a validated Clusters Across Subgroups of Pulmonary Fibrosis (CLASS-PF) model applicable in patients from diverse races to improve PF classification and outcome prediction. My preliminary studies in clinical prediction modeling, and completion of a Master’s Degree in Public Health Sciences have provided a solid foundation for success in this investigation. My long-term career goal is to utilize genetic data from diverse races to improve clinical decision-making and outcomes for patients with PF. To achieve this, I have formulated a career development plan that will provide exceptional mentorship, and training in genomic analyses, statistical genetics, big-data analysis and clinical trials. Leading experts in ILD, genetics, and risk-stratification modeling will mentor me. I have also assembled a multidisciplinary advisory committee with expertise in telomere disorders, clinical trials, and biorepository processing. The outlined work will be performed at the University of Chicago, an institution with established track record of excellence in patient-oriented research, and abundant resources for collaboration. This K23 award is fundamental to achieve successfully the goals outlined in this proposal, as it will provide dedicated time to attain these realistic milestones and acquire the skills to independently develop genomic prediction tools that integrate clinical phenotype data for subsequent validation in clinical trials. This work will provide an invaluable pharmacogenomic resource for studying PF across diverse races, and improve PF classification and prediction thus channeling discovery into translation and ultimately to clinical implementation.

项目摘要/摘要 肺纤维化（PF）是一种以深刻疤痕为特征的破坏性间质性肺疾病（ILD）。在 严重的PF，通常在3 - 5年内发生死亡。而当前分类标准指导诊断，但 PF的预后和处理，PF表型的实质异质性限制了这些标准的效用。 许多被归类为特发性PF的患者可能属于替代PF子类，很少有 患者无法分类。最近的基因组进步确定了影响PF异质性的因素 但是，从这些遗传研究中排除主要种族群体会限制其发现的普遍性。 在此提案中，我旨在改善PF患者的疾病分类和结果预测。我会使用 从潜水员种族的患者收集的尖端统计工具和DNA样品。我的中心假设是 将潜水员种族的基因组生物标志物包括在基于集群的模型中将导致更好的PF 分类。我将首先对PF相关的基因变异进行靶向基因分型，并测量端粒长度 确定他们在美国种族群体中使用PF的变化。然后，我将使用并验证使用PF群集模型 来自大致潜水PF种群的临床数据，并确定基因组数据的额外价值 预言。最后，使用此模型，我将确定治疗对患者结局影响的异质性 该提案的成功完成将导致经过验证 肺部纤维化亚组（类PF）模型的簇适用于来自潜水员种族的患者 改善PF分类和结果预测。我在临床预测建模和完成方面的初步研究 公共卫生科学硕士学位为这项调查为成功提供了坚实的基础。 我的长期职业目标是利用来自潜水员种族的遗传数据来改善临床决策和 PF患者的结果。为了实现这一目标，我制定了一项职业发展计划，将提供 特殊的精神训练和基因组分析，统计遗传学，大数据分析和临床的培训 试验。 ILD，遗传学和风险分层建模领域的主要专家将心理我。我也集会了 多学科咨询委员会具有端粒疾病，临床试验和生物验证方面的专业知识 加工。概述的工作将在芝加哥大学进行，该机构已建立 以患者为导向的研究和合作资源丰富的资源的卓越记录。这个K23 奖励是成功实现本提案中概述的目标的基础，因为它将提供专门的目标 是时候获得这些现实的里程碑并获得独立发展基因组预测的技能 整合临床表型数据以在临床试验中进行验证的工具。这项工作将提供 宝贵的药物基因组资源，用于研究跨潜水员种族的PF，并改善PF分类 并预测将发现引导到翻译，并最终转化为临床实施。