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: 9892577
• 负责人: Ayodeji Adegunsoye
• 金额: $ 16.12万
• 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/9892577
• 关键词: Address; Advisory Committees; Affect; African; American; Asians; Autoimmune Process; Big Data; Biological; 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; Development Plans; Diagnosis; Diagnostic; Disease; Disease Outcome; Enrollment; Epidemiology; European; Exclusion; Foundations; Frequencies; Future; Genetic; Genetic Risk; Genetic study; Genomics; Genotype; Goals; Health Sciences; Heterogeneity; Hispanics; Hospitals; Institution; Interstitial Lung Diseases; Investigation; K-Series Research Career Programs; Length; Leukocytes; MUC5B gene; Master' s Degree; 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; Public Health; Publishing; Pulmonary Fibrosis; Quality of life; Race; Registries; Research Training; Resources; Respiratory physiology; Risk stratification; Sampling; Scientist; Single Nucleotide Polymorphism; Societies; Solid; Subgroup; TOLLIP gene; Testing; Time; Training; Translations; Treatment outcome; Universities; Validation; Variant; Work; base; biobank; career; career development; clinical decision-making; clinical implementation; clinical phenotype; cohort; disease classification; diverse data; ethnic minority population; genetic association; genetic variant; genomic biomarker; genomic data; idiopathic pulmonary fibrosis; improved; multidisciplinary; outcome forecast; outcome prediction; patient oriented research; patient registry; peripheral blood; personalized medicine; phenotypic data; precision medicine; predictive modeling; prognostic; prospective; racial diversity; racial minority; respiratory; skills; stem; success; 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.

项目总结/摘要 肺纤维化（PF）是一种以严重瘢痕形成为特征的破坏性间质性肺病（ILD）。在 重度PF，一般在3-5年内死亡。虽然目前的分类标准指导诊断， PF的预后和治疗，PF表型的实质异质性限制了这些标准的实用性。 许多被归类为特发性PF的患者可能属于备选PF亚类，并且显著少数的 患者无法分类。最近的基因组学进展已经确定了影响PF异质性的因素 然而，将主要种族群体排除在这些遗传研究之外，限制了其研究结果的普遍性。 在这个建议中，我的目标是改善PF患者的疾病分类和结局预测。 尖端的统计工具和从不同种族的患者中收集的DNA样本。我的中心假设是 将来自不同种族的基因组生物标志物纳入基于聚类的模型将导致更好的PF 分类.我将首先对PF相关基因变异进行靶向基因分型，并测量端粒长度 以确定其在美国种族群体与PF的变化。然后，我将推导和验证PF集群模型，使用 来自种族多样性PF人群的临床数据，并确定基因组数据对结局的附加价值 预测.最后，使用该模型，我将确定治疗效果对患者结局的异质性 前瞻性入组国家PF登记研究。成功完成此建议书将产生一个有效的 适用于不同种族患者的肺纤维化亚组聚类（CLASS-PF）模型， 改善PF分类和结果预测。我的初步研究在临床预测建模，并完成 硕士学位的公共卫生科学提供了一个坚实的基础，在这项调查的成功。 我的长期职业目标是利用来自不同种族的遗传数据来改善临床决策， 为了实现这一目标，我制定了一个职业发展计划， 在基因组分析、统计遗传学、大数据分析和临床医学方面的培训 审判ILD、遗传学和风险分层建模方面的领先专家将指导我。 多学科咨询委员会，具有端粒疾病、临床试验和生物储存方面的专业知识 处理.概述的工作将在芝加哥大学进行，这是一个建立了 在以患者为导向的研究方面有着卓越的记录，以及丰富的合作资源。K23 该奖项对于成功实现本提案中概述的目标至关重要，因为它将提供专门的 时间来实现这些现实的里程碑，并获得独立开发基因组预测的技能 整合临床表型数据的工具，用于随后的临床试验验证。这项工作将提供 宝贵的药物基因组学资源，用于研究不同种族的PF，并改善PF分类 和预测，从而将发现转化为翻译，并最终转化为临床应用。