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.

项目摘要/摘要 肺纤维化(PF)是一种以深度瘢痕为特征的破坏性间质性肺疾病(ILD)。在……里面 严重的肺泡炎，一般在3-5年内死亡。虽然当前的分类标准指导诊断， 对于PF的预后和治疗，PF表型的高度异质性限制了这些标准的应用。 许多被归类为特发性肺间质纤维化的患者可能属于另一种肺功能亚类，而相当少数的 病人是无法分类的。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基因研究之外，限制了他们研究结果的概括性。 在这项建议中，我的目标是改进PF患者的疾病分类和预后预测。我将使用以下工具完成此操作 从不同种族的患者那里收集的尖端统计工具和DNA样本。我的中心假设是 将来自不同种族的基因组生物标记物纳入基于集群的模型将导致更好的PF 分类。我将首先对PF相关基因变异进行有针对性的基因分型，并测量端粒长度 以确定他们在患有PF的美国种族群体中的差异。然后，我将使用以下内容派生和验证PF集群模型 来自不同种族的PF人群的临床数据，并确定基因组数据对预后的额外价值 预测。最后，使用这个模型，我将确定治疗效果对患者结果的异质性。 有可能被国家警署登记在册。成功完成此建议书将产生经过验证的 跨肺纤维化亚组簇(CLASS-PF)模型适用于不同种族的患者 改进PF分类和结果预测。我在临床预测建模方面的初步研究，并完成 公共卫生科学硕士学位的获得为这项调查的成功奠定了坚实的基础。 我的长期职业目标是利用来自不同种族的基因数据来改善临床决策和 肺泡炎患者的预后。为了实现这一目标，我制定了一项职业发展计划，将提供 在基因组分析、统计遗传学、大数据分析和临床方面提供卓越的指导和培训 审判。ILD、遗传学和风险分层建模方面的领先专家将指导我。我还组装了一个 在端粒紊乱、临床试验和生物信息库方面具有专业知识的多学科咨询委员会 正在处理。概述的工作将在芝加哥大学进行，这是一所已建立的机构 在以患者为中心的研究方面取得了卓越的成绩，并拥有丰富的协作资源。这个K23 奖项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 是时候达到这些现实的里程碑并获得独立开发基因组预测的技能了 整合临床表型数据以在临床试验中进行后续验证的工具。这项工作将提供一种 为跨不同种族研究PF和改进PF分类提供了宝贵的药物基因组资源 和预测，从而将发现引导到翻译中，并最终应用于临床。