Proteomic Profiling of Idiopathic Pulmonary Fibrosis Progression Trajectory

特发性肺纤维化进展轨迹的蛋白质组学分析

• 批准号: 10708256
• 负责人: Justin M Oldham
• 金额: $ 71.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708256
• 关键词: Address; Biological; Biological Markers; Blood; Categories; Cessation of life; Characteristics; Clinical Trials; Collaborations; Custom; Data; Data Science; Development; Ensure; Fibrosis; Foundations; Funding; Geography; Goals; Heterogeneity; Interstitial Lung Diseases; Investigation; Knowledge; Linear Regressions; Lung Transplantation; Machine Learning; Malignant Neoplasms; Mediator; Methodology; Modeling; Molecular; National Heart, Lung, and Blood Institute; Outcome; Patients; Pharmaceutical Preparations; Phase III Clinical Trials; Phenotype; Pirfenidone; Plasma; Positioning Attribute; Progressive Disease; Proteins; Proteomics; Publishing; Pulmonary Fibrosis; Registries; Research; Research Personnel; Resources; Sample Size; Speed; Technology; Validation; Vital capacity; Work; antifibrotic treatment; biomarker driven; biomarker identification; biomarker signature; clinically actionable; cohort; drug development; experience; fibrotic interstitial lung disease; idiopathic pulmonary fibrosis; innovation; mortality; new therapeutic target; nintedanib; novel; novel marker; novel therapeutics; performance tests; pointed protein; precision medicine; predictive marker; prospective; proteomic signature; pulmonary function; pulmonary function decline; recruit; tool; treatment effect; treatment group

Project Summary Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease characterized by progressive lung function decline survival worse than most cancers. Despite high mortality, the trajectory of IPF progression is highly heterogeneous. This heterogeneity hampers drug development, as large sample sizes are required when conducting IPF clinical trials to ensure enough patients experience forced vital capacity (FVC) decline to detect a treatment effect. The ability to predict IPF progression trajectory, however, remains elusive. Emerging proteomic platforms provide a valuable opportunity to address this gap in knowledge. In Aim 1, we will validate proteomic biomarkers of IPF progression. Using a high-throughput, semi-quantitative proteomic platform, we will determine plasma concentration for ~3000 analytes in the UK-based PROFILE cohort (n=550) and perform. targeted analysis of 258 preliminary biomarkers of one-year categorical IPF progression identified in a discovery cohort from the Pulmonary Fibrosis Foundation (n=813). In aim 2, we will derive and validate a proteomic signature of FVC trajectory. Machine learning will be applied to semi-quantitative proteomic data to select proteins for quantitative signature development. A custom, quantitative platform will be developed and used to quantify selected proteins, with data used to develop a proteomic signature of one-year FVC trajectory. This signature will then be assessed in three prospectively recruited IPF cohorts. In aim 3, we will determine whether anti-fibrotic therapy modulates biomarkers of IPF progression. Quantitative proteomic data will be generated for 240 treatment-naïve patients and repeated at 12-months after 80 patients each received pirfenidone, nintedanib or no therapy. Longitudinal change in biomarker concentration and test performance characteristics will be compared between treatment groups before and after anti-fibrotic initiation. Successful completion of this proposal will identify novel molecular mediators of IPF progression and result in a highly significant biomarker signature to predict IPF progression trajectory. This tool has high potential to speed drug development through clinical trial enrichment, making precision medicine a reality in patients with IPF.

项目总结