Improving Pulmonary Fibrosis Classification with Genomics Informed Phenotypic Clusters
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.