Pulmonary fibrosis is a devastating disease characterized by the progressive replacement of alveolar tissue with fibrotic scar resulting in impaired gas exchange and reduced lung compliance. The cause of pulmonary fibrosis often cannot be identified (idiopathic pulmonary fibrosis), but pulmonary fibrosis can also be attributed to connective tissue diseases such as systemic sclerosis (SSc-ILD), or to environmental, occupational, or drug exposures. Diagnostic approaches to distinguish different causes of pulmonary fibrosis are imprecise, and few laboratory features have been identified that predict response to treatment.1,2 There is a growing body of evidence that systems biology approaches to the analysis of gene expression profiling data can provide insight into the pathobiology of disease, and can form the basis for the development of novel biomarkers for diagnosis, risk stratification, and guidance of therapy. My long-term goal is to leverage systems biology approaches including machine learning to integrate transcriptomic, flow cytometric, biochemical, immunohistochemical, and imaging data to elucidate the underlying molecular mechanisms and biological processes driving pulmonary fibrosis and to improve diagnosis, risk-stratification, and treatment selection for patients with pulmonary fibrosis.
|Effective start/end date||7/1/19 → 6/30/22|
- Francis Family Foundation (Agmt 3/6/19)
Idiopathic Pulmonary Fibrosis
Connective Tissue Diseases
Gene Expression Profiling