Chronic smoking related lung diseases such as COPD and IPF affect a significant portion of the population. In the last decades of the 20th century pulmonary clinical researchers dedicated significant efforts to define and identify the purest classes of chronic lung diseases based on physiology, imaging, histology and most importantly significant negative findings. These new disease classifications created a unified vocabulary of lung diseases that allowed better communication between clinicians and researchers and became rapidly and widely accepted. While critically important and widely accepted, these clinical definitions and classification did not account for a large number of patients that presented with intermediate phenotypes or some less common features and overlooked the complexity and potential overlap of some of the manifestations of emphysema/COPD and IPF. In this proposal we plan to use the lung Tissue Resource Consortium (LTRC), a large NIH sponsored collection of well characterized lung samples from patients with IPF and COPD to define known disease phenotypes and discover new disease phenotypes using gene expression microarrays, a novel platforms for high throughput parallel PCR (SmartChip) as well as novel computational approaches. We hypothesize that by applying gene expression profiling and advanced computational approaches to a large enough and well characterized cohort of samples of IPF and emphysema/COPD we will be able to identify disease relevant gene expression modules that are highly distinct, reproducible and characteristic of disease phenotypes that go beyond current disease definitions. We will address this hypothesis by performing the following specific aims: 1. To determine the gene expression signatures that globally characterize COPD and IPF. 2. To identify disease microenvironments in which disease profiles diverge or converge and their relevance to known disease phenotypes 3. To generate a disease relevant module map of IPF and COPD. 4. To validate module networks predictions on an independent sample set of COPD and IPF samples using the custom designed molecular phenotyping assay (PulmoSmartChip).
In this proposal we aim to identify molecular phenotypes that go beyond traditional disease definitions in IPF and COPD. By applying microarray gene expression profiling and systems biology approaches to a large enough and well characterized cohort of samples of IPF and emphysema/COPD we will identify disease-relevant gene expression modules that are highly distinct, reproducible and characteristic of disease phenotypes that go beyond current disease definitions. We then plan to use SmartChip a technology for Genome wide PCR and an independent validation cohort to validate these modules. Our experiments have the potential to significantly change the way we think and classify IPF and COPD and lead to discovery of new diagnostic measures and new therapeutic interventions.
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