Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive and heterogeneous interstitial lung disease of unknown origin with a low survival rate. There are few treatment options available due to the fact that mechanisms underlying disease progression are not well understood, likely because they arise from dysregulation of complex signaling networks spanning multiple tissue compartments. To better characterize these networks, we used systems-focused data-driven modeling approaches to identify cross-tissue compartment (blood and bronchoalveolar lavage) and temporal proteomic signatures that differentiated IPF progressors and non-progressors. Partial least squares discriminant analysis identified a signature of 54 baseline (week 0) blood and lung proteins that differentiated IPF progression status by the end of 80 weeks of follow-up with 100% cross-validation accuracy. Overall we observed heterogeneous protein expression patterns in progressors compared to more homogenous signatures in non-progressors, and found that non-progressors were enriched for proteomic processes involving regulation of the immune/defense response. We also identified a temporal signature of blood proteins that was significantly different at early and late progressor time points (p < 0.0001), but not present in non-progressors. Overall, this approach can be used to generate new hypothesis for mechanisms associated with IPF progression and could readily be translated to other complex and heterogeneous diseases.
Highlights
Idiopathic pulmonary fibrosis (IPF) is a heterogeneous and irreversible interstitial pneumonia, with symptoms including progressive cough, shortness of breath, and respiratory failure, with a median survival of only 3–5 years post diagnosis[1]
We applied data-driven modeling approaches to blood and bronchoalveolar lavage (BAL) samples from patients enrolled in the COMET (Correlating Outcomes With Biochemical Markers to Estimate Timeprogression in Idiopathic Pulmonary Fibrosis) study to gain insight into cross-tissue compartment and temporal mechanisms of action associated with IPF progression
We evaluated a subset of participants (n = 59) with an IPF diagnosis enrolled in the COMET IPF study
Summary
Idiopathic pulmonary fibrosis (IPF) is a heterogeneous and irreversible interstitial pneumonia, with symptoms including progressive cough, shortness of breath, and respiratory failure, with a median survival of only 3–5 years post diagnosis[1]. One potential explanation for failure to validate a specific prognostic biomarker is that disease progression is driven by dysregulated proteomic signaling networks rather than individual proteins This hypothesis is supported by the multiple known actions of the two FDA-approved drugs that slow IPF progression, n intedanib[19] and pirfenidone[19]. Data-driven (“machine learning”) modeling approaches are able to integrate data across multiple tissue compartments and assays to identify signatures of factors that are associated with the disease state[24,25] They serve as valuable tools for network inference by identifying co-varying factors that aid in generating new hypotheses for mechanisms of action based on protein interaction pathways rather that individual proteins. Overall these results provide insight into mechanisms of IPF progression that could be investigated further in follow-up murine studies
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
