Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disease that is unresponsive to current therapy. While it carries a median survival of less than 3 years its rate of progression varies widely between patients. We hypothesized that studying the gene expression profiles of physiologically stable patients and those in which the disease progressed rapidly after the initial diagnosis would aid in the search for biomarkers and contribute to the understanding of disease pathogenesis. We generated 12 Idiopathic Pulmonary Fibrosis (IPF) lung parenchyma SAGE profiles. Initial cluster analysis including 8 other public available lung SAGE libraries verified that the IPF transcriptome is distinct from normal lung tissue and other lung diseases like COPD. In order to identify candidate markers of disease progression we segregated the IPF SAGE profiles in two groups based on clinical parameters regarding lung volume and lung function.
Project description:The response to a 4 hour treatment with TGFbeta (4 ng/ml) was evaluated in lung fibroblasts derived from three controls (normal periphery of resected tumor), open lung biopsies from three patients with idiopathic pulmonary fibrosis (usual interstitial pneumonia pattern on biopsy) and from three patients with fibrosing alveolitis associated with systemic sclerosis (fibrotic non specific interstitial pneumonia pattern on biopsy). Lung fibroblasts were grown to confluence in DMEM with 10% fetal calf serum. At confluence, lung fibroblasts were serum-deprived overnight, and exposed to either 4 ng/ml of activated TGF-Ã1 (R&D Systems) or serum-free culture medium with 0.1% BSA for four hours.
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disease that is unresponsive to current therapy. While it carries a median survival of less than 3 years its rate of progression varies widely between patients. We hypothesized that studying the gene expression profiles of physiologically stable patients and those in which the disease progressed rapidly after the initial diagnosis would aid in the search for biomarkers and contribute to the understanding of disease pathogenesis.
Project description:Antifibrotic therapy with nintedanib is the clinical mainstay in the treatment of progressive fibrosing interstitial lung disease (ILD). High-dimensional medical image analysis, known as radiomics, provides quantitative insights into organ-scale pathophysiology, generating digital disease fingerprints. Here, we used an integrative analysis of radiomic and proteomic profiles (radioproteomics) to assess whether changes in radiomic signatures can stratify the degree of antifibrotic response to nintedanib in (experimental) fibrosing ILD. Unsupervised clustering of delta radiomic profiles revealed two distinct imaging phenotypes in mice treated with nintedanib, contrary to conventional densitometry readouts, which showed a more uniform response. Integrative analysis of delta radiomics and proteomics demonstrated that these phenotypes reflected different treatment response states, as further evidenced on transcriptional and cellular levels. Importantly, radioproteomics signatures paralleled disease- and drug related biological pathway activity with high specificity, including extracellular matrix (ECM) remodeling, cell cycle activity, wound healing, and metabolic activity. Evaluation of the preclinical molecular response-defining features, particularly those linked to ECM remodeling, in a cohort of nintedanib-treated fibrosing ILD patients, accurately stratified patients based on their extent of lung function decline. In conclusion, delta radiomics has great potential to serve as a non-invasive and readily accessible surrogate of molecular response phenotypes in fibrosing ILD. This could pave the way for personalized treatment strategies and improved patient outcomes. References: Hallal, Mahmoud, Sophie Braga-Lagache, Jovana Jankovic, Cedric Simillion, Rémy Bruggmann, Anne-Christine Uldry, Ramanjaneyulu Allam, Manfred Heller, and Nicolas Bonadies. 2021. “Inference of Kinase-Signaling Networks in Human Myeloid Cell Line Models by Phosphoproteomics Using Kinase Activity Enrichment Analysis (KAEA).” BMC Cancer 21 (1): 789.