Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis
David Lauer, Cheryl Y. Magnin, Luca R. Kolly, Huijuan Wang, Matthias Brunner, Mamta Chabria, Grazia M. Cereghetti, Hubert S. Gabryś, Stephanie Tanadini-Lang, Anne-Christine Uldry, Manfred Heller, Stijn E. Verleden, Kerstin Klein, Adela-Cristina Sarbu, Manuela Funke-Chambour, Lukas Ebner, Oliver Distler, Britta Maurer, Janine Gote-Schniering
David Lauer, Cheryl Y. Magnin, Luca R. Kolly, Huijuan Wang, Matthias Brunner, Mamta Chabria, Grazia M. Cereghetti, Hubert S. Gabryś, Stephanie Tanadini-Lang, Anne-Christine Uldry, Manfred Heller, Stijn E. Verleden, Kerstin Klein, Adela-Cristina Sarbu, Manuela Funke-Chambour, Lukas Ebner, Oliver Distler, Britta Maurer, Janine Gote-Schniering
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Research Article Pulmonology Therapeutics

Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis

Abstract

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 performed 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 2 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 patients with ILD, accurately stratified patients based on their extent of lung function decline. In conclusion, delta radiomics has great potential to serve as a noninvasive and readily accessible surrogate of molecular response phenotypes in fibrosing ILD. This could pave the way for personalized treatment strategies and improved patient outcomes.

Authors

David Lauer, Cheryl Y. Magnin, Luca R. Kolly, Huijuan Wang, Matthias Brunner, Mamta Chabria, Grazia M. Cereghetti, Hubert S. Gabryś, Stephanie Tanadini-Lang, Anne-Christine Uldry, Manfred Heller, Stijn E. Verleden, Kerstin Klein, Adela-Cristina Sarbu, Manuela Funke-Chambour, Lukas Ebner, Oliver Distler, Britta Maurer, Janine Gote-Schniering

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Figure 4

Delta radiomics stratifies nintedanib-treated patients with PF-ILD according to lung function decline.

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Delta radiomics stratifies nintedanib-treated patients with PF-ILD accor...
(A) Workflow schematic. We retrospectively included patients (n = 19 of 359 patients) with PF-ILD undergoing treatment with nintedanib at Bern University Hospital and the SWISS-IIP cohort fulfilling the inclusion criteria. For each patient, changes in pulmonary function parameters and radiomic features were calculated between pre- and posttreatment stages. Unsupervised k-means clustering of patients was performed on subsets of experimentally defined delta radiomic features, including response-defining features (n = 54) and features positively enriched for ECM remodeling pathway activity (n = 8). The resulting clusters were investigated for differences in clinical outcome parameters and patient demographics. (B) Heatmap displaying the results of unsupervised k-means clustering of the Z-scored response-defining delta radiomic feature set (n = 54) in the PF-ILD cohort. The feature class for each variable and the enrichment of the radioproteomic association module for Reactome pathways is indicated. (C and D) Box plots comparing FVC (percent predicted and liters) delta and baseline level between clusters A1–A3. Mann-Whitney U test was used to compare the groups (E) Associations of clusters A1–A3 with clinical and demographic parameters in the PF-ILD cohort. Fisher’s exact test was used to compare the categorical variables. (F) Heatmap displaying the results of unsupervised k-means subclustering of the Z-scored features whose radioproteomic association modules are positively enriched with ECM remodeling Reactome pathway activity (n = 8) in the PF-ILD cohort. The feature class for each variable and the enrichment of the radioproteomic association module for Reactome pathways is indicated. (G and H) Box plots comparing FVC (% pred and liters) delta and baseline level between subclusters B1 and B2. Mann-Whitney U test was used to compare the groups.