PXD052594 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis |
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. |
HostingRepository | PRIDE |
AnnounceDate | 2024-10-17 |
AnnouncementXML | Submission_2024-10-17_09:58:47.729.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | Manfred Heller |
SpeciesList | scientific name: Mus musculus (Mouse); NCBI TaxID: 10090; |
ModificationList | phosphorylated residue |
Instrument | Bruker Daltonics timsTOF series |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2024-05-27 08:34:13 | ID requested | |
⏵ 1 | 2024-10-17 09:58:48 | announced | |
Publication List
10.1172/jci.insight.181757; |
Lauer D, Magnin CY, Kolly LR, Wang H, Brunner M, Chabria M, Cereghetti GM, Gabry, ś HS, Tanadini-Lang S, Uldry AC, Heller M, Verleden SE, Klein K, Sarbu AC, Funke-Chambour M, Ebner L, Distler O, Maurer B, Gote-Schniering J, Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis. JCI Insight, 9(15):(2024) [pubmed] |
Keyword List
submitter keyword: interstitial lung disease,radiomics, proteomics, multiomics, imaging, lung fibrosis, nintedanib |
Contact List
Janine Gote-Schniering |
contact affiliation | University of Bern, Department of BioMedical Research (DBMR), Lung Precision Medicine (LPM) Program |
contact email | janine.gote-schniering@unibe.ch |
lab head | |
Manfred Heller |
contact affiliation | Proteomics and Mass Spectrometry Core Facility, Departement for Biomedical Research, University of Berne |
contact email | pmscf.dbmr@unibe.ch |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
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PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD052594
- Label: PRIDE project
- Name: Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis