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PXD052594

PXD052594 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleRadioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis
DescriptionAntifibrotic 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.
HostingRepositoryPRIDE
AnnounceDate2024-10-17
AnnouncementXMLSubmission_2024-10-17_09:58:47.729.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterManfred Heller
SpeciesList scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationListphosphorylated residue
InstrumentBruker Daltonics timsTOF series
Dataset History
RevisionDatetimeStatusChangeLog Entry
02024-05-27 08:34:13ID requested
12024-10-17 09:58:48announced
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 affiliationUniversity of Bern, Department of BioMedical Research (DBMR), Lung Precision Medicine (LPM) Program
contact emailjanine.gote-schniering@unibe.ch
lab head
Manfred Heller
contact affiliationProteomics and Mass Spectrometry Core Facility, Departement for Biomedical Research, University of Berne
contact emailpmscf.dbmr@unibe.ch
dataset submitter
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Dataset FTP location
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