A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas
VILLAIN, Laura
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
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Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
VILLAIN, Laura
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
FERTE, Thomas
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
HEJBLUM, Boris
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Réduire
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Langue
EN
Article de revue
Ce document a été publié dans
International Journal of Cancer. 2025-04-11
Résumé en anglais
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with ...Lire la suite >
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with unpredictable recurrence. A better classification of patient risk of recurrence is needed. Here, we describe a multimodal analytical pipeline integrating imaging, transcriptomic, and proteomic profiles using machine learning to improve patient stratification with novel signatures of patient risk of recurrence based on gene expression, protein level, and imaging. Additionally, we describe the biological characteristics of IDH-mutant glioma subtypes categorized by positron emission tomography (PET) and histology, and we reinforce the integration of positron emission tomography (PET) metrics in the classification of IDH-mutant gliomas. We identify a gene signature (KRT19, RUNX3, and SCRT2) and a protein signature (ATXN10, EIF4H, ITGAV, and NCAM1) associated with an increased risk of early recurrence. Furthermore, we integrated these markers with imaging-derived features, obtaining a better stratification of IDH-mutant glioma patients in comparison to histomolecular classification alone.< Réduire
Mots clés en anglais
IDH‐Mutant Glioma
Multimodal Integration
Proteomic
Radiomic
Transcriptomic