1224 Glioblastoma: bridging mouse model insights to human tumor microenvironment using imaging mass cytometry
Notice bibliographique
Résumé
Background Human brain neoplasms, such as glioblastoma (GBM), are among the most lethal malignancies, characterized by rapid progression, therapeutic resistance and recurrence. Their complex spatial heterogeneity features compartmentalized niches like necrotic cores, tumor vascularization and suppressed immune cells, which hinders treatment efficacy. Mouse models are widely utilized in neuro-research, as their evolutionary-conserved brain architecture serves as a miniaturized model that permits visualization of whole-tissue spatial relationships. However, translating the finding to human brain requires technologies that can resolve complex high-plex spatial biology at cellular and subcellular levels without compromising on data quality. Imaging Mass Cytometry™ (IMC™) technology enables quantitative spatial proteomic evaluation of the brain without the challenges of autofluorescence, tissue degradation and spectral overlap. This study seeks to demonstrate the value of IMC for bridging translational insights from mouse studies to human disease.Methods We used the Hyperion™ XTi Imaging System to simultaneously assess multiple individual protein markers across tissues with high dynamic range. We applied a 40-marker panel composed of the Maxpar™ OnDemand Mouse Immuno-Oncology IMC Panel Kit combined with the Maxpar Neuro Phenotyping IMC Panel Kit to evaluate the spatial biology of whole mouse GBM tissue. For human GBM, the Maxpar Neuro Phenotyping IMC Panel Kit formed the backbone of a 41-marker panel supplemented by the Human Immuno-Oncology IMC Panel. Subsequent pixel-clustering using MCD™ SmartViewer and single-cell analyses quantified expression patterns of structural and immune markers in GBM of both species.Results Conserved spatial features were detected in both human and mouse GBM samples, highlighting striking heterogeneity. Organized necrotic areas were surrounded by replicating Olig2-positive cells, indicating elevated tumor growth capabilities. A high degree of vascularization was observed in non-necrotic areas. A high concentration of lymphoid and myeloid immune cells was detected in tumor margins and in necrotic cores. Analysis identified distinct tumor regions: subsets of differentiated tumor cells, immune hot and cold zones, stromal compartments, de novo vascularization and extracellular matrix deposition (fig. 1 and 2). Such detailed spatial maps of the whole tissues are critical for locating expression signatures and tissue landmarks.Conclusions IMC establishes a critical bridge between preclinical models and human therapies. Cross-species validation accelerates marker discovery using mouse models as potential predictors of human tumor microenvironment (TME) development, stratifies immunotherapy candidate selection by utilizing high-throughput whole-tissue visualization and screening, and simultaneously explores multiple biological outputs to advance translational and clinical applications.For Research Use Only. Not for use in diagnostic procedures.Abstract 1224 Figure 1Whole slide Tissue Mode IMC image and pixel-clustering analysis of mouse GBM. Metabolically active tumor cells and activation of Ras signaling pathway were detected at the periphery of the tumor, and cell replication markers were observed in virtually all tumor cellsAbstract 1224 Figure 2Whole-sample pixel-clustering analysis of human GBM. The GBM sample demonstrated a dual stem-like origin and coexistence of pro-tumorigenic and anti-tumorigenic immune responses. The expression of shown markers suggests that the TME is conductive to immune evasion, which is a hallmark of aggressive GBM
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».