1224 Glioblastoma: bridging mouse model insights to human tumor microenvironment using imaging mass cytometry
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".