GP.06 Differential microglia and macrophage profiles in human IDH-mutant and -wildtype glioblastoma reveal therapeutic vulnerabilities
Bibliographic record
Abstract
Background: Microglia and macrophages (MMs) are the largest component of the inflammatory infiltrate in glioblastoma (GBM). However, whether there are immunophenotypic differences in isocitrate dehydrogenase (IDH)-mutated and -wildtype GBMs is unknown. Studies on specimens of untreated IDH-mutant GBMs are rare given they comprise 10% of all GBMs and often receive treatment at lower grades that can drastically alter MM phenotypes. Methods: We obtained large samples of untreated IDH-mutant and -wildtype GBMs. Using immunofluorescence techniques with single-cell automated segmentation, and comparison between single-cell RNA-sequencing (scRNA-seq) databases of human GBM, we discerned dissimilarities between GBM-associated MMs (GAMMs). Results: There are significantly fewer but more pro-inflammatory GAMMs in IDH-mutant GBMs, suggesting this contributes to the better prognosis of these tumors. Our pro-inflammatory score which combines the expression of inflammatory markers (CD68/HLA-A, -B, -C/TNF/CD163/IL10/TGFB2), Iba1 intensity, and GAMM surface area also indicates more pro-inflammatory GAMMs are associated with longer overall survival independent of IDH status. scRNA-seq analysis demonstrates microglia in IDH-mutants are mainly pro-inflammatory, while anti-inflammatory macrophages that upregulate genes such as FCER1G and TYROBP predominate in IDH-wildtype GBM. Conclusions: Taken together, these observations are the first head-to-head comparison of GAMMs in treatment-naïve IDH-mutant versus -wildtype GBMs that highlight biological disparities that can be exploited for therapeutic purposes.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".