59 Microglia and macrophages display heterogeneous phenotypes in IDH-mutant and -wildtype glioblastomas
Bibliographic record
Abstract
Background: CNS innate immune cells, microglia and macrophages (MMs), are the largest component of the inflammatory infiltrate in glioblastoma (GBM). They initially participate in tumor surveillance, but are subverted by GBM. Immunotherapies have proven incredibly successful in cancers such as melanoma, but not against GBM in part because GBM-associated MMs are not well understood. We hypothesized the content and inflammatory phenotype of MMs in GBM is variable between patients. We suspect MMs in IDH-wildtype and –mutant GBMs display divergent inflammatory phenotypes that helps explain the latter's better prognosis. Understanding GBM-associated MM heterogeneity will allow for better immunotherapy development and selection. Methods: MMs were isolated from untreated human IDH-wildtype and -mutant GBMs using flow cytometry and cultured for collection of conditioned media and analysis of secretory products. Automated segmentation with a high-content analysis system was used to quantitate MM content and inflammatory phenotype in frozen sections. New bioinformatics techniques allowed the comparison of MM profiles in publicly available single-cell RNA-sequencing databases with IDH-wildtype and -mutant GBMs. Results: Surprisingly marked variation in MM content exists between GBMs ranging from ~0-70%. A mixture of pro- and anti-inflammatory MMs are found in each GBM. Interestingly, IDH-mutant GBM-associated MMs were more activated than MMs in IDH-wildtype GBMs. Conclusions: Taken together, the highly variable MM content and phenotype of GBMs suggests the success of immunotherapies hinges on taking a precision medicine approach. MM-rich GBMs would benefit more from therapies that target them. MM activation in IDH-mutant GBMs may contribute to better patient prognoses.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".