Programs, origins and immunomodulatory functions of myeloid cells in glioma
Bibliographic record
Abstract
Gliomas are incurable malignancies notable for having an immunosuppressive microenvironment with abundant myeloid cells, the immunomodulatory phenotypes of which remain poorly defined1. Here we systematically investigate these phenotypes by integrating single-cell RNA sequencing, chromatin accessibility, spatial transcriptomics and glioma organoid explant systems. We discovered four immunomodulatory expression programs: microglial inflammatory and scavenger immunosuppressive programs, which are both unique to primary brain tumours, and systemic inflammatory and complement immunosuppressive programs, which are also expressed by non-brain tumours. The programs are not contingent on myeloid cell type, developmental origin or tumour mutational state, but instead are driven by microenvironmental cues, including tumour hypoxia, interleukin-1β, TGFβ and standard-of-care dexamethasone treatment. Their relative expression can predict immunotherapy response and overall survival. By associating the respective programs with mediating genomic elements, transcription factors and signalling pathways, we uncover strategies for manipulating myeloid-cell phenotypes. Our study provides a framework to understand immunomodulation by myeloid cells in glioma and a foundation for the development of more-effective immunotherapies. A study of myeloid cells in gliomas, a type of brain tumour, used a factor-based computational framework to reveal four immunomodulatory gene-expression programs that are expressed across myeloid cell types, driven by microenvironmental cues and predictive of therapeutic response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".