Microenvironmental Heterogeneity in Brain Malignancies
Bibliographic record
Abstract
Brain tumors are among the deadliest malignancies. The brain tumor microenvironment (TME) hosts a unique collection of cells, soluble factors, and extracellular matrix components that regulate disease evolution of both primary and metastatic brain malignancies. It is established that macrophages and other myeloid cells are abundant in the brain TME and strongly correlate with aggressive phenotypes and distinct genetic signatures, while lymphoid cells are less frequent but are now known to have a pronounced effect on disease progression. Different types of brain tumors vary widely in their microenvironmental contexture, and the proportion of various stromal components impacts tumor biology. Indeed, emerging evidence suggests an intimate link between the molecular signature of tumor cells and the composition of the TME, shedding light on the mechanisms which underlie microenvironmental heterogeneity in brain cancer. In this review, we discuss the association between TME composition and the diverse molecular profiles of primary gliomas and brain metastases. We also discuss the implications of these associations on the efficacy of immunotherapy in brain malignancies. An appreciation for the causes and functional consequences of microenvironmental heterogeneity in brain cancer will be of crucial importance to the rational design of microenvironment-targeted therapies for these deadly diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".