NF2 loss-of-function and hypoxia drive radiation resistance in grade 2 meningiomas
Bibliographic record
Abstract
BACKGROUND: World Health Organization Grade 2 meningiomas (G2Ms) often recur and resist therapies. Grade 2 meningiomas with histopathological necrosis have been associated with worse local control (LC) after radiation therapy, but the drivers and biomarkers of radiation resistance in G2Ms remain unknown. METHODS: We performed genetic sequencing and histopathological analysis of 113 G2Ms and investigated the role of genetic and microenvironmental factors on clonogenic survival after ionizing radiation. We performed transcriptional profiling of our in vitro model and 18 human G2M tumors by bulk RNA sequencing as well as 8 G2Ms by single nuclei RNA sequencing. RESULTS: NF2 loss-of-function (LOF) mutations were associated with necrosis in G2Ms (P = .0127). Tumors with NF2 mutation and necrosis had worse post-radiation LC compared to NF2 wildtype tumors without necrosis (P = .035). Under hypoxic conditions, NF2 knockdown increased radiation resistance in vitro (P < .001). Bulk RNA sequencing revealed NF2- and hypoxia-specific changes and a 50-gene set signature specific to radiation-resistant, NF2 knockdown, and hypoxic cells, which distinguished NF2 mutant/necrotic patient G2Ms by unsupervised clustering. Enrichment analysis revealed downregulation of apoptosis pathway genes and upregulation of proliferation-associated genes and genes normally downregulated after UV radiation exposure in NF2-mutant/necrotic tumor cells, which were validated with functional assays. CONCLUSIONS: NF2 LOF in the setting of hypoxia confers radiation resistance through transcriptional programs that reduce apoptosis and promote proliferation. These pathways may identify tumors resistant to radiation and represent therapeutic targets that in the future could improve LC in patients with radiation resistant G2Ms.
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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) | 0.001 | 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".