GENT-37. THE GENETICS DRIVING RADIATION INDUCED MENINGIOMAS
Bibliographic record
Abstract
One-third of all primary central nervous system tumors in adults are meningiomas, which arise from the meninges. Although the majority of meningioma cases are not associated with an environmental risk factor, it is well established that individuals receiving radiation treatment to the CNS are susceptible to radiation-induced meningiomas. The genomic integrity of spontaneous meningiomas has been extensively profiled by whole genome sequencing and exome sequencing, providing a well-developed catalogue of meningioma associated mutations. In contrast, a comprehensive understanding of the molecular changes associated with RIMs is undetermined. We have compiled a cohort of radiation-induced meningiomas and molecularly characterized their methylome, transcriptome and mutational signatures. The integration of these three platforms at base-pair resolution has facilitated the identification of molecular changes that drive oncogenesis. We reveal that radiation induced meningiomas possess a unique mutational signature that is distinct from spontaneous meningiomas. This distinct mutational signature is dominated by genomic rearrangements that generate novel fusion genes and disrupt tumor suppressors. These findings have serious implications for the development of therapies focused towards the treatment of radiation-induced meningiomas. In summary, this is the largest cohort of profiled meningiomas to date, providing a robust characterization of distinct radiation induced meningioma features that can be exploited for the development of future therapies.
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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 | 0.001 |
| 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) | 0.008 | 0.002 |
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".