GENE-37. PATHWAY ANALYSIS OF RADIATION-INDUCED MENINGIOMAS REVEALS THAT TUMOURS WITH NF2-FUSION HAVE UPREGULATION OF INFLAMMATORY PATHWAYS
Bibliographic record
Abstract
As more pediatric cancer patients are surviving into adulthood from improved oncological therapy there is an increase in the long-term sequelae of treatment. Radiation-induced meningiomas (RIMs), one such secondary effect, demonstrate significantly different biology than sporadic meningiomas (SMs) and demonstrate clinically more aggressive behaviour. RIMs have been shown to not harbor mutations in common SM associated driver genes, such as NF2, TRAF7, KLF4, PIK3CA and SMO; however, a subset of RIMs have genomic rearrangements with NF2 gene fused to a reciprocal gene, which was previously not seen in SMs. We aimed to compare and contrast the gene expression in RIMs with and without the NF2-fusion event. A primary cohort of 7 RIMs with NF2-fusion and 12 RIMs with wildtype NF2 had RNA sequencing performed using the Illumina HiSeq platform. RNA-Seq expression profiles were analyzed using edgeR, available through BioConductor. Gene Set Enrichment Analysis was performed using Cytoscape®. Principle component analysis of the RNA-Seq data showed that 5/7 RIMs with NF2-fusion were similar to one another. One of the outliers was the only NF2-fusion RIM without a 1p chromosomal arm loss. Significant differentially expressed genes included IGF-1 (log2 of fold change or logFC = 4.14, p=2.65E-05), MME (logFC = 3.03, p = 3.56E-03) and MMP16(logFC = 2.47, p=8.14E-03). Notably, the pathway analysis demonstrated that there was an upregulation of immune/inflammation pathways involving PD-1, STAT-4, IGF-1 and other genes in RIMs with NF2-fusion compared to RIMs with NF2 wildtype. RIMs with the NF2-fusion event have a distinct gene expression profile, with upregulation of inflammatory pathways. Further studies need to be performed to validate these findings using immunohistochemistry. These results suggest that NF2-fusion RIMs may be candidates for immunotherapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".