56 Unique Immune Microenvironment in NF2-Fusion Positive Radiation Induced Meningiomas
Bibliographic record
Abstract
Introduction: Radiation-induced meningiomas (RIMs) are increasing in prevalence as cancer patients live longer. Our laboratory has demonstrated that RIMs have a unique genomic landscape compared to sporadic meningiomas. Notably, a subset of RIMs harbor genomic rearrangement resulting in NF2 gene fusion with a nonrecurrent reciprocal gene. We aimed to compare the gene expression of NF2-Fusion and NF2-Wild Type (NF2-WT) RIMs. Methods: RNA sequencing using Illumina HiSeq was performed on 7 NF2-Fusion and 12 NF2-WT RIMs. Short read sequences obtained from sequencing were mapped to reference human genome(hg19). We performed differential expression analysis using edgeR statistical packages. Pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Immunohistochemistry was performed to validate findings. Results: Principal component analysis revealed that 5/7 of NF2-Fusion RIMs had similar gene expression profiles. One outlier had no chromosome 1p loss like the other NF2-Fusion RIMs. Pathway analysis demonstrated there was an upregulation of immune pathways in NF2-Fusion RIMs. Immunohistochemistry of PD-L1 revealed that 0/7 and 7/7 of NF2-Fusion tumors had positive expression in tumoral and inflammatory cells, respectively. In comparison, 6/12 of RIMs had tumoral and inflammatory cell expression of PD-L1. In addition, there was a higher CD3 lymphocyte infiltration in NF2-WT (42.2 vs. 12.4 number of cells per HPF). Discussion: Preliminary data in our lab demonstrates that NF2-Fusion tumors have a distinct immune microenvironment compared to NF2-WT tumors. Although pathway analysis indicates that NF2-Fusion RIMs have overexpression of immune pathways, immunohistochemistry reveals that inflammatory cells have positive PD-L1 expression, suggestive of immune burnout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".