Identification of PD-L2, B7-H3 and CTLA-4 immune checkpoint proteins in genetic subtypes of meningioma
Bibliographic record
Abstract
Meningioma is the most common brain tumor in adults. Surgical resection remains the primary treatment. No chemotherapy exists. However, gene mutations now could explain ~ 80% of meningioma and targeted therapies based on these are being investigated. Furthermore, with the recent discovery of PD-L1 in malignant meningioma, clinical trials using immunotherapy have commenced. Here, we report for the first time the expression profiles of immune checkpoint proteins PD-L2, B7-H3 and CTLA-4 in meningioma and their association to common gene mutations. PD-L2 and B7-H3 expression was significantly greater than all immune checkpoint proteins studied, and particularly elevated in patients with gene mutations affecting the PI3K/AKT/mTOR pathway. CTLA-4 expressing CD3+ lymphocytes were observed in atypical and malignant meningioma and tumors harboring a PIK3CA or SMO mutation. These results identify novel targets for immunotherapy irrespective of grade and distinguish potential patient populations based on genetic classification for stratification into checkpoint inhibitor clinical trials.
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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 | 0.000 |
| 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".