Clinical significance of checkpoint regulator “Programmed death ligand-1 (PD-L1)” expression in meningioma: review of the current status
Bibliographic record
Abstract
INTRODUCTION: Meningioma is the most common primary brain tumor. Most meningiomas are benign; however, a subset of these tumors can be aggressive, presenting with early or multiple tumor recurrences that are refractory to neurosurgical resection and radiotherapy. There is no standard systemic therapy for these patients, and post-surgical management of these patients is usually complicated due to lack of accurate prediction for tumor progression. METHODS: In this review, we summarise the crucial immunosuppressive role of checkpoint regulators, including PD-1 and PD-L1 interacting in the tumor microenvironment, which has led to efforts aimed at targeting this axis. RESULTS: Since their discovery, checkpoint inhibitors have significantly improved the outcome in many types of cancers. Currently, targeted therapy for PD-1 and PD-L1 proteins are being tested in several ongoing clinical trials for brain tumors such as glioblastoma. More recently, there have been some reports implicating increased PD-L1 expression in high-grade (WHO grades II and III) meningiomas. Several clinical trials are underway to assess the efficacy of checkpoint inhibitors in the therapeutic management of patients with aggressive meningiomas. Here, we review the immune suppressive microenvironment in meningiomas, and then focus on clinical and pathological characterization and tumor heterogeneity with respect to PD-L1 expression as well as challenges associated with the assessment of PD-L1 expression in meningioma. CONCLUSION: We conclude with a brief review of ongoing clinical trials using checkpoint inhibitors for the treatment of high-grade and refractory meningiomas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".