Treatment with pembrolizumab in programmed death ligand 1–positive recurrent glioblastoma: Results from the multicohort phase 1 KEYNOTE‐028 trial
Bibliographic record
Abstract
BACKGROUND: Current treatments for recurrent glioblastoma offer limited benefit. The authors report the antitumor activity and safety of the anti-programmed death 1 (anti-PD-1) immunotherapy, pembrolizumab, in programmed death ligand 1 (PD-L1)-positive, recurrent glioblastoma. METHODS: Adult patients with PD-L1-positive tumors were enrolled in the recurrent glioblastoma cohort of the multicohort, phase 1b KEYNOTE-028 study (ClinicalTrials.gov identifier, NCT02054806) and received pembrolizumab 10 mg/kg every 2 weeks for up to 2 years. The primary endpoint was investigator-assessed overall response rate according to the Response Evaluation Criteria in Solid Tumors, version 1.1. Archival tumor samples were assessed for PD-L1 expression levels (prospectively) and T-cell-inflamed gene expression profile score (retrospectively). RESULTS: After a median follow-up of 14 months (range, 2-55 months) among the 26 enrolled patients, the overall response rate was 8% (95% CI, 1%-26%). Two partial responses, lasting 8.3 and 22.8 months, occurred. Progression-free survival (median, 2.8 months; 95% CI, 1.9-8.1 months) rate at 6 months was 37.7%, and the overall survival (median, 13.1 months; 95% CI, 8.0-26.6 months) rate at 12 months was 58%. Correlation of therapeutic benefit to level of PD-L1 expression, gene expression profile score, or baseline steroid use could not be established. Treatment-related adverse events occurred in 19 patients (73%), and 5 patients experienced grade 3 or 4 events (there were no grade 5 events). Immune-mediated adverse events and infusion reactions occurred in 7 patients (27%). CONCLUSIONS: Pembrolizumab monotherapy demonstrated durable antitumor activity in a subset of patients with manageable toxicity in this small, signal-finding, recurrent glioblastoma cohort. Future studies evaluating rationally designed pembrolizumab combination regimens may improve outcomes in patients with recurrent glioblastoma.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".