Pembrolizumab for the treatment of programmed death–ligand 1‒positive advanced carcinoid or pancreatic neuroendocrine tumors: Results from the KEYNOTE‐028 study
Bibliographic record
Abstract
BACKGROUND: Despite a protracted disease course and multiple available therapies, patients with well-differentiated neuroendocrine tumors (NETs) inevitably experience disease progression. Programmed death-ligand 1 (PD-L1) has been associated with NET progression and prognosis. The multicohort, phase 1 KEYNOTE-028 study (ClinicalTrials.gov identifier NCT02054806) evaluated the activity and safety of the anti-programmed cell death protein 1 immunotherapy pembrolizumab in patients with well-differentiated or moderately-differentiated NETs. METHODS: Patients with PD-L1-positive, locally advanced or metastatic carcinoid or well-differentiated or moderately-differentiated pancreatic NETs (pNETs) were enrolled into separate cohorts and received pembrolizumab at a dose of 10 mg/kg every 2 weeks for up to 2 years. The objective response rate was the primary endpoint (as per Response Evaluation Criteria in Solid Tumors version 1.1, by investigator review). Safety was a secondary endpoint. RESULTS: Of 170 and 106 patients, respectively, who had evaluable samples among those screened for the carcinoid and pNET cohorts, 21% and 25%, respectively, had PD-L1-positive tumors; of these, 25 and 16 patients, respectively, were eligible and treated. The median follow-up was 20 months (range, 2-35 months) and 21 months (range, 5-32 months), respectively. The objective response rate was 12.0% (95% CI, 2.5%-31.2%) and 6.3% (95% CI, 0.2%-30.2%), respectively; 3 partial responses occurred among the carcinoid cohort and 1 among the pNET cohort. The median duration of response in the carcinoid cohort was 9.2 months (range, 6.9-11.1 months), and was not reached in the pNET cohort. No complete responses occurred. Treatment-related adverse events occurred in 68% and 69% of patients, respectively, most often diarrhea (7 patients in the carcinoid cohort and 4 patients in the pNET cohort) and fatigue (6 patients in each cohort). Hypothyroidism was the most common immune-mediated adverse event (5 patients in the carcinoid cohort and 2 patients in the pNET cohort). CONCLUSIONS: Pembrolizumab demonstrated antitumor activity in a subset of patients with NETs and was well-tolerated.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".