Entinostat plus Pembrolizumab in Patients with Metastatic NSCLC Previously Treated with Anti–PD-(L)1 Therapy
Bibliographic record
Abstract
Abstract Purpose: New therapies are needed to treat immune checkpoint inhibitor–resistant non–small cell lung cancer (NSCLC) and identify biomarkers to personalize treatment. Epigenetic therapies, including histone deacetylase inhibitors, may synergize with programmed cell death-1 (PD-1) blockade to overcome resistance. We report outcomes in patients with anti-programmed cell death ligand-1 [PD-(L)1]–resistant/refractory NSCLC treated with pembrolizumab plus entinostat in ENCORE 601. Patients and Methods: The expansion cohort of ENCORE 601 included patients with NSCLC who previously experienced disease progression with immune checkpoint inhibitors. The primary endpoint for the phase II expansion cohort is overall response rate (ORR); safety, tolerability, and exploratory endpoints are described. Results: Of 76 treated patients, 71 were evaluable for efficacy. immune-regulated RECIST–assessed ORR was 9.2% [95% confidence interval (CI): 3.8–18.1], which did not meet the prespecified threshold for positivity. Median duration of response was 10.1 months (95% CI: 3.9–not estimable), progression-free survival (PFS) at 6 months was 22%, median PFS was 2.8 months (95% CI: 1.5–4.1), and median overall survival was 11.7 months (95% CI: 7.6–13.4). Benefit was enriched among patients with high levels of circulating classical monocytes at baseline. Baseline tumor PD-L1 expression and IFNγ gene expression were not associated with benefit. Treatment-related grade ≥3 adverse events occurred in 41% of patients. Conclusions: In anti–PD-(L)1–experienced patients with NSCLC, entinostat plus pembrolizumab did not achieve the primary response rate endpoint but provided a clinically meaningful benefit, with objective response in 9% of patients. No new toxicities, including immune-related adverse events, were seen for either drug. Future studies will continue to evaluate the association of monocyte levels and response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".