Pembrolizumab or Placebo Plus Adjuvant Chemotherapy With or Without Radiotherapy for Newly Diagnosed, High-Risk Endometrial Cancer: Results in Mismatch Repair-Deficient Tumors
Bibliographic record
Abstract
Mismatch repair-deficient (dMMR) endometrial cancer (EC) is an inflamed phenotype with poor outcomes when meeting high-risk criteria and limited treatment options in the adjuvant setting. We report protocol-prespecified subgroup analysis of patients with dMMR tumors from the phase III ENGOT-en11/GOG-3053/KEYNOTE-B21 study (ClinicalTrials.gov identifier: NCT04634877) in newly diagnosed, high-risk EC after surgery with curative intent. Patients were randomly assigned to pembrolizumab 200 mg or placebo (six cycles) plus carboplatin-paclitaxel (four to six cycles) once every 3 weeks, then pembrolizumab 400 mg or placebo once every 6 weeks (six cycles), respectively. MMR status was a stratification factor. Patients received radiotherapy at investigator discretion. Investigator-assessed disease-free survival (DFS) was a primary end point. No formal hypothesis testing was performed for subgroup analysis. In the intention-to-treat population, 141 patients in the pembrolizumab arm and 140 in the placebo arm had dMMR tumors. At this interim analysis, hazard ratio for DFS favored pembrolizumab (0.31 [95% CI, 0.14 to 0.69]); median DFS was not reached in either group. Two-year DFS rates were 92.4% (95% CI, 84.4 to 96.4) and 80.2% (95% CI, 70.8 to 86.9), respectively. No new safety signals occurred. Longer-term follow-up of outcomes will be evaluated at final analysis. Preplanned subgroup analysis on the basis of the study's stratification factors suggests that pembrolizumab plus chemotherapy improves DFS and is clinically relevant for patients with dMMR tumors in the curative-intent setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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 | 0.002 |
| 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".