A rapidly evolving landscape: immune checkpoint inhibitors in pretreated metastatic endometrial cancer
Bibliographic record
Abstract
Background and objectives: Endometrial cancer is a common malignancy and recurrences can be fatal. Although platinum-pretreated endometrial tumors are commonly treated with anthracyclines and taxanes, there is no current standard of care. Both immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) have been extensively assessed in this setting, including tumors selected for DNA mismatch repair (MMR)/microsatellite instability (MSI) and programmed death-ligand 1 expression status. This review will provide evidence-based guidance on use of ICIs alone or in combination with TKIs in patients with pretreated advanced, persistent, or recurrent metastatic endometrial cancer. Data sources and methods: Randomized phase II-III trials in unselected populations pretreated, recurrent, or metastatic endometrial cancer and phase I-II trials in biomarker selected populations were identified from PubMed as well as conference proceedings using the key search terms 'immune checkpoint inhibitors', 'endometrial cancer', and 'advanced'. Results: A total of nine eligible studies were identified assessing ICI monotherapy for biomarker-selected or ICI plus TKI combinations and a dual ICI regimen for biomarker-unselected patients with pretreated recurrent or metastatic endometrial cancer. In MMR/MSI-selected tumors, five phase I/II studies evaluated ICI monotherapy indicating benefit in these patients. Only the phase III KEYNOTE-775 trial reported a statistically significant overall survival improvement for the combination of pembrolizumab plus lenvatinib compared with docetaxel or paclitaxel regardless of MMR/MSI status. Conclusions: Pembrolizumab plus lenvatinib is indicated for patients with unselected pretreated metastatic endometrial cancer and pembrolizumab monotherapy is a preferred option for patients with MMRd/MSI-H tumors.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".