Pembrolizumab in vaginal and vulvar squamous cell carcinoma: a case series from a phase II basket trial
Bibliographic record
Abstract
Vaginal and vulvar squamous cell carcinoma (SCC) are rare tumors that can be challenging to treat in the recurrent or metastatic setting. We present a case series of patients with vaginal or vulvar SCC who were treated with single-agent pembrolizumab as part of a phase II basket clinical trial to evaluate efficacy and safety. Two cases of recurrent and metastatic vaginal SCC, with multiple prior lines of systemic chemotherapy and radiation, received pembrolizumab. One patient had significant reduction (81%) in target tumor lesions prior to treatment discontinuation at cycle 10 following confirmed progression of disease with new metastatic lesions (stable disease by irRECIST criteria). In contrast, the other patient with vaginal SCC discontinued treatment after cycle 3 due to disease progression. Both patients had PD-L1 positive vaginal tumors and tolerated treatment well. One case of recurrent vulvar SCC with multiple surgical resections and prior progression on systemic carboplatin had a 30% reduction in her target tumor lesions following pembrolizumab treatment with a PD-L1 positive tumor. Treatment was discontinued for grade 3 mucositis after cycle 5. Pembrolizumab may provide some clinical benefit to some patients with vaginal or vulvar SCC and is overall safe to utilize in this population. Future studies are needed to evaluate the efficacy of pembrolizumab in these rare tumor types and to identify predictive biomarkers of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".