Safety, Efficacy, and Biomarker Analyses of Dostarlimab in Patients with Endometrial Cancer: Interim Results of the Phase I GARNET Study
Bibliographic record
Abstract
PURPOSE: This interim report of the GARNET phase I trial presents efficacy and safety of dostarlimab in patients with advanced or recurrent endometrial cancer (EC), with an analysis of tumor biomarkers as prognostic indicators. PATIENTS AND METHODS: A total of 153 patients with mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) and 161 patients with mismatch repair proficient (MMRp)/microsatellite stable (MSS) EC were enrolled and dosed. Patients received 500 mg dostarlimab every 3 weeks for four cycles, then 1,000 mg every 6 weeks until progression. Primary endpoints were objective response rate (ORR) and duration of response (DOR). RESULTS: A total of 143 patients with dMMR/MSI-H EC and 156 patients with MMRp/MSS EC were evaluated for efficacy. ORR was 45.5% (n = 65) and 15.4% (n = 24) for dMMR/MSI-H EC and MMRp/MSS EC, respectively. Median DOR for dMMR/MSI-H EC was not met (median follow-up, 27.6 months); median DOR for MMRp/MSS EC was 19.4 months. The ORRs by combined positive score (CPS) ≥1 status were 54.9% and 21.7% for dMMR/MSI-H EC and MMRp/MSS EC, respectively. ORRs by high tumor mutational burden (≥10 mutations/Mb) were 47.8% (43/90) and 45.5% (5/11) for dMMR/MSI-H EC and MMRp/MSS EC, respectively. ORR in TP53mut or POLεmut molecular subgroups was 18.1% (17/94) and 40.0% (2/5), respectively. The safety profile of dostarlimab was consistent with previous reports. CONCLUSIONS: Dostarlimab demonstrated durable antitumor activity and safety in patients with dMMR/MSI-H EC. Biomarkers associated with EC may identify patients likely to respond to dostarlimab. See related commentary by Jangra and Dhani, p. 4521.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".