Treatment sequence with tebentafusp and immune checkpoint inhibitors in patients with metastatic uveal melanoma and metastatic GNA11/GNAQ mutant melanocytic tumors
Bibliographic record
Abstract
BACKGROUND: Metastatic uveal melanoma (mUM) is rare. Immune checkpoint inhibitors (ICIs) have shown modest efficacy in mUM. Tebentafusp prolonged overall survival (OS) in a phase 3 study. We aimed to investigate the efficacy and safety of the sequence of tebentafusp and ICIs. METHODS: Patients with HLA-A * 02:01 positive mUM, or metastatic GNA11/GNAQ mutant melanocytic tumors treated with tebentafusp followed by ICIs (group 1) or the inverse sequence (group 2) at any treatment line were retrospectively identified. The primary objective was OS rate at 2 years. RESULTS: 131 patients were included; 51 in group 1 and 80 in group 2. 30 % in group 1 % and 40 % in group 2 had normal baseline lactate dehydrogenase (LDH, p = 0.05). 94 % in group 1 % and 77 % in group 2 had multilobular liver disease (p = 0.02). Median OS was 22.4 months (95 % CI 19-24.8) in group 1 and 33.6 months (95 % CI 28.9-43) in group 2 (p = 0.004). Total median PFS was 12 months (95 % CI 10.7-18.8) in group 1 and 20.3 months (95 % CI 17.2-27.3) in group 2 (p = 0.04). The frequency of cytokine release syndrome was higher in group 2 (15 % vs 27 %). Other clinical factors were associated with short total PFS in the multivariable analysis. CONCLUSIONS: Both treatment sequences are clinically feasible. A clinical benefit was noted in the sequential combination of ICIs followed by tebentafusp. This observation is limited by the retrospective nature of the study and merits further investigation in prospective clinical trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".