Concurrent local therapy extends clinical benefit of tebentafusp in metastatic uveal melanoma patients
Bibliographic record
Abstract
BACKGROUND: Tebentafusp has significantly improved overall survival in HLA-A*02:01+ metastatic uveal melanoma (mUM) patients even in those with a best objective response of progressive disease. Thus, strategies to maintain tebentafusp therapy are critical. Here, we examine the efficacy and safety of adding concurrent local therapy (CLT) to tebentafusp upon radiological progression with tebentafusp alone. PATIENTS AND METHODS: This multicenter retrospective study included mUM patients treated with tebentafusp and CLT, consisting of extrahepatic soft tissue irradiation and liver-directed therapies (LDTs). Efficacy of target and nontarget sites were assessed per RECIST version 1.1. PFS with tebentafusp alone (PFS1) was compared to that after adding CLTs to tebentafusp upon progression (PFS1+PFS2). ctDNA responses were explored. RESULTS: Of the 30 eligible patients, 21 (70%) received concurrent LDT, 7 (23%) had extrahepatic irradiation, and 2 (7%) had both. The objective response rate (ORR) was 12% (95% CI, 3-32) for tebentafusp alone and 28% (95% CI, 14-47) after adding CLTs. The disease-control rate with tebentafusp alone was 44% (95% CI, 25-65) vs 63% (95% CI, 44-78) after CLT. Median PFS1 was 5.8 months (95% CI, 2.8-13.4), while median PFS1+PFS2 was 14.8 months (95% CI, 9.2-NA). CLT thereby allowed treatment beyond progression with tebentafusp for approximately 9 months. Two patients (66%) had decreased ctDNA with tebentafusp alone, while 4 (100%) had decreased ctDNA after CLT. There were no treatment discontinuations due to toxicities from tebentafusp with CLT. CONCLUSIONS: CLT with tebentafusp was well-tolerated, extending the duration of tebentafusp benefit in a highly selected mUM population. This merits further studies to assess clinical utility.
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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 | 0.001 |
| 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".