SALVO: Single-Arm Trial of Ipilimumab and Nivolumab as Adjuvant Therapy for Resected Mucosal Melanoma
Bibliographic record
Abstract
PURPOSE: Mucosal melanoma is a rare, aggressive form of melanoma with extremely high recurrence rates despite definitive surgical resection with curative intent. Currently there is no consensus on adjuvant therapy. Data on checkpoint inhibitors for adjuvant therapy are lacking. PATIENTS AND METHODS: We performed a single-arm, multicenter clinical trial using "flip dose" ipilimumab (1 mg/kg q3w × 4 cycles), and nivolumab (3 mg/kg q3w × 4 cycles), then nivolumab 480 mg q4w × 11 cycles to complete a year of adjuvant therapy. Participants must have had R0/R1 resection ≤90 days before registration, no prior systemic therapy (adjuvant radiotherapy allowed), ECOG 0/1, and no uncontrolled autoimmune disease or other invasive cancer. Patients were recruited through the Midwest Melanoma Partnership/Hoosier Oncology Network. RESULTS: From September 2017 to August 2021, 35 patients were enrolled. Of these, 29 (83%) had R0 resections, and 7 (20%) received adjuvant radiotherapy. Median age was 67 years, 21 (60.0%) female. Recurrence-free survival (RFS) rates at 1 and 2 years were 50% [95% confidence interval (CI), 31%-66%] and 37% (95% CI, 19%-55%), respectively. Overall survival rates at 1 and 2 years were 87% (95% CI, 68%-95%) and 68% (95% CI, 46%-83%), respectively. Median RFS was 10.3 months (95% CI, 5.7-25.8). Most common grade 3 toxicities were diarrhea (14%), hypertension (14%), and hyponatremia (11%), with no grade 4/5 toxicities. CONCLUSIONS: Flip-dose ipilimumab and nivolumab after resection of mucosal melanoma is associated with outcomes improved over that of surgical resection alone. Long-term follow-up, subgroup analyses and correlative studies are ongoing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".