Exceptional response to combination ipilimumab and nivolumab in metastatic uveal melanoma: Insights from genomic analysis
Bibliographic record
Abstract
Uveal melanoma is the most common intraocular malignancy and has a poor prognosis compared to other melanoma subtypes with a median overall survival of 6-10 months. With immune checkpoint inhibitor therapy, either PD-1 inhibitor alone or combination ipilimumab/nivolumab (anti-CTLA-4/anti-PD-1), responses are rare and often not durable. We present a case report of a now 66-year-old woman with diffuse metastatic uveal melanoma previously treated with a combination of ipilimumab/nivolumab, followed by maintenance nivolumab. Almost complete resolution of all sites of metastatic disease was observed except for one liver metastasis which regressed partially on immunotherapy. Notably, the patient had a significantly elevated BMI and developed widespread vitiligo on treatment. Whole-genome and transcriptome analysis was performed on the residual liver biopsy and molecular markers that may have contributed to the exceptional response were investigated. Several alterations were observed in genes involved in T-cell responses. Estimates of tumour infiltrating immune cells indicated a high level of plasma cells compared to other uveal melanoma cases, a finding previously associated with indolent disease. The patient also carried several germline SNPs that may have contributed to her treatment response as well as widespread vitiligo. Whole-genome and transcriptome sequencing have provided insight into potential molecular underpinnings of an exceptional treatment response in a tumour type typically associated with poor prognosis. Immunological findings suggest a role for plasma cells in the tumour microenvironment. Elevated BMI and the development of vitiligo may be clinically relevant factors for predicting response to immune checkpoint inhibitor therapy, warranting further studies in patients with uveal melanoma.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".