Immune checkpoint inhibitor therapy in a liver transplant recipient with a rare subtype of melanoma: a case report and literature review
Bibliographic record
Abstract
Immunotherapy with immune checkpoint inhibitors (ICIs) may be considered as a treatment option for various types of tumors, but the transplant recipient population as well as patients requiring long-term systemic immunosuppression for other reasons have been systematically excluded from clinical trials involving ICIs. We report a case of successful treatment with ICI in a liver transplant recipient diagnosed with a rare subtype of melanoma. This patient had not required any modification to her antirejection immunosuppression before or during immunotherapy, had not experienced any serious immune-related adverse event, and had a durable objective response for nearly 1.5 year now. A summary of a literature review on other case reports is included to show that ICIs can be safe and provide clinically meaningful benefit in transplant patients, although acute rejection and graft loss remain a significant risk. Given the serious complication of graft failure, a detailed discussion of risks and benefits with immunotherapy needs to be made for an informed consent. Nevertheless, transplant recipients with cancer should not be deprived of this potentially life-saving or life-prolonging treatment, and inclusion of this population in future clinical trials should be considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".