Immune Checkpoint Inhibitors in Merkel Cell Carcinoma of the Skin: A 2025 Comprehensive Review
Bibliographic record
Abstract
Objective: Merkel cell carcinoma (MCC) is a rare and aggressive form of skin cancer. Although immunotherapy has transformed MCC management, published data remain limited. This comprehensive review evaluates current evidence on immune checkpoint inhibitors (ICIs) in MCC, in relation to other treatment modalities such as surgery and radiotherapy. Methods: Peer-reviewed articles published between January 2000 and August 2025 were searched manually in four databases: Scopus, ScienceDirect, PubMed and MEDLINE, using the keywords “Merkel cell carcinoma” AND “immunotherapy” AND “immune checkpoint inhibitors”. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology was employed. Results: ICIs can be given in different settings: (A) Neoadjuvant: The CheckMate 358 trial reported a 54.5% response rate among 33 radiologically evaluable patients treated with nivolumab, each showing over 30% tumor reduction. (B) Adjuvant: (1) The ADMEC-O phase II trial demonstrated improved disease-free survival with adjuvant nivolumab. (2) The ADAM phase III trial evaluates adjuvant avelumab in node-positive patients post-surgery/radiation, with common side effects including nausea, fatigue, and itching. (3) STAMP, a phase III trial, investigates pembrolizumab in stage I–III MCC. Both ADAM and STAMP have completed accrual and results are pending. (C) Primary therapy: KEYNOTE-017 and JAVELIN trials reported a 60% overall response rate and ~40% 3-year progression-free survival with first-line pembrolizumab or avelumab. Both agents also show promise as salvage therapies. Conclusions: ICIs demonstrate encouraging outcomes in MCC across various treatment stages. Continued research is essential to optimize treatment timing and integrate multimodal therapies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".