Molecular Mechanisms of Cutaneous Immune-Related Adverse Events (irAEs) Induced by Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Over the past few decades, immune checkpoint inhibitors (ICIs) have emerged as promising therapeutic options for the treatment of various cancers. These novel treatments effectively target key mediators of immune checkpoint pathways. Currently, ICIs primarily consist of monoclonal antibodies that specifically block cytotoxic T-lymphocyte antigen 4 (CTLA-4), programmed cell death 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and lymphocyte activation gene 3 protein (LAG-3). Despite the notable efficacy of ICIs in cancer treatment, they can also trigger immune-related adverse events (irAEs), which present as autoimmune-like or inflammatory conditions. IrAEs have the potential to affect multiple organ systems, with cutaneous toxicities being the most commonly observed. Although cutaneous irAEs are typically of low-grade severity and can usually be managed effectively, there are cases where severe irAEs can become life-threatening. Therefore, early recognition and a comprehensive understanding of the mechanisms underlying cutaneous irAEs are crucial for improving clinical outcomes in cancer patients. However, the precise pathogenesis of cutaneous irAEs remains unclear. This review focuses on the skin manifestations induced by ICIs, the prognosis related to cutaneous irAEs, and the exploration of potential mechanisms involved in cutaneous irAEs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".