Checkpoint inhibitor hepatotoxicity: pathogenesis and management
Bibliographic record
Abstract
Immunotherapy, including immune checkpoint inhibitor (ICI) therapy, has been a paradigm shift in cancer therapeutics, producing durable cancer responses across a range of primary malignancies. ICI drugs increase immune activity against tumor cells, but may also reduce immune tolerance to self-antigens, resulting in immune-mediated tissue damage. ICI-associated hepatotoxicity usually manifests as hepatocellular enzyme elevation and may occur in 2%-25% of ICI-treated patients. Although ICI-associated hepatotoxicity is clinically and pathologically distinct from idiopathic autoimmune hepatitis, our understanding of its pathogenesis continues to evolve. Pending greater understanding of the pathophysiology, mainstay of management remains through treatment with high-dose corticosteroids. This approach works for many patients, but up to 30% of patients with high-grade hepatotoxicity may not respond to corticosteroids alone. Furthermore, atypical cholestatic presentations are increasingly recognized, and rare cases of fulminant hepatitis due to ICI hepatotoxicity have been reported. Optimal management for these challenging patients remains uncertain. Herein, we review the current understanding of pathogenesis of ICI-associated toxicities, with a focus on hepatotoxicity. Based on the existing literature, we propose evolving management approaches to incorporate strategies to limit excess corticosteroid exposure, and address rare but important presentations of cholestatic hepatitis and fulminant liver failure. Finally, as ICI hepatotoxicity frequently occurs in the context of treatment for advanced malignancy, we review the impact of hepatotoxicity and its treatment on cancer outcomes, and the overall safety of re-challenge with ICI, for patients who may have limited treatment options.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".