Immune-Mediated Hepatitis During Immune Checkpoint Inhibitor cancer Immunotherapy: Lessons From Autoimmune Hepatitis and Liver Immunology
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICI) are being increasingly used to successfully treat several types of cancer. However, due to their mode of action, these treatments are associated with several immune-related adverse events (irAEs), including immune-mediated autoimmune-like hepatitis in 5 to 10% of cases. The specific immune mechanism responsible for the development of immune-mediated liver injury caused by immune checkpoint inhibitors (ILICI) is currently unknown. This review summarizes the current knowledge on hepatic irAEs during cancer immunotherapy. It also addresses the clinical management of ILICI and how it is becoming an increasingly important clinical issue. Clinical, histological, and laboratory features of autoimmune hepatitis (AIH) and ILICI are compared, and their shared and distinctive traits are discussed in an effort to better understand the development of hepatic irAEs. Finally, based on the current knowledge of liver immunology and AIH pathogenesis, we propose a series of events that could trigger the observed liver injury in ICI-treated patients. This model could be useful in the design of future studies aiming to identify the specific immune mechanism(s) at play in ILICI and improve immune checkpoint inhibitor cancer immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".