Evaluation of liver enzyme elevations and hepatotoxicity in patients treated with checkpoint inhibitor immunotherapy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Immune checkpoint inhibitors (ICI) are increasingly used in cancer therapy. Elevated liver enzymes frequently occur in patients treated with ICI but evaluation is poorly described. We sought to better understand causes of liver enzyme elevation, investigation and management. METHODS: Patients treated with anti-PD-1, PDL-1 or CTLA-4 therapy in Phase I/II clinical trials between August 2012 and December 2018 were included. Clinical records of patients with significant liver enzyme elevations were retrospectively reviewed. RESULTS: Of 470 ICI-treated patients, liver enzyme elevation occurred in 102 (21.6%), attributed to disease progression (56; 54.9%), other drugs/toxins (7; 6.9%), other causes (22; 21.6%) and ICI immunotoxicity (17; 16.7%; 3.6% of total cohort). Immunotoxicity was associated with higher peak ALT than other causes of enzyme elevation (N = 17; M = 217, 95% CI 145-324 for immunotoxicity, N = 103; M = 74, 95% CI 59-92 for other causes; ratio of means 0.34, 95% CI 0.19-0.60, p = <0.001) and higher ALT:AST ratio (M = 1.27, 95% CI 0.78-2.06 for immunotoxicity, M = 0.69, 95% CI 0.59-0.80 for other causes, ratio of means 0.54, 95% CI 0.36-0.82, p = 0.004). Immunotoxicity was more often seen in patients with prior CPI exposure (41.2% of immunotoxicity vs 15.9% of patients without, p = 0.01), anti-CTLA-4 -containing ICI treatments (29.4% of immunotoxicity vs 6.8% of patients without, p = <0.001) and other organ immunotoxicity (76.5% of immunotoxicity vs 19.2% of patients without, p = <0.001). Cause for enzyme elevation was established in most patients after non-invasive investigation. Liver biopsy was reserved for four patients with atypical treatment response. CONCLUSIONS: Liver enzyme elevation is common in patients receiving ICI, but often has a cause other than immunotoxicity. A biochemical signature with higher ALT and ALT/AST ratio, a history of prior ICI exposure and other organ immunotoxicities may help to identify patients at a higher likelihood of immunotoxicity. Liver biopsy can be safely deferred in most patients. We propose an approach to diagnostic evaluation in patients with liver enzyme elevations following ICI exposure.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".