Tumour SNPs Associated with Immune-Related Hepatitis in Patients with Melanoma Receiving Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Background and Aims: Immune checkpoint inhibitors (ICIs) have significantly improved survival rates for patients with metastatic melanoma. However, these treatments can lead to immune-related adverse events (irAEs), including hepatitis. This exploratory study sought to identify tumour single-nucleotide polymorphisms (SNPs) associated with the risk of ICI-induced hepatitis in melanoma patients. Methods: The study cohort comprised 69 patients with malignant melanoma treated with ICIs at several hospitals in Madrid, Spain. DNA was extracted from formalin-fixed paraffin-embedded tumour biopsies and SNP genotyping was conducted using a MassARRAY platform. Results: Significant associations were found between hepatitis risk and 4 of the 20 SNPs examined. A possible risk effect was shown for the variant GABRP SNP alleles rs11743438 and rs11743735. Among patients homozygous for these variant alleles (v/v), significantly higher proportions developed hepatitis, 75% and 71.4%, respectively, compared to 32.8% and 21.4, respectively, not developing hepatitis (p = 0.046; p = 0.013). However, in the same genotype group comparisons, the RGMA SNP rs4778080 seemed to have a protective effect, as 100% of patients who developed hepatitis were not in the v/v group for this allele (p = 0.043). Additionally, in genotype group comparisons wt/wt versus wt/v + v/v, the PACRG SNP rs55733913 was also associated with a higher risk of ICI-induced hepatitis: 66.7% of patients with hepatitis versus 22.8% without hepatitis in genotype group wt/v + v/v (p = 0.041). Conclusions: This exploratory study identifies candidate tumour SNPs as possible biomarkers to predict the risk of ICI-induced hepatitis, warranting their validation in larger patient cohorts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".