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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".