TPMT, COMT and ACYP2 genetic variants in paediatric cancer patients with cisplatin-induced ototoxicity
Bibliographic record
Abstract
OBJECTIVES: Cisplatin ototoxicity affects 42-88% of treated children. Catechol-O-methyltransferase (COMT), thiopurine methyltransferase (TPMT) and AYCP2 genetic variants have been associated with ototoxicity, but the findings have been contradictory. The aims of the study were as follows: (a) to investigate these associations in a carefully phenotyped cohort of UK children and (b) to perform a systematic review and meta-analysis. METHODS: We recruited 149 children from seven UK centres using a retrospective cohort study design. All participants were clinically phenotyped carefully. Genotyping was performed for one ACYP2 (rs1872328), three TPMT (rs12201199, rs1142345 and rs1800460) and two COMT (rs4646316 and rs9332377) variants. RESULTS: For CTCAE grading, hearing loss was present in 91/120 (75.8%; worst ear) and 79/120 (65.8%; better ear). Using Chang grading, hearing loss was diagnosed in 85/119 (71.4%; worst ear) versus 75/119 (63.0%; better ear). No TPMT or COMT single-nucleotide polymorphisms (SNPs) were associated with ototoxicity. ACYP2 SNP rs1872328 was associated with ototoxicity (P=0.027; worst ear). Meta-analysis of our data with that reported in previous studies showed the pooled odds ratio (OR) to be statistically significant for both the COMT SNP rs4646316 (OR: 1.50; 95% confidence interval: 1.15-1.95) and the ACYP2 SNP rs1872328 (OR: 5.91; 95% confidence interval: 1.51-23.16). CONCLUSION: We showed an association between the ACYP2 polymorphism and cisplatin-induced ototoxicity, but not with the TPMT and COMT. A meta-analysis was statistically significant for both the COMT rs4646316 and the ACYP2 rs1872328 SNPs. Grading the hearing of children with asymmetric hearing loss requires additional clarification.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".