Systematic Critical Review of Genetic Factors Associated with Cisplatin-induced Ototoxicity: Canadian Pharmacogenomics Network for Drug Safety 2022 Update
Bibliographic record
Abstract
BACKGROUND: Cisplatin is commonly used to treat solid tumors; however, its use can be complicated by drug-induced hearing loss (ie, ototoxicity). The presence of certain genetic variants has been associated with the development/occurrence of cisplatin-induced ototoxicity, suggesting that genetic factors may be able to predict patients who are more likely to develop ototoxicity. The authors aimed to review genetic associations with cisplatin-induced ototoxicity and discuss their clinical relevance. METHODS: An updated systematic review was conducted on behalf of the Canadian Pharmacogenomics Network for Drug Safety, based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 statement. Pharmacogenomic studies that reported associations between genetic variation and cisplatin-induced ototoxicity were included. The evidence on genetic associations was summarized and evaluated, and knowledge gaps that can be used to inform future pharmacogenomic studies identified. RESULTS: Overall, 40 evaluated reports, considering 47 independent patient populations, captured associations involving 24 genes. Considering GRADE criteria, genetic variants in 2 genes were strongly (ie, odds ratios ≥3) and consistently (ie, replication in ≥3 independent populations) predictive of cisplatin-induced ototoxicity. Specifically, an ACYP2 variant has been associated with ototoxicity in both children and adults, whereas TPMT variants are relevant in children. Encouraging evidence for associations involving several other genes also exists; however, further research is necessary to determine potential clinical relevance. CONCLUSIONS: Genetic variation in ACYP2 and TPMT may be helpful in predicting patients at the highest risk of developing cisplatin-induced ototoxicity. Further research (including replication studies considering diverse pediatric and adult patient populations) is required to determine whether genetic variation in additional genes may help further identify patients most at risk.
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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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".