Genetic Variations and Cisplatin Nephrotoxicity: A Systematic Review
Bibliographic record
Abstract
Background: Nephrotoxicity is a notable adverse effect in cisplatin treated patients characterized by tubular injury and/or increased serum creatinine (SCr) with incidence varying from 20 to 70%. Pharmacogenomics has been shown to identify strongly predictive genetic markers to help determine which patients are more likely to experience, for example, a serious adverse drug reaction or receive optimal benefit through enhanced efficacy. Genetic variations have been reported to influence the risk of cisplatin nephrotoxicity; however, a comprehensive overview is lacking. Methods: A systematic review was performed using Pubmed, Embase and Web of Science on clinical studies that used cisplatin-based chemotherapy as treatment, had available genotyping data, and evaluated nephrotoxicity as an outcome. The quality of reporting was assessed using the STrengthening the REporting of Genetic Association Studies (STREGA) checklist. Results: Twenty-eight eligible studies were included; all were candidate gene studies. Over 300 SNPs across 135 genes were studied; 29 SNPs in 14 genes were significantly associated with cisplatin-induced nephrotoxicity. A variation in SLC22A2 rs316019, a gene involved in platinum uptake by the kidney, was associated with different measures of nephrotoxicity in four independent studies. Further, variants of ERCC1 (rs11615 and rs3212986) and ERCC2 (rs13181), two genes involved in DNA repair, were found to be positively associated with increased risks of nephrotoxicity in two independent studies. Conclusion: Three genes consistently associated with cisplatin-induced nephrotoxicity. Further research is needed to assess the biological mechanism and the clinical value of modifying treatment based on SLCC22A2 and ERCC1/2 genotypes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".