Pharmacogenomics of <scp>cisplatin‐induced</scp> neurotoxicities: Hearing loss, tinnitus, and peripheral sensory neuropathy
Bibliographic record
Abstract
Abstract Purpose Cisplatin is a critical component of first‐line chemotherapy for several cancers, but causes peripheral sensory neuropathy, hearing loss, and tinnitus. We aimed to identify comorbidities for cisplatin‐induced neurotoxicities among large numbers of similarly treated patients without the confounding effect of cranial radiotherapy. Methods Utilizing linear and logistic regression analyses on 1680 well‐characterized cisplatin‐treated testicular cancer survivors, we analyzed associations of hearing loss, tinnitus, and peripheral neuropathy with nongenetic comorbidities. Genome‐wide association studies and gene‐based analyses were performed on each phenotype. Results Hearing loss, tinnitus, and peripheral neuropathy, accounting for age and cisplatin dose, were interdependent. Survivors with these neurotoxicities experienced more hypertension and poorer self‐reported health. In addition, hearing loss was positively associated with BMIs at clinical evaluation and nonwork‐related noise exposure (>5 h/week). Tinnitus was positively associated with tobacco use, hypercholesterolemia, and noise exposure. We observed positive associations between peripheral neuropathy and persistent vertigo, tobacco use, and excess alcohol consumption. Hearing loss and TXNRD1, which plays a key role in redox regulation, showed borderline significance ( p = 4.2 × 10 −6 ) in gene‐based analysis. rs62283056 in WFS1 previously found to be significantly associated with hearing loss ( n = 511), was marginally significant in an independent replication cohort ( p = 0.06; n = 606). Gene‐based analyses identified significant associations between tinnitus and WNT8A ( p = 2.5 × 10 −6 ) , encoding a signaling protein important in germ cell tumors. Conclusions Genetics variants in TXNRD1 and WNT8A are notable risk factors for hearing loss and tinnitus, respectively. Future studies should investigate these genes and if replicated, identify their potential impact on preventive strategies.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".