Clinical and Genome-Wide Analysis of Multiple Severe Cisplatin-Induced Neurotoxicities in Adult-Onset Cancer Survivors
Bibliographic record
Abstract
Abstract Purpose: Cisplatin is a first-line chemotherapeutic for many cancers, but causes neurotoxicity including hearing loss, tinnitus, and peripheral sensory neuropathy. However, no study has comprehensively characterized risk factors for developing multiple (>1) severe neurotoxicities. Experimental Design: The relationship between multiple severe neurotoxicities and age, cumulative cisplatin dose, medical history, and lifestyle/behavioral factors was evaluated in 300 cisplatin-treated testicular cancer survivors using logistic regression. Case–control genome-wide association study (GWAS; cases, n = 104 and controls, n = 196) was also performed. Results: Age at clinical examination (P = 6.4 × 10−16) and cumulative cisplatin dose (P = 5.4 × 10−4) were positively associated with multiple severe neurotoxicity risk, as were high serum platinum levels (P = 0.02), tobacco use (ever smoker, P = 0.001 and current smoker, P = 0.002), and hypertension (P = 0.01) after adjustment for age and cumulative cisplatin dose. Individuals with multiple severe neurotoxicities were more likely to experience dizziness/vertigo (P = 0.01), Raynaud phenomenon (P = 3.7 × 10−9), and symptoms consistent with peripheral motor neuropathy (P = 4.3 × 10−14) after age and dose adjustment. These patients also reported poorer overall health (P = 2.7 × 10−5) and a greater use of psychotropic medications (P = 0.06). GWAS identified no genome-wide significant SNPs. Gene-based association analysis identified RGS17 (P = 3.9 × 10−5) and FAM20C (P = 5.5 × 10−5) as near genome-wide significant. Decreased FAM20C expression was associated with increased cisplatin sensitivity in tumor cell lines. Conclusions: Certain survivors are more susceptible to cisplatin-induced neurotoxicity, markedly increasing likelihood of developing numerous neuro-otological symptoms that affect quality of life. Genome-wide analysis identified genetic variation in FAM20C as a potentially important risk factor.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".