Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4
Bibliographic record
Abstract
Cisplatin-induced ototoxicity remains a significant concern in pediatric cancer treatment due to its permanent impact on quality of life. Previously, genetic association analyses have been performed to detect genetic variants associated with this adverse reaction. In this study, a combination of interpretable neural networks and Generative Adversarial Networks (GANs) was employed to identify genetic markers associated with cisplatin-induced ototoxicity. The applied method, BRI-Net, incorporates biological domain knowledge to define the network structure and employs adversarial training to learn an unbiased representation of the data, which is robust to known confounders. Leveraging genomic data from a cohort of 362 cisplatin-treated pediatric cancer patients recruited by the CPNDS (Canadian Pharmacogenomics Network for Drug Safety), this model revealed two statistically significant single nucleotide polymorphisms to be associated with cisplatin-induced ototoxicity. Two markers within the CERS6 (rs13022792, p-value: 3 × 10 −4 ) and TLR4 (rs10759932, p-value: 7 × 10 −4 ) genes were associated with this cisplatin-induced adverse reaction. CERS6, a ceramide synthase, contributes to elevated ceramide levels, a known initiator of apoptotic signals in mouse models of inner ear hair cells. TLR4, a pattern-recognition protein, initiates inflammation in response to cisplatin, and reduced TLR4 expression has been shown in murine hair cells to confer protection from ototoxicity. Overall, these findings provide a foundation for understanding the genetic landscape of cisplatin-induced ototoxicity, with implications for improving patient care and treatment outcomes. • BRI-Net, a neural network model, computes feature importance and causality in genomic studies. • In children treated with cisplatin, BRI-Net revealed two significant biomarkers of ototoxicity. • CERS6 rs13022792 increases the risk of cisplatin hearing loss; TLR4 rs10759932 is protective. • CER6 rs13022792 is strongly associated with ceramide elevation, triggering apoptosis in the ear. • TLR4 rs10759932 is strongly associated with initiating inflammation in response to cisplatin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".