Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: Overall, these findings provide a foundation for understanding the genetic landscape of cisplatin-induced ototoxicity, with implications for improving patient care and treatment outcomes.

Keywords: Adverse drug reaction; Cisplatin; Deep learning; Machine learning; Ototoxicity; Pharmacogenomics.