Systemic therapies such as Lenvatinib and radioiodine in thyroid cancer (TC) show promising clinical efficacy, but often cause significant toxicity. Interindividual variability in response to this toxicity is largely influenced by genetic factors, requiring a pharmacogenomic approach to accurately identify predictive biomarkers. This study aims to integrate pharmacogenomic and bioinformatics strategies in identifying genetic variants associated with the risk of TC therapy toxicity. The research method involved a systematic search of the PharmGKB database using the keyword “Thyroid Cancer.” The selection results were filtered based on clinical pharmacogenomic relevance and ESMO therapy guidelines. Only gene-drug pairs with Level of Evidence (LOE) 1A to 3 were included. Furthermore, genetic variant data were analyzed bioinformatically based on tissue expression profiles and biological relevance. The results of the study of three main systemic therapies (lenvatinib, sorafenib, and radioiodine) identified two biomarkers with LOE level 3: the rs776746 (CYP3A5) variant associated with lenvatinib toxicity, and rs620815 (ATM) associated with gastrointestinal toxicity due to radioiodine. Tissue expression analysis showed CYP3A5 to be dominant in the liver and intestines, supporting its role in drug metabolism, while ATM was widely expressed and involved in DNA repair. Both genes have potential as predictive biomarkers of TC therapy toxicity. This study demonstrates that the integration of pharmacogenomic and bioinformatics approaches can identify potential genetic biomarkers contributing to the risk of TC therapy toxicity. These findings strengthen the basis for the application of genetic-based precision medicine in optimizing the safety and individualization of TC therapy.
Rahmawati et al. (Tue,) studied this question.