PHARMACOGENOMIC VARIATIONS AND DRUG RESPONSE: A MOLECULAR STUDY IN PERSONALISED MEDICINE

Abstract

Variability in anticancer drug response presents a significant challenge in oncology, necessitating the integration of pharmacogenomics to enable personalised treatment strategies. This study aimed to investigate the molecular determinants of drug response using a secondary pharmacogenomic analysis of the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. A total of 232,669 drug–cell line observations were analysed, incorporating multi-omics features including gene expression, copy number alterations (CNA), and DNA methylation. Linear regression models were applied at both global and drug-specific levels to assess associations with drug response, measured by log-transformed IC50 (LN_IC50). The global model demonstrated statistically significant associations (p < 0.001) but limited explanatory power (R² = 0.001). In contrast, drug-wise analyses improved model performance (R² up to 0.036) and identified 95 drugs with significant pharmacogenomic associations. Gene expression emerged as the most consistent predictor, predominantly associated with increased drug resistance, while CNA and methylation exhibited context-dependent effects. These findings highlight the importance of multi-omics integration and drug-specific modeling in understanding pharmacogenomic variability. The study underscores the limitations of aggregated analyses and supports precision oncology approaches for biomarker-driven therapy. Overall, this work contributes to advancing personalised medicine by identifying molecular features that influence anticancer drug response.