Artificial Intelligence–Driven Integration of Metabolic and Genetic Biomarkers in Precision Oncology

Abstract

Conventional pharmacogenomic markers, including polymorphisms in CYP or DPYD genes, often fail to accurately predict drug metabolism within solid tumors. This discrepancy occurs because drug metabolism is an adaptive phenotype influenced by the tumor microenvironment and host factors rather than a static inherited trait. Therefore, a transition from variant-focused models to a dynamic systems pharmacology framework using artificial intelligence (AI) is proposed in this review. This review evaluates AI architectures, including graph convolutional networks (GCNs), transformers, and reinforcement learning, for their ability to synthesize highdimensional data. These models process multi-omic and spatially resolved metabolomic data to track the shifting nature of tumor biology. We emphasize explainable AI (XAI) for causal consistency and federated learning for privacy-preserving, multi-institutional collaboration. AI models can effectively reverse-engineer metabolic states, forecast therapy resistance, and map evolutionary pathways in tumors. By integrating diverse data streams, these systems reveal the regulatory networks governing individual patient responses. The findings emphasize that tumor drug metabolism is governed by dynamic biological interactions rather than static genetic variants alone. AI-based multi-omics integration therefore provides a promising strategy to capture the complex regulatory networks linking tumor metabolism, genetic alterations, and therapeutic response. Such approaches may improve the predictive accuracy of precision oncology models and support more individualized treatment strategies. AI models can effectively reverse-engineer metabolic states, forecast therapy resistance, and map evolutionary pathways in tumors. By integrating diverse data streams, these systems reveal the regulatory networks governing individual patient responses.