# Pharmacogenomics of Antineoplastic Therapy in Children: Genetic Determinants of Toxicity and Efficacy

## TL;DR

This review explores how genetic differences affect children's responses to cancer drugs, aiming to personalize treatment for better outcomes and fewer side effects.

## Contribution

The paper synthesizes current evidence on pharmacogenetic variants influencing pediatric cancer therapy and discusses emerging technologies for clinical translation.

## Key findings

- Genetic variants like TPMT, NUDT15, and DPYD significantly influence drug response in children.
- Multi-omics and AI can accelerate pharmacogenomic data translation into clinical practice.
- Population-specific variability and ethical issues remain barriers to clinical implementation.

## Abstract

Over the past decades, remarkable progress in multimodal therapy has significantly improved survival outcomes for children with cancer. Yet, considerable variability in treatment response and toxicity persists, often driven by underlying genetic differences that affect the pharmacokinetics and pharmacodynamics of anticancer drugs. Pharmacogenomics, the study of genetic determinants of drug response, offers a powerful approach to personalize pediatric cancer therapy by optimizing efficacy while minimizing adverse effects. This review synthesizes current evidence on key pharmacogenetic variants influencing the response to major classes of antineoplastic agents used in children, including thiopurines, methotrexate, anthracyclines, alkylating agents, vinca alkaloids, and platinum compounds. Established gene–drug associations such as TPMT, NUDT15, DPYD, SLC28A3, and RARG are discussed alongside emerging biomarkers identified through genome-wide and multi-omics studies. The review also examines the major challenges that impede clinical implementation, including infrastructural limitations, cost constraints, population-specific variability, and ethical considerations. Furthermore, it highlights how integrative multi-omics, systems pharmacology, and artificial intelligence may accelerate the translation of pharmacogenomic data into clinical decision-making. The integration of pharmacogenomic testing into pediatric oncology protocols has the potential to transform cancer care by improving drug safety, enhancing treatment precision, and paving the way toward ethically grounded, personalized therapy for children.