Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches

Over the past two decades, comparative genomic hybridization (CGH) and array CGH have become essential tools in clinical diagnostics, oncology, and toxicological risk assessment. Initially developed to identify chromosomal imbalances like copy number variations (CNVs) in tumor cells, these technologies have expanded into genotoxicology and toxicogenomics, exploring gene responses to toxic agents and their molecular mechanisms. As of 2024, new developments include integrating array CGH with next-generation sequencing (NGS), machine learning, and CRISPR-Cas9 genome editing, greatly improving precision. High-density CGH arrays now offer single-cell resolution, enabling the detection of cellular heterogeneity in toxic responses, while long-read sequencing facilitates the identification of complex genomic rearrangements. Recent innovations include combining CGH and toxicogenomics with organ-on-chip models for real-time, tissue-specific toxicological assessment. This has significantly improved the relevance of toxicological data for human health. However, while these advances are promising, array CGH remains costly and requires substantial data processing, driving the need for advanced bioinformatics tools. AI-driven predictive toxicology models are also gaining traction, correlating toxicogenomic profiles with clinical outcomes. Despite these advancements, the field still faces challenges, such as evolving regulatory guidelines and complex data interpretation, which hinder broader adoption and the full realization of CGH's potential in toxicology and risk assessment.