Multi-Omics-Driven Insights into Cancer Biology and Therapeutic Targeting
The increasing biological complexity and heterogeneity of cancer have driven a shift in oncology drug discovery from single-target approaches toward system-level strategies capable of capturing multilayered disease regulation. Multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics, have emerged as powerful tools for elucidating cancer-driving mechanisms, identifying therapeutic targets, and enabling biomarker-guided drug development. This review examines how integrative multi-omics approaches support cancer drug discovery and therapeutic targeting, focusing on target identification, pathway elucidation, target validation, biomarker discovery, and therapeutic development. Genomics and transcriptomics facilitate the identification of driver alterations and dysregulated signaling pathways, whereas proteomics and metabolomics provide functional insights into protein activity, metabolic reprogramming, and treatment response. We further highlight the contributions of epigenomic and microbiomic profiling to biomarker discovery, therapeutic response prediction, and precision oncology. Given the complexity of multi-omics datasets, the review also explores the application of artificial intelligence (AI) and machine-learning methodologies for data integration, network modeling, biomarker discovery, and drug repurposing, including deep learning, Bayesian frameworks, graph-based models, and explainable AI approaches. Emerging computational frameworks and integration strategies that enable interpretation of heterogeneous molecular datasets and support therapeutic discovery are also discussed. Cancer-focused examples demonstrate how integrative multi-omics frameworks have enabled the identification of clinically relevant biomarkers, therapeutic targets, and rational combination therapies. Furthermore, the clinical translation of biomarker-driven precision oncology, exemplified by HER2-, EGFR-, and MSI-directed therapies, highlights the growing impact of omics-informed approaches on personalized cancer treatment. Overall, AI-enabled multi-omics approaches hold substantial promise for accelerating cancer drug discovery and precision oncology. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing.