Big Data in Cancer Genomics: Computational Foundations and Emerging Pathways for Precision Oncology

Abstract

This review aims to explore the computational foundations of big data in cancer genomics and examine emerging pathways that support precision oncology and personalized cancer care. A narrative review approach was adopted to synthesize evidence from PubMed, Scopus, Web of Science, and IEEE Xplore. The literature search was conducted between January 10 and February 25, 2026, and 68 relevant studies were included in the final synthesis. Relevant studies were selected on the basis of their focus on computational methods, data integration strategies, and artificial intelligence (AI) applications in cancer genomics. Extracted data were organized into thematic categories and analyzed using an iterative synthesis framework. The findings indicate that high‐throughput sequencing and multi‐omics technologies have significantly expanded the volume and complexity of cancer‐related data. Advanced infrastructures, including cloud platforms, improve storage and access but raise privacy and interoperability concerns. Machine learning and AI support tumor classification, biomarker discovery, and treatment prediction. Integrative multi‐omics enhances biological insight and predictive accuracy. However, challenges such as data heterogeneity, limited model generalizability, and gaps in clinical integration remain. Big data in cancer genomics offer substantial potential to advance precision oncology by enabling more accurate and personalized treatment strategies. However, overcoming technical, ethical, and infrastructural barriers is essential to ensure effective translation into clinical practice and equitable healthcare outcomes.