The evolving bioinformatic approaches to identifying genetic targets of promise in cancers

The search for actionable genetic targets in cancer has evolved substantially over the past decade. Earlier approaches were focused on single genes or individual molecular alterations, but this is insufficient to capture tumor complexity in vivo. Cancer is influenced not only by genomic changes but also by transcriptional plasticity, epigenetic regulation, protein activity, metabolic adaptation, and dynamic interactions with the tumor microenvironment. Consequently, bioinformatic target discovery has shifted toward integrative, systems-level models of tumor biology.

This article discusses the evolution of bioinformatic approaches for cancer target identification, underscoring key achievements and persistent challenges. Advances from 2018-2025 are analyzed, including multi-omics integration, single-cell sequencing, and functional genomics, which enhance the identification of context-dependent molecular vulnerabilities. Also, the role of machine learning in analyzing large-scale datasets to uncover potential therapeutic targets is discussed.

Precision medicine now recognizes that genetic background alone is insufficient to define actionable targets. Factors such as cell type, tumor spatial context, environmental influences, clonal lineage and epigenetic state are vital. Current bioinformatic frameworks increasingly incorporate artificial intelligence, offer unprecedented opportunities to integrate these dimensions and refine target discovery.