Recent advances in precision oncology have led to significant breakthroughs through the targeting of defined oncogenic drivers. However, the clinical efficacy of single-target therapies is increasingly constrained by the intrinsic complexity and adaptability of cancer. Solid tumors frequently arise from multifactorial oncogenic processes and adapt via diverse resistance mechanisms, ultimately limiting the durability of monotherapies. This review advocates for a paradigm shift toward multi-targeted, AI-enhanced strategies that harness high-throughput multi-omic data to inform the rational design of combination therapies. By leveraging artificial intelligence for drug discovery and repurposing, response prediction, and clinical trial optimization, the field of oncology is poised to transcend reductionist approaches and more fully address the biological intricacy of cancer.
Mauro et al. (2025) studied this question.
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