Targeting the P53–Mdm2 Axis In Breast Cancer: Integrating Artificial Intelligence, Multi-Omics, and Natural Compounds to Overcome Therapeutic Resistance
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Abstract
Breast cancer management is complicated by therapeutic resistance and molecular heterogeneity, including abnormalities in the p53–MDM2 pathway. Artificial intelligence [AI], multi-omics profiling, and bioactive natural compounds offer complementary approaches for clarifying resistance mechanisms and advancing individualized treatment. This systematic review examined the involvement of the p53-MDM2 axis in breast cancer progression and therapeutic resistance and explored integrating AI, multi-omics, and natural compounds for response prediction and therapeutic discovery. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched from inception to March 2026 using terms related to breast cancer, p53/TP53, MDM2, treatment resistance, AI, machine learning, multi-omics, and natural compounds. Of 1,550 records, 1,530 remained after duplicate removal. Screening resulted in full-text assessment of 130 articles, with 67 studies included in the qualitative synthesis. Observational, experimental, computational, and modeling studies were eligible. The methodological robustness of the included studies was evaluated with the Newcastle–Ottawa Scale (NOS). Disruption of the regulatory interaction between p53 and MDM2 can contribute to cancer progression and treatment resistance by limiting programmed cell death and enabling malignant cells to persist and proliferate. AI and machine learning support treatment-response prediction and patient stratification, whereas multi-omics identifies molecular features linked to resistance. Natural compounds show preclinical potential to modulate p53–MDM2 signaling and restore p53 activity. However, clinical translation is limited by validation requirements, tumor heterogeneity, TP53 status, toxicity, poor bioavailability, and limited clinical evidence. Integrating AI, multi-omics, and natural-product discovery may strengthen precision strategies against p53–MDM2-associated therapeutic resistance. Further prospective research and clinical trials are required to establish clinical utility.
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