AI-driven CRISPR strategies in breast cancer: Organoid modeling, adaptive editing, and precision delivery

Abstract

Triple-negative breast cancer (TNBC) is defined by profound heterogeneity, dormant metastatic reservoirs, and rapid therapy resistance. Building on our AI-Driven CRISPR Strategies in Breast Cancer framework, CRISPR–Cas9 is emerging as more than a gene-editing tool, capable of restoring circadian integrity, eliminating dormant clones, and re-programming immune surveillance. A structured PubMed, Scopus, and ClinicalTrials.gov review through 2025 integrated mechanistic, preclinical, and early clinical evidence. Beyond standard knockout, base, and prime editing, we highlight chrono-genomic repair of BMAL1/PER2, dormancy-focused synthetic-lethality screens, and genomic-collapse tactics for BRCA1-deficient tumors. Adaptive AI pipelines that iteratively refine guide RNAs and exosome-mimetic carriers, incorporating Boolean logic gates, were also evaluated for self-regulated, tumor-specific delivery. Proof-of-concept studies show that HER2 deletion, TP53 rescue, and ABCB1 silencing enhance chemosensitivity across luminal, HER2-positive, and TNBC models. Circadian restoration expands therapeutic windows and delays relapse in xenografts. Dormancy-directed CRISPR screens reveal unique vulnerabilities in disseminated tumor cells, whereas genomic collapse selectively destroys BRCA1-mutant clones. Integration with CAR-T cells and antibody–drug conjugates amplifies cytotoxicity, and transient nanoparticle or exosome systems improve solid-tumor penetration while minimizing off-target events. CRISPR–Cas9 is transitioning from a molecular scalpel to an adaptive, self-learning therapeutic ecosystem. By uniting AI-guided design, circadian reprogramming, dormancy eradication, and logic-gated delivery, the strategies detailed here define a next-generation precision-oncology paradigm capable of anticipating tumor evolution, overcoming resistance, and preventing metastatic relapse.