In various cancers, the KRAS pathway is central to governing cellular proliferation, differentiation, and survival. Despite the comparative rarity of KRAS mutations in breast malignancies, aberrant pathway activity significantly influences tumor progression, immune modulation, and clinical resistance. In the present review, we synthesize current knowledge on KRAS signaling in breast cancer, focusing on its prevalence and the molecular drivers behind its activation. Our discussion extends to how dysregulated cascades associated with KRAS, such as PI3K-AKT-mTOR and RAF-MEK-ERK, impact the biological landscape of the tumor beyond mere mutational status.
Furthermore, the review explores the transformative impact of omics technologies and artificial intelligence (AI) in decoding KRAS-driven molecular networks. Recent progress in genomics, transcriptomics, proteomics, and especially multiomics data integration has enabled a more comprehensive understanding of KRAS pathway dynamics. At the same time, machine learning and deep learning approaches have significantly improved tumor classification, biomarker identification, and prediction of therapeutic outcomes. Emerging AIdriven multimodal frameworks that combine molecular profiles, histopathological features, and imaging data show great promise for enhancing prognostic assessment and developing personalized treatment strategies in breast cancer.
Nevertheless, ongoing challenges such as data heterogeneity, limited interpretability of models, insufficient external validation, and ethical considerations remain to be addressed. Future efforts are oriented toward explainable AI, federated learning, and clinically validated predictive systems to establish a robust foundation for AIenabled precision oncology in breast cancer. Ultimately, integrating KRAS pathway biology with advanced AI analytics could accelerate the evolution of individualized diagnostic and therapeutic approaches in breast cancer management.