Graph neural networks for multi-scale gene-drug interaction modeling in cancer: applications, challenges, and therapeutic opportunities for breast cancer drug resistance.

Breast cancer drug resistance remains a major clinical challenge driven by complex genetic, signaling, and microenvironmental interactions. Conventional machine learning and deep learning represent genes, drugs, and patients as independent feature vectors, limiting their ability to capture biological relationships governing therapeutic response. Graph neural networks have emerged as a powerful paradigm by modelling biological systems as interconnected networks rather than isolated entities. This review synthesizes recent advances in graph neural network based multi-scale modelling of gene-drug interactions across cancer research, emphasizing translational relevance to breast cancer drug resistance. Although many architectures originate from pan-cancer or methodological studies, their potential for breast cancer is critically assessed while distinguishing models directly validated from those requiring adaptation. Recent architectures, including hierarchical, heterogeneous, knowledge graph-guided, explainable, and graph-augmented models, demonstrate strong predictive performance across oncology tasks. These studies computationally nominated potential targets such as FAK, FLT3, COX8A, SEC61G, and CYP27B1, though predictions require experimental validation in breast cancer resistance models. The field has leveraged established synthetic lethal relationships, such as BRCA1/PARP, as benchmarks for GNN-based discovery frameworks. Despite encouraging progress, current evidence remains largely retrospective, benchmark-based, or preclinical. Cross-cohort heterogeneity, limited interpretability, and scarce breast cancer-specific resistance validation represent central limitations. Future integration with spatial transcriptomics, multimodal omics, and federated learning may improve precision oncology, but rigorous biological validation and interdisciplinary collaboration are essential for clinical implementation.