SPATIAL OMICS AND FOUNDATION AI MODELS: DECODING TUMOR HETEROGENEITY FOR PRECISION ONCOLOGY

Abstract

Tumor heterogeneity remains one of the greatest challenges in precision oncology because malignant tissues exhibit extensive spatial, molecular, cellular, and microenvironmental diversity that continuously evolves during disease progression and therapeutic intervention. Recent advances in spatial omics technologies and foundation artificial intelligence (AI) models have created unprecedented opportunities to decode this complexity by integrating high-dimensional molecular, histopathological, imaging, and clinical information into unified computational frameworks. Foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence enable generalized representation learning across spatial transcriptomics, spatial proteomics, spatial metabolomics, digital pathology, radiological imaging, genomics, transcriptomics, epigenomics, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support comprehensive characterization of tumor heterogeneity, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen spatial oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, spatial resolution, interoperability, clinical validation, and equitable implementation. This review provides a comprehensive overview of spatial omics and foundation AI models, emphasizing their role in decoding tumor heterogeneity for precision oncology and personalized cancer medicine.