Spatial Multi-Omics and Artificial Intelligence in Precision Oncology: Decoding the Tumor Ecosystem

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The biological complexity of cancer extends beyond genomic alterations and is increasingly recognized as a consequence of dynamic spatial interactions among malignant cells, immune populations, stromal components, vascular networks, and the extracellular matrix within the tumor ecosystem. Recent advances in spatial multi-omics technologies and artificial intelligence (AI) have transformed precision oncology by enabling simultaneous characterization of molecular, cellular, and spatial organization across tumor tissues. Spatial transcriptomics, spatial proteomics, spatial metabolomics, spatial epigenomics, multiplex imaging, single-cell sequencing, and computational pathology collectively generate unprecedented multidimensional datasets describing tumor architecture and biological function. Artificial intelligence—including machine learning, deep learning, graph neural networks, transformer architectures, multimodal foundation models, self-supervised learning, and generative AI—facilitates integration of these heterogeneous datasets into comprehensive computational representations that support diagnosis, prognostic prediction, biomarker discovery, therapeutic optimization, immunotherapy response prediction, and personalized medicine. Despite remarkable technological progress, important challenges remain regarding data harmonization, computational scalability, explainability, interoperability, regulatory validation, and equitable implementation. This review provides a comprehensive overview of spatial multi-omics and artificial intelligence in precision oncology, highlighting computational principles, clinical applications, emerging innovations, and future perspectives for decoding the tumor ecosystem.