Spatial Multi-omics in Cancer: Integrating Transcriptomics, Proteomics, and Metabolomics for Precision Oncology

Patient-derived tumor organoids (PDOs) have become one of the most valuable experimental models in cancer research because they retain many of the histological, genetic, and phenotypic features of the original tumors. Compared with conventional two-dimensional cell cultures and many animal models, PDOs more accurately reflect human tumor biology, making them powerful tools for studying cancer progression, therapeutic response, and precision medicine. Nevertheless, organoid models alone cannot fully capture the complexity of the tumor microenvironment or the dynamic interactions among different cellular populations. Recent advances in single-cell and spatial multi-omics have greatly expanded the potential of organoid-based research. Single-cell technologies reveal cellular heterogeneity, lineage dynamics, and molecular regulatory mechanisms with unprecedented resolution, while spatial multi-omics preserves tissue architecture and uncovers how cells interact within their native environment. Combining these technologies with PDOs enables comprehensive molecular characterization of tumors while maintaining their structural organization, providing new insights into tumor evolution, immune–tumor interactions, drug resistance, and disease progression. At the same time, artificial intelligence (AI) and advanced computational methods have become essential for analyzing the large and complex datasets generated by these integrated platforms. Machine learning approaches can combine genomic, transcriptomic, proteomic, metabolomic, and spatial information to identify clinically relevant biomarkers, predict therapeutic responses, optimize drug screening, and support personalized treatment strategies. In this review, we discuss recent advances in patient-derived tumor organoid technology and examine its integration with single-cell and spatial multi-omics. We summarize current applications in cancer modeling, biomarker discovery, drug screening, immuno-oncology, and precision oncology, and highlight the growing role of artificial intelligence and computational analysis in interpreting multimodal datasets. Finally, we discuss the major biological, technical, computational, and clinical challenges that remain, together with future directions, including organoid-on-a-chip systems, immune-competent and vascularized organoids, digital twin technologies, and next-generation multimodal platforms. Overall, the convergence of patient-derived organoids, multi-omics technologies, and artificial intelligence is reshaping translational cancer research and is expected to play a central role in the future of personalized cancer medicine.

کلیدواژه ها:

Patient-derived tumor organoids (PDOs) ، Precision oncology ، Single-cell ، multi-omics ، Spatial multi-omics ، Spatial transcriptomics ، Artificial intelligence ، Drug discovery ، Personalized medicine