Integrating single-cell and spatial multi-omics for precision oncology: from tumor ecosystems to clinical decision-making
TL;DR
This conceptual workflow provides a stepwise translational roadmap from multi-omics discovery to clinical implementation, and is important to note that many of these applications remain at an early stage, with most evidence derived from small cohorts, preclinical models, or retrospective analyses, and prospective clinical validation is still needed.
Abstract
The integration of single-cell and spatial multi-omics technologies has substantially advanced our understanding of cancer as a dynamic ecosystem characterized by profound intratumoral heterogeneity and complex microenvironmental interactions. While traditional bulk sequencing approaches average signals across millions of cells, obscuring critical diversity that drives therapeutic resistance, these advanced platforms may enable simultaneous profiling of genomic, transcriptomic, epigenetic, and proteomic information while preserving spatial architecture. This mini-review synthesizes recent technological advances and clinical applications of integrated single-cell and spatial multi-omics for precision oncology, employing a structured narrative literature search across primary databases spanning 2021 to 2026. The convergence of these technologies may enable comprehensive tumor ecosystem mapping, may facilitate spatially informed biomarker discovery for patient stratification, has shown potential to predict immunotherapy response, and may guide rational combination therapy selection. Artificial intelligence holds promise as a computational engine for harmonizing heterogeneous datasets, with the potential to support future real-time clinical decision-making and adaptive treatment strategies. The proposed conceptual workflow provides a stepwise translational roadmap from multi-omics discovery to clinical implementation. However, it is important to note that many of these applications remain at an early stage, with most evidence derived from small cohorts, preclinical models, or retrospective analyses, and prospective clinical validation is still needed. Despite significant barriers including technical standardization, computational complexity, high costs, and regulatory challenges, the integration of single-cell and spatial multi-omics holds immense promise for advancing personalized precision oncology, with the ultimate goal of improving patient outcomes through dynamic, response-adaptive therapeutic approaches.