From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies

Abstract

Cancer is a highly heterogeneous disease, and its diagnosis, prognosis, and therapeutic responsiveness depend not only on genetic alterations but also on the intricate organization of cells within the tumor microenvironment (TME). Spatial transcriptomics—a suite of techniques that preserves the spatial context of gene expression in intact tissue—has revolutionized our ability to decipher tumor architecture and intercellular communication. This review provides an in‐depth analysis of recent advancements in spatial transcriptomics technologies and their applications in solid tumor research. We first describe the evolution of spatial transcriptomics from early in situ hybridization methods to state‐of‐the‐art imaging‐ and sequencing‐based platforms. Next, we discuss how spatially resolved transcriptomics is transforming cancer research by revealing the molecular landscapes of tumor cores, invasive edges, and immunological niches. The integration of spatial transcriptomics with single‐cell multiomics and advanced computational algorithms is leading to the identification of novel prognostic and predictive biomarkers. Despite tremendous progress, challenges remain in terms of technical resolution, data processing, sample preparation, and clinical standardization. Finally, we highlight emerging trends—including three-dimensional (3D) spatial profiling, multimodal integration, and the use of artificial intelligence and Deep learning—to envision a future in which spatial transcriptomics will serve as a pivotal tool for precision oncology. Together, these developments promise to refine cancer biomarker studies and ultimately improve patient outcomes.