Figure 3. Translating spatial analysis of hepatocellular carcinoma (HCC) into clinical applications.
Spatial multi-omic technologies integrate histology, genomics, transcriptomics, and proteomics to generate high-dimensional tissue maps that link molecular states to spatial context. These approaches have enabled four major clinical applications in HCC:
(1) Predict clinical outcomes- Spatially resolved biomarkers integrating tumor, immune, and stromal architecture can stratify patients by recurrence risk and overall survival.
(2) Personalized medicine- Spatial multi-omic profiling differentiates immune, stromal, and molecular tumor states to guide the selection of systemic, targeted, or combination therapies.
(3) Response prediction- Spatial signatures of immune proximity, ligand–receptor pairing, and checkpoint expression can be translated into predictive classifiers to identify likely responders before therapy initiation.
(4) Identify new therapeutic axes- Mapping cellular interactions at single-cell resolution uncovers novel pathways within the tumor microenvironment that can be targeted to modulate disease progression and overcome treatment resistance.
Together, these advances illustrate how spatial biology bridges tissue architecture with actionable clinical insight in liver cancer.