Spatial and single-cell omics transform cancer immunotherapy biomarker discovery

Cancer immunotherapy has reshaped oncology by harnessing the immune system to target and eliminate malignant cells. Treatments such as immune checkpoint inhibitors and adoptive T-cell therapies have yielded remarkable benefits for some patients, but low response rates and immune-related side effects remain major challenges. Identifying reliable biomarkers that can predict treatment response and guide patient stratification is critical for improving outcomes.

This review highlights how emerging technologies—spatial omics and single-cell omics—are addressing these challenges. Spatial omics integrates molecular profiling with tissue localization, allowing researchers to visualize immune cell infiltration patterns, tumor-stroma interactions, and molecular heterogeneity within the tumor microenvironment. Single-cell omics, including scRNA-seq and scATAC-seq, provides high-resolution insights into cellular diversity, gene regulation, and rare immune populations associated with therapy resistance.

Together, these approaches have enabled the discovery of novel biomarkers, such as chemokine expression profiles and exhausted T-cell subsets, which are critical for predicting immunotherapy success. Multi-omics integration, combined with artificial intelligence and machine learning, further enhances the interpretation of complex datasets and improves predictive accuracy.

Despite these advances, challenges remain in data standardization, integration across platforms, and cost-effectiveness. Nevertheless, the incorporation of spatial and single-cell omics into biomarker research represents a transformative step toward precision oncology. By enabling dynamic monitoring, personalized patient selection, and biomarker-guided therapies, these technologies pave the way for safer, more effective cancer immunotherapies.