Application of spatial and single-cell omics in tumor immunotherapy biomarkers

Tumor immunotherapy has emerged as a transformative approach in cancer therapy that harnesses the immune system to target and eliminate malignant cells.¹ Landmark advancements, including the development of immune checkpoint inhibitors such as anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) and anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies, have demonstrated notable efficacy across diverse cancer types, leading to significantly improved survival outcomes for many patients. Personalized strategies, such as adoptive T cell therapies and cancer vaccines, continue to expand in parallel, thereby further enriching the therapeutic arsenal in oncology.

Despite these advances, the widespread implementation of immunotherapy continues to face numerous challenges. For instance, not all patients respond favorably to treatment, owing to various factors, such as tumor heterogeneity, immune evasion mechanisms, and variations in the tumor microenvironment (TME). Additionally, immune-related adverse events (irAEs), resulting from the overstimulation of the immune system, can affect multiple organ systems and pose considerable challenges for therapeutic management. Consequently, there is a pressing need for identifying reliable biomarkers capable of predicting therapeutic responses, minimizing irAEs, and enabling effective patient stratification for the optimization of immunotherapy outcomes.

Biomarkers have emerged as indispensable tools in oncology, offering critical diagnostic and prognostic insights, in addition to guiding the selection of appropriate therapeutic strategies. In the context of immunotherapy, biomarkers are used to identify patient populations that are most likely to benefit from specific therapeutic interventions, predict treatment responses, and monitor therapeutic efficacy. Several key biomarkers, including PD-L1 expression, have already been integrated in clinical practice to inform the use of immune checkpoint inhibitors. These biomarkers enable the implementation of personalized therapy by aligning the molecular and immunological characteristics of tumors with specific therapeutic interventions. In addition to immunotherapy, circulating biomarkers such as exosomes offer non-invasive approaches for monitoring disease progression and therapeutic outcomes in real time. However, the biomarkers implemented in current research have various limitations in terms of sensitivity, specificity, and reproducibility. The complex and dynamic nature of both the TME and host immune responses highlights the need to identify robust, multidimensional biomarkers for enhancing clinical decision-making.

Emerging technologies, such as spatial and single-cell omics, exhibit transformative potential for addressing these challenges (Fig. 1). Spatial omics integrates spatial context with multi-omics data, thereby enabling researchers to elucidate the spatial organization of molecular and cellular interactions within the TME. Meanwhile, single-cell omics offers unprecedented resolution for analyzing cellular heterogeneity, gene expression, epigenomic profiles, and metabolic states at the single-cell level. By integrating these advanced technologies, researchers can identify novel biomarkers that can aid in the characterization of spatial heterogeneity and cellular diversity, thereby facilitating more accurate predictions of immunotherapy outcomes. For instance, spatial transcriptomics (ST) is used to identify immune cell infiltration patterns associated with therapeutic responses, while single-cell RNA sequencing (scRNA-seq) is employed for identifying rare cell populations that contribute to immunotherapy resistance.

The incorporation of spatial and single-cell omics into biomarker discovery not only advances our understanding of tumor biology but also paves the way for the development of more effective personalized and innovative cancer immunotherapies. Altogether, these approaches represent the next-generation strategy in biomarker research, providing the precision and depth necessary for overcoming existing limitations and improving patient outcomes.