Tumor immunotherapy has revolutionized the landscape of cancer treatment by leveraging the immune system to effectively recognize and eliminate malignant cells. Over the past decade, pivotal breakthroughs have been made in the development of immune checkpoint inhibitors, targeting critical regulators such as programmed cell death protein 1 (PD-1), its ligand PD-L1, and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). These therapies have engendered durable responses across a variety of cancer types, significantly improving patient survival and transforming previously intractable cancers into manageable chronic conditions. The clinical success of these agents underscores the profound potential of manipulating immune checkpoints to overcome tumor-induced immunosuppression and restore robust antitumor immunity.
Despite these remarkable advances, the clinical implementation of tumor immunotherapy continues to face substantial obstacles, primarily due to the intrinsic complexity and heterogeneity of tumors. A considerable fraction of patients exhibit either primary resistance or develop adaptive resistance to immunotherapy, which is frequently attributed to variations in tumor genetics, epigenetic modifications, and the dynamic interplay within the tumor microenvironment (TME). The multifaceted mechanisms tumors employ to evade immune surveillance include alterations in antigen presentation pathways, recruitment of immunosuppressive cell populations, and the secretion of inhibitory cytokines, thereby perpetuating therapeutic challenges. Consequently, refining patient selection to predict responders accurately remains a critical unmet need.
In parallel to overcoming resistance mechanisms, managing immune-related adverse events (irAEs) has emerged as a formidable clinical challenge. These toxicities arise from immune system hyperactivation and can impact multiple organ systems, ranging from mild dermatologic manifestations to severe endocrinopathies and life-threatening pneumonitis or colitis. The unpredictable onset and severity of irAEs necessitate vigilant monitoring and prompt intervention, often requiring immunosuppressive treatments that may compromise the antitumor efficacy of immunotherapies. Hence, it is imperative to develop predictive biomarkers that not only forecast treatment efficacy but can also anticipate and mitigate these adverse immune responses.
Biomarkers have become essential pillars in the rational deployment of immunotherapies, offering insights that transcend standard clinical and pathological parameters. Their utility spans patient stratification, real-time monitoring of therapeutic effects, and prognostication. The expression of PD-L1 on tumor and immune cells currently serves as the most clinically adopted biomarker guiding the administration of PD-1/PD-L1 inhibitors, yet it suffers from limitations including intratumoral heterogeneity and variable assay standardization. Circulating biomarkers, including exosomes and cell-free nucleic acids, provide minimally invasive alternatives for longitudinal disease monitoring, enabling the dynamic assessment of tumor evolution and therapeutic resistance, although their clinical validation remains ongoing.
Recent advances in omics technologies—particularly spatial and single-cell omics—have opened unprecedented avenues for the biomolecular dissection of tumors and their microenvironment. Spatial omics integrates genomic, transcriptomic, proteomic, and metabolomic data while preserving the tissue architecture, thereby revealing the spatially resolved cellular interactions that underpin immune evasion and therapeutic resistance. Single-cell omics techniques, such as single-cell RNA sequencing, offer granular resolution to unravel intratumoral cellular heterogeneity, identifying rare and functionally distinct cell populations that conventional bulk analyses overlook. These technologies collectively empower researchers to characterize the heterogeneity and complexity of the immune landscape within tumors at unmatched precision.
The application of spatial transcriptomics has been instrumental in delineating the topography of immune infiltration, uncovering niches where immunosuppressive regulatory T cells and exhausted cytotoxic T lymphocytes coexist. This spatial delineation informs the understanding of why certain tumors respond to checkpoint blockade while others do not, highlighting critical microenvironmental contexts influencing therapeutic outcomes. Simultaneously, single-cell RNA sequencing enables the identification of transcriptional programs driving resistance pathways, including the upregulation of alternative immune checkpoints and metabolic reprogramming of tumor-infiltrating lymphocytes, offering novel therapeutic targets.
Incorporating metabolomic profiling at the single-cell level further enriches our comprehension of the metabolic crosstalk within the TME. Tumor and immune cells engage in metabolic competition and cooperation that profoundly affects immune cell function and survival. For example, hypoxia-induced metabolic shifts and lactate accumulation can impair effector T cell activity, promoting tumor immune evasion. Understanding these metabolic landscapes through single-cell metabolomics provides opportunities to design combinatorial therapeutic strategies that not only target immune checkpoints but also modulate metabolic constraints within the TME.
Despite the promise of these advanced omics technologies, challenges remain in their clinical translation. The integration and interpretation of multidimensional datasets demand sophisticated computational frameworks capable of managing data heterogeneity, batch effects, and spatial context. Moreover, the scalability and cost-effectiveness of spatial and single-cell omics are still barriers to routine clinical use. Ongoing collaborative efforts are focused on developing standardized protocols and analytical pipelines to ensure reproducibility and robustness across laboratories, facilitating the transition from bench to bedside.
Crucially, the synthesis of spatial and single-cell omics data heralds a new era in personalized cancer immunotherapy, where therapeutic regimens could be tailored based on the unique molecular and spatial features of an individual’s tumor. Such precision medicine approaches may enable not only the prediction of therapeutic efficacy but also the preemptive identification of potential irAEs, optimizing the delicate balance between antitumor immunity and immune tolerance. This strategy aligns with emerging paradigms in oncology, emphasizing dynamic and adaptive treatment decisions informed by comprehensive biomarker profiling.
Moreover, these innovations pave the way for the development of next-generation immunotherapies that exploit newly identified molecular targets and pathways unveiled by high-resolution omics analyses. By illuminating the intricate ecosystem of the TME with unparalleled clarity, researchers can design combination therapies that synergize immune checkpoint blockade with metabolic modulators, epigenetic drugs, or targeted delivery of adoptive cell therapies, potentially overcoming current therapeutic bottlenecks.
In conclusion, while substantial progress has been made in tumor immunotherapy, integrating spatial and single-cell omics technologies represents a transformative leap forward in biomarker discovery and precision oncology. These tools provide critical insights into the complex cellular choreography and molecular determinants of therapeutic response and resistance, equipping clinicians and researchers with the knowledge to refine and personalize immunotherapeutic strategies. By harnessing the full potential of these advanced methodologies, the oncology field moves closer to the ultimate goal: durable, effective, and safe cancer treatments tailored to the unique immunobiology of each patient’s tumor.
Subject of Research: Tumor immunotherapy biomarkers and their characterization via spatial and single-cell omics technologies.
Article Title: Application of spatial and single-cell omics in tumor immunotherapy biomarkers
News Publication Date: 27-May-2025
Web References: http://dx.doi.org/10.1016/j.lmd.2025.100076
Image Credits: Chu-chu Zhang, Hao-ran Feng, Ji Zhu, Wei-feng Hong.
Keywords: Immunotherapy
