How single-cell and spatial omics are reshaping the understanding of metabolic reprogramming and tumor–immune ecosystems in NSCLC is discussed, and how these findings may inform biomarker discovery, patient stratification, resistance monitoring, and rational combination therapy are discussed.
Abstract
Non-small cell lung cancer (NSCLC) is often presented as a success story for precision oncology, but that success remains uneven. Targeted agents and immune checkpoint inhibitors have changed treatment for molecularly defined or immune-responsive tumors, yet many patients relapse as resistant clones emerge, tumor cells shift state, and local immune pressure changes across the lesion. Bulk genomic and transcriptomic assays have been useful for clinical stratification, but they average signals across mixed cell populations and therefore miss rare resistant cells, transient drug-tolerant states, and spatially restricted tumor–immune interactions. Single-cell and spatial omics now provide a more direct way to examine these problems. In NSCLC, single-cell RNA sequencing, single-cell chromatin profiling, spatial transcriptomics, and multiplex imaging have begun to map malignant epithelial plasticity, persister-like states, dysfunctional immune compartments, stromal remodeling, and metabolically distinct niches. These approaches do not simply add resolution; they help connect cell state, metabolic activity, tissue location, and treatment response. This mini review discusses how single-cell and spatial omics are reshaping our understanding of metabolic reprogramming and tumor–immune ecosystems in NSCLC, and how these findings may inform biomarker discovery, patient stratification, resistance monitoring, and rational combination therapy.
Prostate cancer is a hormone-driven malignancy, but its clinical behavior cannot be explained by androgen receptor (AR) signaling alone. Single-cell and spatial omics are redefining prostate cancer as an evolving metabolic-immune ecosystem in which malignant epithelial states, stromal niches, myeloid programs, T-cell exclusion, vascular remodeling, and treatment-imposed selection pressures jointly shape progression and resistance. These technologies have resolved the normal prostate epithelial hierarchy, pre-existing castration-resistant-like cells, basal-like, club-like, and hillock-like programs, neuroendocrine transformation states, immunosuppressive macrophage populations, fibroblast states, and spatial neighborhoods that are difficult to detect by bulk sequencing. Treatment-associated changes have been characterized most directly in androgen-axis and immune contexts, whereas evidence related to taxanes, DNA damage response (DDR)-targeted therapy, and radioligand therapy remains emerging or hypothesis-generating. This review synthesizes single-cell and spatial omics evidence for metabolic-immune ecosystems in prostate cancer, emphasizing the transition from androgen dependence to castration resistance, lineage plasticity, metastatic niche adaptation, and therapy resistance. We argue that the next translational step is not simply to catalog additional cell types, but to connect longitudinal cell-state maps, spatially resolved metabolic dependencies, immune-neighborhood biomarkers, and mechanism-matched combination therapies.
Recent discoveries from single-cell and spatial analyses are summarized, their implications for biomarker development and treatment stratification are discussed, and ongoing technical challenges are outlined, including standardization, reproducibility, and clinical scalability.
R. Watanabe, T. Chu, N. Miura et al.· American journal of clinical...· 2 citations
Immune checkpoint inhibitors have reshaped the treatment landscape of lung adenocarcinoma, but primary, adaptive and acquired resistance are still the main clinical obstacles. This review integrates LUAD-specific evidence with broader NSCLC single-cell and spatial-omics studies, emphasizing lung adenocarcinoma where subtype-specific data are available. We discuss how therapeutic stress can select or induce malignant epithelial states that manifest as impaired antigen presentation, altered interferon signaling, partial epithelial-mesenchymal transition, stress tolerance, and hypoxia-related metabolic adaptations. These programs interact with dendritic cells, exhausted and precursor T cells, regulatory T cells, B cells, tertiary lymphoid structures, myeloid cells, cancer-associated fibroblasts, extracellular matrix, and blood vessels to generate a unique resistance-associated spatial immune niche. Special emphasis is given to the niche with adaptive suppression, immunologically excluded interstitial boundaries, myeloid-rich hypoxic-metabolic suppressive niches, antigen-presentation-low residual niches, and tertiary lymphoid structure (TLS)-associated immuno-tissue niche. We further highlight the translational potential of integrating single-cell and spatial features for biomarker discovery, prognostic assessment, interpretation of residual disease, and mechanism-matched combination therapies. Finally, we outline key challenges for clinical implementation, including sampling bias, platform heterogeneity, distinguishing association from causation, and the need for FFPE-compatible testing and prospective validation. Spatially informed single-cell analysis may enable lung adenocarcinoma to shift from descriptive immunophenotyping to ecological diagnosis and precision immune regulation.
It is identified that lymph node metastases in high-risk neuroblastomas display distinctive cellular heterogeneity and plasticity, marked by mesenchymal-like and stem-like states and heightened epithelial-to-mesenchymal transition activity compared to primary adrenal tumors.
Laiman N. Wu, Janet L. Oblinger, Da-Zhuan Xin et al.· Science Advances· 0 citations
ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive human malignancies and has an extremely poor prognosis. Its progression is largely driven by a highly complex and immunosuppressive tumor microenvironment (TME), highlighting the urgent need for a deeper understanding of its molecular mechanisms. Recent advances in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have provided unprecedented opportunities to dissect cellular heterogeneity, spatial organization, and gene expression dynamics within the TME. In this review, we summarize the major scRNA-seq and ST technologies and their unique strengths in cancer research and highlight their integrated applications in revealing PDAC heterogeneity, stromal–immune interactions, and mechanisms of therapeutic resistance. We further discuss how these approaches can inform biomarker discovery and guide the development of novel therapeutic strategies. Together, these findings suggest that integrated single-cell and spatial transcriptomics offers transformative potential to advance precision oncology and improve outcomes for patients with pancreatic cancer.
Mengting Luo, Feng Shen, Wanli Xu et al.· Cancer Biology & Therapy· 0 citations
Background Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid proliferation, early dissemination, and limited durable benefit from current chemoimmunotherapy. Although immune checkpoint blockade has modestly improved clinical outcomes, the regulatory logic linking malignant cell states to the tumor immune microenvironment remains incompletely understood. Here, we applied an integrative single-nucleus transcriptomic framework to dissect tumor cell heterogeneity, regulatory programs, and immune-stromal communication networks in SCLC. Methods Publicly available Single-nucleus RNA sequencing data from primary and metastatic SCLC samples were analyzed using Seurat-based clustering, inferCNV-based malignant cell identification, differential expression analysis, pathway enrichment, metabolic and stemness scoring, pseudotime trajectory reconstruction, CellChat-mediated cell-cell communication inference, and transcription factor regulatory module analysis. A UBE2C-enriched proliferative tumor cell subpopulation was prioritized for functional validation. siRNA-mediated UBE2C knockdown was performed in DMS114 and NCI-H446 SCLC cell lines, followed by qRT-PCR, CCK-8, colony formation, transwell migration, and Annexin V/PI apoptosis assays. Results Using snRNA-seq, we identified multiple cell types and resolved a UBE2C+ subpopulation with marked proliferative features. UBE2C+ subpopulation displayed strong G2/M-phase enrichment, elevated mitotic and cell cycle programs. And pseudotime analysis positioned C3 UBE2C+ tumor cells at a proliferative state during tumor cell state evolution. Cell-cell communication analysis suggested that this subpopulation might interact with macrophages and fibroblasts through GRN-SORT1 and THBS1-CD47/CD36 signaling axes, indicating a potential link between proliferative tumor states and candidate communication axes. Transcription factor module analysis further revealed enrichment of cell cycle-associated regulators, including MYBL2, NFYB, E2F2, TGIF1, and RXRG, in the C3 subpopulation. Functionally, UBE2C knockdown significantly suppressed proliferation, clonogenic growth, and migration while increasing apoptosis in SCLC cells. Conclusions This study identified UBE2C+ proliferative tumor cells as a functionally relevant malignant subpopulation in SCLC and links this state to immune-stromal communication networks within the tumor microenvironment. By integrating single-nucleus transcriptomics, regulatory network inference, intercellular communication analysis, and in vitro validation, our findings nominate UBE2C as a potential candidate functional regulator and provide a systems-level framework for investigating the cancer-immunity regulome in SCLC.
Hongling Jia, Yongxuan An, Bing Chen et al.· Frontiers in Immunology· 0 citations
