Integrative single-cell and spatial transcriptomic approaches to decipher the tumor microenvironment and therapeutic resistance in pancreatic cancer

Spatial transcriptomic and proteomic approaches for profiling prognostic cell communities in solid tumors.

BACKGROUND A comprehensive understanding of the complexity and heterogeneity of the tumor microenvironment (TME) is critical for advancing cancer treatment. Recent advances in spatial omics technologies have opened new avenues for an in-depth exploration of the TME. By integrating high-resolution spatial information from omics, spatial omics enables the systematic characterization of the spatial distribution of various cell types within tissues and their interaction networks, providing a panoramic view of the TME. Emerging studies have highlighted that specific spatial structures within the TME are strongly associated with cancer prognosis and treatment responses. AIM OF REVIEW This review focuses on the patterns of spatial cell distributions or characteristic cell structures and their relationship with cancer prognosis, underscoring the pivotal role of spatial heterogeneity in tumors. It aims to provide theoretical foundations for precise medicine approaches in prognostic evaluation and therapy design. KEY SCIENTIFIC CONCEPTS OF REVIEW This review discusses how spatial transcriptomics and proteomics enable extraction of spatial metrics including cell density, proximity, and community-level organization. We further summarize the spatial distribution patterns of cells and key cell community structures, emphasizing the pivotal role of spatial heterogeneity in tumors. Furthermore, it highlights critical spatial features, including immune cell infiltration patterns and vascular niches, that are strongly linked to patient outcomes. Additionally, the review discusses the challenges and future prospects of applying spatial omics in clinical applications. As the field evolves, spatial omics holds transformative potential to revolutionize oncology research, enabling novel approaches for risk stratification and improving patient outcomes.

Integrated single-cell and spatial transcriptomics reveals a multicellular program contributing to immunotherapy resistance and adverse clinical outcomes in clear cell renal cell carcinoma.

Advanced renal cell carcinoma frequently acquires resistance to immune checkpoint blockade (ICB), underscoring the pivotal influence of the tumor immune microenvironment (TME) on therapeutic efficacy. While recent studies have implicated multicellular crosstalk within the TME as a central driver of ICB resistance, the precise multicellular programs (MCPs) that orchestrate this process remain poorly defined. Here, through integrative single-cell and spatial transcriptomic profiling of clear cell RCC (ccRCC) cohorts, we delineated a previously unrecognized MCP associated with ICB resistance, distinguished by heightened lysosomal activity, adipogenic signaling, and rewired fatty acid metabolism. Within this program, we uncover a coordinated interplay among TAM_APOE, ccRCC_CXCL14, and endothelial cells, whereby ccRCC_CXCL14 recruits TAM_APOE, which subsequently promotes tumor lipid metabolic reprogramming and angiogenesis, forming a pro-tumorigenic feedforward loop. Spatial mapping revealed a malignant gene topic colocalizing with this MCP in tumor cores, which robustly predicted both unfavorable survival and resistance in ICB-treated patients. Functional assays confirmed that the CXCL14-TAM axis promotes metabolic reprogramming, while dual CXCR4 and PD-1 blockade synergistically reverses the resistant phenotype by restoring CD8⁺ T-cell cytotoxicity. Multiplex immunofluorescence further validated the enrichment of this MCP specifically in non-responders. Collectively, our study defines a spatially organized, functionally coordinated multicellular niche that drives ICB resistance in ccRCC, establishing both a predictive biomarker for patient stratification and a mechanistic framework for therapeutic intervention.

Tumor heterogeneity: development, mechanisms, and therapeutic implications

This review synthesizes key advances in understanding tumor heterogeneity: from its cellular origins and molecular mechanisms to its multidimensional manifestation in the tumor microenvironment and from metastatic heterogeneity to the fundamental causes of treatment resistance.

Single-cell sequencing in pan-cancer research: Decoding heterogeneity, tumor microenvironment, and clinical translation.

Single-cell sequencing (SCS) technologies have revolutionized cancer research by overcoming the critical limitations of bulk sequencing, enabling unprecedented resolution in deciphering intratumoral heterogeneity and the tumor microenvironment (TME). Unlike bulk approaches that average molecular signals across cell populations, SCS resolves rare subpopulations-such as pre-existing therapy-resistant clones and cancer stem cells-and reconstructs clonal evolutionary trajectories driving metastasis and treatment resistance. By profiling individual cells, SCS reveals dynamic cellular interactions, functional states, and spatial architectures within the TME, including immune-stromal crosstalk and transcriptional plasticity. Applied pan-cancer, SCS uncovers conserved molecular programs that transcend tissue origin, such as universal T-cell exhaustion signatures, recurrent stromal reprogramming, and fundamental pathways like epithelial-mesenchymal transition and angiogenesis. Technological advancements in scalability, cost-effectiveness, and spatial multi-omics integration now enable high-resolution mapping of tumor ecosystems. Large-scale initiatives like the Human Cell Atlas and CancerSCEM harmonize datasets, revealing both universal mechanisms and tissue-specific adaptations. Clinically, SCS enhances diagnostic sensitivity and enables precision prognostication through cellular biomarkers. It informs novel therapeutic strategies, such as targeting specific TME components or optimizing immunotherapy response. Emerging artificial intelligence (AI) tools further amplify SCS's impact by integrating multi-omic data to predict drug responses and resistance mechanisms. Despite challenges in standardization and cost, the future convergence of SCS, spatial technologies, and AI-driven modeling positions SCS as a cornerstone of precision oncology. This review systematically summarizes the advances of SCS technologies and their transformative applications in pan-cancer research and clinical translation.

Single-Cell RNA Sequencing of Pancreatic Circulating Tumor Cells.

Cellular plasticity and tumor ecosystem dynamics in prostate cancer: insights from single-cell and spatial transcriptomics.

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.