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Review

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

Aug 2026 · Seminars in Oncology · Vol 53 6, pp. 152540 · 0 citations · 132 references
Medicine

TL;DR

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.

Abstract

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.

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