Unveiling tumor heterogeneity by single cell RNA-sequencing: from basic considerations to clinical applications
Tumor heterogeneity—encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts—is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-c...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 2026
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| In: |
Seminars in cancer biology
Year: 2026, Volume: 125, Pages: 20-33 |
| ISSN: | 1096-3650 |
| DOI: | 10.1016/j.semcancer.2026.06.005 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.semcancer.2026.06.005 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S1044579X26000611 
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| Author Notes: | Tina Fuchs, Michael Menzel, Katharina Mößinger, Daniel Kazdal, Andy Kahles, Jan Budczies, Albrecht Stenzinger |
| Summary: | Tumor heterogeneity—encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts—is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques—such as deep-learning classifiers and graph-based models—with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine. |
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| Item Description: | Online veröffentlicht: 28. Juni 2026, Artikelversion: 1. Juli 2026 Gesehen am 29.07.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1096-3650 |
| DOI: | 10.1016/j.semcancer.2026.06.005 |