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Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

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Τίτλος: Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.
Συγγραφείς: Fuchs T; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany. Electronic address: tina.fuchs@med.uni-heidelberg.de., Menzel M; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany., Mößinger K; NGS Core Facility, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany., Kazdal D; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany., Kahles A; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany., Budczies J; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany., Stenzinger A; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
Πηγή: Seminars in cancer biology [Semin Cancer Biol] 2026 Sep; Vol. 125, pp. 20-33. Date of Electronic Publication: 2026 Jun 28.
Τύπος έκδοσης: Journal Article; Review; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Academic Press Country of Publication: England NLM ID: 9010218 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-3650 (Electronic) Linking ISSN: 1044579X NLM ISO Abbreviation: Semin Cancer Biol Subsets: MEDLINE
Imprint Name(s): Publication: London : Academic Press
Original Publication: Philadelphia, PA, USA : Saunders Scientific Publications, W.B. Saunders Co., c1990-
Ιατρικοί όροι (MeSH): Single-Cell Analysis*/methods , Neoplasms*/genetics , Neoplasms*/pathology , Biomarkers, Tumor*/genetics , Sequence Analysis, RNA*/methods , Genetic Heterogeneity*, Tumor Microenvironment/genetics ; Humans ; Single-Cell Gene Expression Analysis ; Animals ; Transcriptome
Περίληψη: 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.
(Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Competing Interests: Declaration of Competing Interest Tina Fuchs: nothing to declare Michael Menzel: nothing to declare Katharina Mößinger: nothing to declare Daniel Kazdal: nothing to declare Andy Kahles: nothing to declare Jan Budczies: nothing to declare Albrecht Stenzinger: Given the role as Guest Editor for Seminars in Cancer Biology, Albrecht Stenzinger had no involvement in the peer-review of this article and has no access to information regarding its peer-review. Full responsibility for the editorial process for this article was delegated to another journal editor.
Contributed Indexing: Keywords: Lung cancer; Single cell sequencing; Translational oncology; Tumor heterogeneity; Tumor microenvironment
Substance Nomenclature: 0 (Biomarkers, Tumor)
Entry Date(s): Date Created: 20260628 Date Completed: 20260904 Latest Revision: 20260927
Update Code: 20260927
DOI: 10.1016/j.semcancer.2026.06.005
PMID: 42365863
Βάση Δεδομένων: MEDLINE
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  Data: Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.
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  Data: <searchLink fieldCode="AU" term="%22Fuchs+T%22">Fuchs T</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany. Electronic address: tina.fuchs@med.uni-heidelberg.de.<br /><searchLink fieldCode="AU" term="%22Menzel+M%22">Menzel M</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.<br /><searchLink fieldCode="AU" term="%22Mößinger+K%22">Mößinger K</searchLink>; NGS Core Facility, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Kazdal+D%22">Kazdal D</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.<br /><searchLink fieldCode="AU" term="%22Kahles+A%22">Kahles A</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.<br /><searchLink fieldCode="AU" term="%22Budczies+J%22">Budczies J</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.<br /><searchLink fieldCode="AU" term="%22Stenzinger+A%22">Stenzinger A</searchLink>; Institute of Pathology, Heidelberg University Hospital, Center for Personalized Medicine (ZPM), German Cancer Consortium (DKTK), Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
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  Data: <searchLink fieldCode="JN" term="%229010218%22">Seminars in cancer biology</searchLink> [Semin Cancer Biol] 2026 Sep; Vol. 125, pp. 20-33. <i>Date of Electronic Publication: </i>2026 Jun 28.
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  Data: 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.<br /> (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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  Data: Declaration of Competing Interest Tina Fuchs: nothing to declare Michael Menzel: nothing to declare Katharina Mößinger: nothing to declare Daniel Kazdal: nothing to declare Andy Kahles: nothing to declare Jan Budczies: nothing to declare Albrecht Stenzinger: Given the role as Guest Editor for Seminars in Cancer Biology, Albrecht Stenzinger had no involvement in the peer-review of this article and has no access to information regarding its peer-review. Full responsibility for the editorial process for this article was delegated to another journal editor.
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