Identification of malignant cells in single-cell transcriptomics data

Article Article scientifique Revue de la littérature Identification of malignant cells in single-cell transcriptomics data eng 2025-08-22 2025-08-22 author Andreatta Massimo 0000-0002-8036-2647 Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland. massimo.andreatta@unige.ch.,Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland. massimo.andreatta@unige.ch.,Translational Research Centre in Onco-Hematology (CRTOH), Geneva, Switzerland. massimo.andreatta@unige.ch. 435130 Département de pathologie et immunologie (PATIM) author Garnica Caparros Josep 0000-0001-9493-1321 Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland.,Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.,Translational Research Centre in Onco-Hematology (CRTOH), Geneva, Switzerland. 440383 author Carmona Santiago 0000-0002-2495-0671 Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland.,Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.,Translational Research Centre in Onco-Hematology (CRTOH), Geneva, Switzerland. 433334 Département de pathologie et immunologie (PATIM) 1264 10.1038/s42003-025-08695-4 40846797 2399-3642 PMC12373766 https://www.nature.com/articles/s42003-025-08695-4 Single-cell transcriptomics has significantly advanced our ability to uncover the cellular heterogeneity of tumors. A key challenge in single-cell transcriptomics is identifying cancer cells and, in particular, distinguishing them from non-malignant cells of the same cell lineage. Focusing on features that can be measured by single-cell transcriptomics, this review explores the molecular aberrations of cancer cells and their observable readouts at the RNA level. Identification of bona fide cancer cells typically relies on three main features, alone or in combination: i) expression of cell-of-origin marker genes; ii) inter-patient tumor heterogeneity; iii) inferred copy-number alterations. Depending on the cancer type, however, alternative or additional features may be necessary for accurate classification, such as single-nucleotide mutations, gene fusions, increased cell proliferation, and altered activation of signaling pathways. We summarize computational approaches commonly applied in single-cell analysis of tumoral samples, as well as less explored features that may aid the identification of malignant cells. Biomarkers, Tumor / genetics Computational Biology / methods DNA Copy Number Variations Gene Expression Profiling / methods Gene Expression Regulation, Neoplastic Humans Neoplasms / genetics Neoplasms / pathology Single-Cell Analysis / methods Transcriptome Dewey 616.07 Swiss National Science Foundation A meta-analysis of single-cell transcriptomics data to characterize the landscape of tumor infiltrating T-cells 180010 CH Careers 137 Département de pathologie et immunologie 21F false 1459 Science des données "-omiques" à l'échelle de la cellule unique pour le cancer et l'immunologie 1071 Communications biology 8 1 9fea96e4-8450-43e5-b86a-c7ae8d8f0748 Article (Published version) Public s42003-025-08695-4.pdf 701788 application/pdf CC BY-4.0 85d6d4399fcbaa22a3c5a3ac3e157509b78e03881d34aa149d98899b74111790 openAccess Faculté de médecine / Section de médecine fondamentale / Département de pathologie et immunologie

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Identification of malignant cells in single-cell transcriptomics data	Communications biology			2025	25	95

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Garnica Caparros, Josep