A. Bolck, Hella and Migh, Ede and Kriston, András and Natalia, Zajac and Susanne, Kreutzer and Totu, Tiberiu and Leary, Peter and Kovács, Ferenc and Rutishauser, Dorothea and Grossmann, Jonas and Lakshminarayanan, Harini and Pfammatter, Sibylle and Basu, Debleena and Litchfield, Cassandra and Buljan, Marija and J. Rupp, Niels and Horváth, Péter and Moch, Holger (2026) Deep visual multi-omics profiling reveals mechanisms that underlie cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma. EMBO MOLECULAR MEDICINE, 18. pp. 3850-3877. ISSN 1757-4676
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Abstract
Clear cell renal cell carcinoma exhibits striking intra-tumoral heterogeneity at morphological and genetic levels, complicating treatment and contributing to disease progression. CcRCCs with rhabdoid differentiation are highly aggressive tumors characterized by distinct histopathologies. However, the relationship between morphology, underlying molecular alterations, and tumor behavior remains largely unclear. Here, we present Deep Visual Multi-Omics, an approach integrating digital pathology, morphology-guided single-cell isolation, and ultra-sensitive multi-omics profiling to link cell morphologies to their molecular underpinnings. Across five tumors, we profiled ~40,000 AI-classified and expert-curated cells. We identified progressive molecular dysregulation across cells with increasing histopathological grade coexisting within heterogeneous tumors as well as distinct molecular alterations associated with aggressive rhabdoid ccRCC cells, including signatures consistent with enhanced FOXM1-driven proliferation, altered cell-matrix interactions, and a putative immunomodulatory phenotype. Notably, rhabdoid cells exhibited elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling, suggesting that they themselves may act as a source of signals influencing the local immune microenvironment. Besides providing new insights into the biology of ccRCC and highlighting avenues for future translational studies, this illustrates the potential of Deep Visual Multi-omics to dissect cancer heterogeneity and characterize high-risk cell populations.
| Item Type: | Article |
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| Additional Information: | Department of Pathology and Molecular Pathology, University Hospital Zürich and University of Zürich, Zürich, Switzerland Centre for Artificial Intelligence, Zürich University of Applied Sciences, Winterthur, Switzerland Synthetic and Systems Biology Unit, Biological Research Centre, HUN-REN, Szeged, Hungary Single-Cell Technologies Ltd., Szeged, Hungary Functional Genomics Center Zürich, Eidgenössische Technische Hochschule Zürich/University of Zürich, Zürich, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland Nanomaterials in Health Laboratory, Swiss Federal Laboratories for Materials Science and Technology, St. Gallen, Switzerland Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany Export Date: 21 August 2026; Cited By: 0; Correspondence Address: H.A. Bolck; Department of Pathology and Molecular Pathology, University Hospital Zürich and University of Zürich, Zürich, Switzerland; email: Hella.Bolck@zhaw.ch; H. Moch; Department of Pathology and Molecular Pathology, University Hospital Zürich and University of Zürich, Zürich, Switzerland; email: holger.moch@usz.ch; P. Horvath; Synthetic and Systems Biology Unit, Biological Research Centre, HUN-REN, Szeged, Hungary; email: horvath.peter@brc.hu |
| Subjects: | R Medicine / orvostudomány > R1 Medicine (General) / orvostudomány általában |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 05 Oct 2026 14:25 |
| Last Modified: | 05 Oct 2026 14:25 |
| URI: | https://real.mtak.hu/id/eprint/248331 |
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