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Published on: January 22, 2013
Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma
Hella A Bolck1,2, Ede Migh3, Andras Kriston4
1Department of Pathology and Molecular Pathology, University Hospital Zürich and University of Zürich, Zürich, Switzerland. Hella.Bolck@zhaw.ch.
EMBO Molecular Medicine
|August 7, 2026
Summary
Deep Visual Multi-Omics links cell appearance to molecular changes in clear cell renal cell carcinoma (ccRCC). This reveals distinct molecular drivers in aggressive rhabdoid ccRCC cells, impacting tumor progression and immune response.
Area of Science:
- Oncology
- Genomics
- Computational Pathology
Background:
- Clear cell renal cell carcinoma (ccRCC) presents significant intra-tumoral heterogeneity, complicating treatment strategies and disease management.
- Rhabdoid differentiation in ccRCC signifies highly aggressive tumors, but the link between their distinct morphology and molecular characteristics is poorly understood.
Purpose of the Study:
- To integrate digital pathology, single-cell isolation, and multi-omics profiling to connect cell morphology with molecular underpinnings in ccRCC.
- To investigate the molecular basis of tumor heterogeneity and identify specific alterations in aggressive rhabdoid ccRCC cells.
Main Methods:
- Developed Deep Visual Multi-Omics, an approach combining AI-driven digital pathology with morphology-guided single-cell isolation.
- Performed ultra-sensitive multi-omics profiling on approximately 40,000 AI-classified and expert-curated cells from five ccRCC tumors.
- Analyzed molecular dysregulation across varying histopathological grades and characterized aggressive rhabdoid ccRCC cell populations.
Main Results:
- Identified progressive molecular alterations correlating with increasing histopathological grade within heterogeneous ccRCC tumors.
- Discovered distinct molecular signatures in aggressive rhabdoid ccRCC cells, including enhanced FOXM1-driven proliferation and altered cell-matrix interactions.
- Observed elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling in rhabdoid cells, suggesting a role in modulating the immune microenvironment.
Conclusions:
- Deep Visual Multi-Omics effectively dissects cancer heterogeneity and characterizes high-risk cell populations in ccRCC.
- Rhabdoid ccRCC cells possess unique molecular features that contribute to their aggressive behavior and influence the tumor immune microenvironment.
- Findings provide novel biological insights into ccRCC and suggest potential avenues for translational research and targeted therapies.
