ArticleCell reports. Medicine2025
AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- Should Metastatic Site Inform First-Line Therapy in Clear Cell Renal Cell Carcinoma?Targeted oncology · 2026Review
- Tumor organoids as a revolutionary platform for advancing cancer nanomedicine.Molecular cancer · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Review
- Gamma-aminobutyric acid transaminase mediates tumor suppression in renal cell carcinoma through the cGAS-STING-interferon-β axis.Scientific reports · 2026Article
- Advances in immunotherapy for thyroid malignancies: from molecular targets to clinical outcomes.Frontiers in medicine · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma.Cell reports. Medicine · 2025Article
- Recent progress on tyrosine kinase inhibitors resistance in renal cell carcinoma: another brick in the wall?Cancer drug resistance (Alhambra, Calif.) · 2025Review
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Authors and funding
20 authors.
Funding
Abstract
Treatment for metastatic clear cell renal cell carcinoma (ccRCC) has dramatically advanced with tyrosine kinase inhibitor (TKI) and immune checkpoint inhibitor (ICI) administration. However, most patients eventually succumb to their disease, and toxicities associated with individual treatment modalities are significant. Multiple single-modality transcriptomic signatures have been developed to predict treatment response, yielding insightful yet inconsistent results when applied to independent cohorts. By unifying transcriptomic data from 14 cohorts (total n = 3,621), we present harmonized immune tumor microenvironment (HiTME) ccRCC subtypes validated with spatial proteomics. This AI-based multimodal approach integrates genomic, transcriptomic, and tumor microenvironment (TME) features for ICI and TKI therapy response prediction.
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Registered trials
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