AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 40834854.
- Also identified by DOI 10.1016/j.xcrm.2025.102299 and PMC identifier 12432380.
- Licence recorded as CC BY-NC-ND.
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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.
Medical subject headings
- Carcinoma, Renal Cell
- Kidney Neoplasms
- Immunotherapy
- Algorithms
- Molecular Targeted Therapy