Highlights
• Multimodal AI-based ICI and TKI response prediction models
• Curation of the largest known harmonized ccRCC transcriptomic database
• Prognostic and predictive ccRCC tumor microenvironment subtypes for therapy response
• An integrated decision-tree model to guide ICI and TKI therapy selection

Summary
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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