AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma

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 transcriptomic signatures were previously developed using clinical trial datasets 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 uncovered harmonized immune tumor microenvironment (HiTME) subtypes and examined them using multiplex immunofluorescence. A machine learning-based multiparametric approach was applied to harmonize genomic, transcriptomic, and TME features to predict ICI and TKI therapy response. Retrospective analyses support the clinical utility of this approach for therapy selection.