Clear cell renal cell carcinoma (ccRCC) progresses along two predominant evolutionary trajectories, defined by PBRM1 (~40%) or BAP1 (~15%) mutations on a VHL-inactivated background. They have distinct patterns of evolutionary tempo and mode, and vastly different clinical outcomes, yet the underlying genotype-specific molecular phenotypic programmes are unknown. We established a patient-derived preclinical model biobank that captures the genetic diversity of ccRCC. Through integrative analyses of preclinical models and tumour bulk and single cell profiling, we identified transcriptional and epigenetic changes specific to PBRM1- and BAP1-driven ccRCC. Modelling PBRM1 loss in vitro demonstrates that it reinforces renal lineage identity and maintains progenitor-like cell state. In contrast, BAP1 loss drives inflammatory signalling and chromosomal instability. These insights reconcile the distinct evolutionary modes (branched versus punctuated), tempo (slow versus fast) and clinical outcomes associated with PBRM1 and BAP1 mutations, respectively, establishing a framework for patient stratification and genotype-directed therapeutic development.
Disclosure in the era of generative artificial intelligence
Generative artificial intelligence (AI) has rapidly become embedded in academic writing, assisting with tasks ranging from language editing to drafting text and producing evidence. Despite



