Incremental Predictive Value and Representation of Transcriptomic Features for Immunotherapy Response in Advanced Urothelial Carcinoma

Transcriptomic profiling may improve immunotherapy response prediction, but its incremental value beyond established clinical and tumor biomarkers is unclear. In 298 patients with advanced urothelial carcinoma from IMvigor210, an atezolizumab (PD-L1 inhibitor) trial, we compared knowledge-guided tumor microenvironment gene-set features with data-driven transcriptome-wide gene selection. Knowledge-guided features improved AUPRC by 0.058 beyond clinical variables alone, with smaller gains after including standard biomarkers (TMB, PD-L1; +0.011) and research-enriched biomarkers (neoantigen burden, immune phenotype; +0.016). Data-driven gene selection was unstable across folds (mean Jaccard similarity, 0.227) and, under the clinical-plus-biomarker baseline, yielded an AUPRC 0.150 lower than the knowledge-guided representation. Despite this instability, IL-6/JAK/STAT3 signaling was the most recurrent Hallmark pathway, suggesting pathway-level biological convergence. External evaluation showed a similar pattern: knowledge-guided features improved AUPRC by 0.128 over clinical variables vs. 0.019 over clinical-plus-biomarker baseline. These findings support biologically informed transcriptomic representation when molecular profiling adds value beyond established biomarkers.