Molecular heterogeneity in urothelial carcinoma and determinants of clinical benefit to PD-L1 blockade

Checkpoint inhibitors targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have revolutionized cancer therapy across many indications including urothelial carcinoma (UC). Because many patients do not benefit, a better understanding of the molecular mechanisms underlying response and resistance is needed to improve outcomes. We profiled tumors from 2,803 UC patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab by RNA sequencing (RNA-seq), a targeted DNA panel, immunohistochemistry, and digital pathology. Machine learning identifies four transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care is observed in immune and basal tumors, through different response mechanisms. A self-supervised digital pathology approach can classify molecular subtypes from H&E slides with high accuracy, which could accelerate tumor molecular profiling. This study represents a large integration of UC molecular and clinical data in randomized trials, paving the way for clinical studies tailoring treatment to specific molecular subtypes in UC and other indications. [Display omitted] •Four molecular subtypes in urothelial carcinoma with distinct tumor microenvironments•Survival benefit from atezolizumab in patients with immune and basal tumors•Digital pathology predicts molecular subtypes from single H&E slides•This classification could help tailor treatment of urothelial carcinoma Hamidi et al. report the molecular profiling of 2,803 urothelial carcinoma tumors from four randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab. Machine learning identifies four transcriptional subtypes. Survival benefit from atezolizumab is observed in immune and basal tumors. Digital pathology can classify molecular subtypes from H&E slides, which could accelerate molecular profiling in prospective studies.