| Abstract: |
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. © 2024 Elsevier Inc. |
| Keywords: |
immunohistochemistry; cancer survival; human tissue; treatment response; aged; major clinical study; overall survival; somatic mutation; genetics; advanced cancer; cancer patient; disease free survival; metabolism; progression free survival; apoptosis; antineoplastic metal complex; gene expression; pathology; transcriptomics; tumor marker; bladder tumor; urinary bladder neoplasms; monoclonal antibody; urothelial carcinoma; drug therapy; carcinoma, transitional cell; transitional cell carcinoma; neutrophil chemotaxis; genetic heterogeneity; programmed death 1 ligand 1; programmed death 1 receptor; randomized controlled trial (topic); digital pathology; clinical outcome; machine learning; immune checkpoint inhibitor; antibodies, monoclonal, humanized; molecular heterogeneity; humans; human; male; female; article; rna sequencing; immune checkpoint inhibitors; atezolizumab; biomarkers, tumor; patient stratification; cd274 protein, human; b7-h1 antigen; molecular fingerprinting; pd-l1 blockade
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