ABSTRACT
Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions: These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.
ABSTRACT
Radical cystectomy with preoperative cisplatin-based neoadjuvant chemotherapy (NAC) is the standard care for muscle-invasive bladder cancers (MIBCs). However, the complete response rate to this modality remains relatively low, and current clinicopathologic and molecular classifications are inadequate to predict NAC response in patients with MIBC. Here, we demonstrate that dysregulation of the glutathione (GSH) pathway is fundamental for MIBC NAC resistance. Comprehensive analysis of the multicohort transcriptomes reveals that GSH metabolism and immune-response genes are enriched in NAC-resistant and NAC-sensitive MIBCs, respectively. A machine-learning-based tumor/stroma classifier is applied for high-throughput digitalized immunohistochemistry analysis, finding that GSH dynamics proteins, including glutaminase-1, are associated with NAC resistance. GSH dynamics is activated in cisplatin-resistant MIBC cells, and combination treatment with a GSH dynamics modulator and cisplatin significantly suppresses tumor growth in an orthotopic xenograft animal model. Collectively, these findings demonstrate the predictive and therapeutic values of GSH dynamics in determining the NAC response in MIBCs.