Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation.

BACKGROUND/AIM: Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response rates vary significantly among patients. Predicting treatment response is crucial to avoid unnecessary toxicity and optimize therapeutic strategies. This study aims to develop a deep learning model leveraging RNA sequencing data to predict chemotherapy response in UC patients. MATERIALS AND METHODS: We developed a deep learning model using RNA sequencing gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus to predict chemotherapy (gemcitabine and cisplatin) response in UC patients. The model was externally validated using an independent cohort from the Pusan National University Yangsan Hospital. Model interpretation was performed through gene ontology and survival analyses using predictions from TCGA samples not included in the training set. CONCLUSION: Our transcriptome-based deep learning approach offers a promising computational strategy for predicting chemotherapy response in urothelial carcinoma. By integrating high-dimensional RNA-seq data and advanced machine learning techniques, we provide a potential decision-support tool for personalized treatment planning.