Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation.
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.
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.
The deep learning model demonstrated excellent predictive performance, achieving 94.7% accuracy in the training dataset and 90.0% accuracy in external validation. Gene ontology analysis revealed four key functional clusters associated with chemotherapy response: DNA damage response, cell cycle regulation, kinesins/microtubule dynamics, and mitotic cytokinesis. Notably, the model showed significant prognostic value in early-stage, with predicted responders displaying markedly better survival outcomes (p=0.019).
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.
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.
The deep learning model demonstrated excellent predictive performance, achieving 94.7% accuracy in the training dataset and 90.0% accuracy in external validation. Gene ontology analysis revealed four key functional clusters associated with chemotherapy response: DNA damage response, cell cycle regulation, kinesins/microtubule dynamics, and mitotic cytokinesis. Notably, the model showed significant prognostic value in early-stage, with predicted responders displaying markedly better survival outcomes (p=0.019).
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.
Authors
Kang Kang, Lee Lee, Oh Oh, Nam Nam, Kim Kim, Ryu Ryu, Ki Ki, Kang Kang, Kim Kim, Lee Lee, Choi Choi, Hong Hong, Park Park
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