DR-286 Biomarker discovery using machine learning to identify patients with advanced urothelial cancer benefitting from pembrolizumab treatment

Immune checkpoint inhibitors (ICIs) have been approved for treatment of metastatic urothelial cancer patients. Unfortunately, treatment response rates are low and many patients are being exposed to ineffective treatment with the risk of developing (severe) side effects. Currently, there is a lack of reliable biomarkers to identify patients that will benefit from ICIs. Therefore, the aim of this study is to identify potential genomic and transcriptomic predictive markers using an unbiased approach with machine learning for identification of response to ICIs.

Jeroen de Ridder, Universtair Medisch Centrum Utrecht (UMCU), the Netherlands