ABSTRACT
BACKGROUND AND OBJECTIVE: Predicting response to therapy for each patient's tumor is critical to improving long-term outcomes for muscle-invasive bladder cancer. This study aims to establish ex vivo bladder cancer patient-derived organoid (PDO) models that are representative of patients' tumors and determine the potential efficacy of standard of care and curated experimental therapies. METHODS: Tumor material was collected prospectively from consented bladder cancer patients to generate short-term PDO models, which were screened against a panel of clinically relevant drugs in ex vivo three-dimensional culture. Multiomic profiling was utilized to validate the PDO models, establish the molecular characteristics of each tumor, and identify potential biomarkers of drug response. Gene expression (GEX) patterns between paired primary tissue and PDO samples were assessed using Spearman's rank correlation coefficients. Molecular correlates of therapy response were identified using Pearson correlation coefficients and Kruskal-Wallis tests with Dunn's post hoc pairwise comparison testing. KEY FINDINGS AND LIMITATIONS: A total of 106 tumors were collected from 97 patients, with 65 samples yielding sufficient material for complete multiomic molecular characterization and PDO screening with six to 32 drugs/combinations. Short-term PDOs faithfully represent the tumor molecular characteristics, maintain diverse cell types, and avoid shifts in GEX-based subtyping that accompany long-term PDO cultures. Utilizing an integrative approach, novel correlations between ex vivo drug responses and genomic alterations, GEX, and protein expression were identified, including a multiomic signature of gemcitabine response. The positive predictive value of ex vivo drug responses and the novel multiomic gemcitabine response signature need to be validated in future studies. CONCLUSIONS AND CLINICAL IMPLICATIONS: Short-term PDO cultures retain the molecular characteristics of tumor tissue and avoid shifts in expression-based subtyping that have plagued long-term cultures. Integration of multiomic profiling and ex vivo drug screening data identifies potential predictive biomarkers, including a novel signature of gemcitabine response. PATIENT SUMMARY: Better models are needed to predict patient response to therapy in bladder cancer. We developed a platform that uses short-term culture to best mimic each patient's tumor and assess potential sensitivity to therapeutics.