Bladder cancer (BLCa) is the 5th most common cancer in the world and the costliest cancer to treat on a per patient basis. Muscle bladder cancer (MIBC) is a highly aggressive disease, to which about half of the patients succumb within 5 years after diagnosis. MIBC is highly heterogeneous and clinical guidelines offer cisplatin-based neoadjuvant chemotherapy (NAC) to all patients. Despite NAC, 45% of patients die within 5 years and 60% due to lack in response. The mechanisms of resistance of MIBC to systemic treatment are largely unknown. Moreover, models for the personalized investigation of drug response and strategies for co-targeting do not exist.
Based on the expression of gene signatures MIBC can be subclassifies at least into 4 groups: Claudin-low, Basal, Luminal infiltrated Luminal tumor. Our patient derived models (Patient derived organoids (PDO), patient derived xenograft (PDX), and ex-vivo tissue slices) can be used for molecular and functional characterization of parental tumor and to test the therapeutic potential and possible responsiveness to selected drugs. In addition, our group applied an algorithm for genomic profiling and in-silico drug predictions developed at the ETH Zurich technology platform NEXUS on 412 patients from The Cancer Genome Atlas (TCGA) bladder cancer data set. 44 drug-related genes were identified with clinically actionable variants. These genes related to 63 known and novel promising therapy targets. Our patient derived models can be used to test the responsiveness of specific drug predicted with the in-silico drug analysis.