BACKGROUND: Tumor heterogeneity has been recognized in many cancer types for decades. However, the significance of tumor heterogeneity on disease course and clinical outcome in bladder cancer is of more recent interest to researchers and clinicians. This is especially true as morphologic and molecular heterogeneity has the potential to confound accurate diagnosis, efficient prognostication, and subsequent clinical management. While this is true, it is not always clear what laboratory models are available or suitable for the study of these important clinical phenomena. OBJECTIVE: To review in vitro and in vivo laboratory models for the study of morphologic and molecular tumor heterogeneity in bladder cancer. METHODS: We undertook a review of PubMed with a focus on identifying suitable models for the study of tumor heterogeneity in bladder cancer. RESULTS: We provide a review of common in vivo (genetically engineered mice and patient-derived xenografts) and in vitro (established cell lines and organoid systems) models and discuss their utility in the study of morphologic and molecular tumor heterogeneity in bladder cancer. CONCLUSION: Genetically engineered mouse models and patient-derived xenografts provide complementary approaches for the study of tumor heterogeneity in bladder cancer. In addition, cell culture-based systems provide a system amenable to genetic manipulation and mechanistic studies, while organoid systems bridge the gap between in vivo and in vitro systems. However, the availability of models to study molecular heterogeneity is limited, partly because of a relative lack of molecular characterization of available models. In summary, while models for the study of specific subsets of morphologic heterogeneity are available, more models are required for studies of molecular heterogeneity. This shortcoming could be partially addressed by more comprehensively characterizing currently available model systems. In addition, each system/approach has advantages and disadvantages, and care should be taken when selecting a given model.
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