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Urothelial carcinomas (UC) arise from the urothelium that covers the proximal urethra, urinary bladder, and the upper urinary tract. In daily routine and clinical trials UC originating from different locations are often treated and investigated in the same manner. However, differences between the two locations seem to be apparent and may question in handling them as a single oncologic entity. In this review we discuss similarities and differences between bladder and upper urinary tract UC and consider their potential impact on treatment strategies. Despite similarities of UC in the bladder (BC) and the upper urinary tract (UTUC), clinicopathologic and molecular differences may question to generally assemble both as a single tumor entity. Treatment standards for UTUC are often adopted from BC. However, a specific investigation in the former may still be meaningful as shown by the example of adjuvant cisplatin based chemotherapy. In conclusion, future investigations should prioritize the understanding of the tumor biology of both BC and UTUC. This may reveal which UTUC can be treated according to treatment standards of BC and in which cases, a separate approach may be more appropriate.

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Improved and cheaper molecular diagnostics allow the shift from “one size fits all” therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients—including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

No abstract