Abstract
The treatment of metastatic urothelial cancer (mUC) has been transformed by recent progress in clinical trials and drug development. There are now three therapeutic classes with proven benefits in mUC: chemotherapy, immunotherapy, and targeted therapy. The optimal sequence and combination of these classes remain to be defined. Biomarker development is essential to guide treatment selection at each therapeutic juncture. Two biomarkers, programmed death-ligand 1 expression and fibroblast growth factor receptor alterations, have been incorporated into the mUC treatment paradigm thus far. This review discusses predictive biomarkers in development and their potential to influence mUC treatment selection moving forward.
Highlights
For 30 years, cisplatin-based combination chemotherapy was the only treatment to demonstrate a meaningful overall survival (OS) benefit in metastatic urothelial cancer[1]
This sparse treatment landscape has been transformed by contemporary trials demonstrating OS benefits from anti-PD-1/ programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) and the antibody-drug conjugate (ADC) enfortumab vedotin (EV)[2,3,4]
Biomarker development has lagged in therapeutic development, with only PD-L1 expression and fibroblast growth factor receptor (FGFR) alterations playing a routine role in treatment selection
Summary
For 30 years, cisplatin-based combination chemotherapy was the only treatment to demonstrate a meaningful overall survival (OS) benefit in metastatic urothelial cancer (mUC)[1]. Two ongoing phase II studies are testing biomarker-guided bladder-preservation strategies in MIBC treated with NAC (NCT03609216, NCT02710734) In these trials, patients with DDR mutations (e.g., ATM, RB1, FANCC, ERCC2) with complete clinical responses after neoadjuvant cisplatin-based chemotherapy can be actively monitored instead of undergoing cystectomy. A gene expression signature derived from these novel CD4 T cells was applied to bulk RNA sequencing data from the IMvigor210 study and was predictive of atezolizumab response among patients with an inflamed tumor microenvironment In another example combining bulk and single-cell analyses to dissect the UC TME, bulk gene expression data from two clinical trials were first used to identify two signatures associated with ICI response and resistance[136]. Their pleiotropic effects through multiple pathways may mask the patient subsets who derive the most benefit, making biomarker analyses even more essential
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