Predicting outcomes in non-muscle invasive (Ta/T1) bladder cancer: the role of molecular grade based on luminal/basal phenotype.

Bladder cancer tumors can be divided into two molecular subtypes referred to as luminal or basal. Each subtype may react differently to current chemotherapy or immunotherapy. Likewise, the technology required for comprehensive molecular analysis is expensive and not yet applicable for routine clinical diagnostics. Therefore, it has been suggested that the immunohistochemical expressions of only two markers, luminal (CK20+, CK5/6-) and basal (CK5/6+, CK20-), is sufficient to identify the molecular subtypes of bladder cancer. This would represent a molecular grade that could be used in daily practice. Molecular classification is done using immunohistochemistry to assess luminal-basal phenotype based on tissular expression of CK20 and CK5/6 as surrogate for luminal or basal subtypes, respectively. A series of 147 non-muscle-invasive bladder carcinoma cases was selected, and the tumors were divided into four subgroups based on the presence of CK20 and/or CK5/6, that is, null (CK20-, CK5/6-), mixed (CK20+, CK5/6+), basal (CK20-, CK5/6+), and luminal (CK20+, CK5/6-) categories. Survival analysis was estimated using the Kaplan-Meier method and the log-rank test. Hazard ratios were calculated by Cox multivariate analysis. The molecular grade included cases with null (n = 89), mixed (n = 6), basal (n = 20), and luminal (n = 32) phenotypes with differences in recurrence-free, progression-free and cancer-specific survival associated with molecular-grade categories in patients with low- or high-grade Ta, or high-grade T1 tumors. The multivariate analysis identified the luminal phenotype as a predictor of more aggressive neoplasms. Our findings provide a rationale to investigate luminal and basal subtypes of bladder cancer using two gene expression signatures as surrogate markers and show that non-muscle-invasive bladder carcinoma can be stratified into biologically and clinically different subgroups by using an immunohistochemical classifier.