The expression landscape and heterogeneity of nectin-4, HER2, and Trop-2 in urothelial carcinoma: clinicopathological correlations and implications for ADC-guided precision therapy

Background: Antibody-drug conjugates (ADCs) have redefined the treatment of urothelial carcinoma (UC). However, the spatiotemporal heterogeneity and co-expression landscapes of these targets across diverse anatomical sites and pathological stages remain poorly understood. This study aimed to map these patterns and explore their clinical implications for personalized ADC therapy. Methods: (CIS, n=8), and lymph node (LN) metastases (n=12). Expression levels were evaluated via immunohistochemistry (IHC). Statistical analyses included the Wilcoxon signed-rank test, Mann-Whitney U test, and multivariable logistic regression. A novel Δscore (tumor score - normal score) was introduced to quantify target "net gain." Results: High expression (IHC 2+ or higher) of at least one target occurred in 90.7 percent of patients, with 56.8 percent showing multi-target co-expression. HER2 and Trop-2 were significantly upregulated during the transition from CIS to invasive carcinoma (P = 0.034 and P = 0.039). While primary tumors and metastases showed global stability (P greater than 0.05), Nectin-4 tended to downregulate in metastases (negative ranks in 50 percent of pairs). Trop-2 expression was significantly higher in the lower than the upper urinary tract (P = 0.042), whereas HER2 and Nectin-4 were uniformly distributed. Furthermore, a high Δ-Trop-2 was independently associated with muscle invasion, lymphovascular invasion, and metastasis. Conclusions: In the "post-EV+P" era, our findings reveal significant antigenic drift and anatomical expression biases during disease progression, advocating for dynamic, multi-site molecular profiling over static primary-site assessment. This provides a translational blueprint to optimize ADC sequencing and overcome heterogeneity-driven resistance. Finally, we introduce the novel Δscore as a new metric for further testing in quantifying net antigenic gain and predicting aggressive phenotypes.