Integrating NECTIN4 Amplification With Membranous Nectin-4 Expression to Develop a Scoring System for Predicting Enfortumab Vedotin Response in Urothelial Carcinoma.
PURPOSE: Enfortumab vedotin (EV) is standard therapy for metastatic urothelial carcinoma (mUC), yet the predictive relevance of NECTIN4 expression-especially membranous versus cytoplasmic-remains unclear. Here, we sought to extend previous findings on NECTIN4 gene amplification in parallel with a systematic subcellular evaluation of NECTIN4 expression. EXPERIMENTAL DESIGN: We retrospectively analyzed 179 EV-treated mUC patients. NECTIN4 amplification was assessed by FISH and NECTIN4 protein levels by IHC. A four-tier membranous scoring algorithm (0,1+,2+,3+) adapted from CAP HER2 gastric guidelines was benchmarked against H-score. We integrated amplification status with membranous staining to refine predictive stratification and compared associations with objective response rate (ORR) to EV-301 data. RESULTS: Combining membranous and cytoplasmic compartments resulted in a median composite H-score of 260 (78.2% ≥150), closely matching NECTIN4 expression prevalence reported in EV-301 (median 250; 82.6% ≥ 150). A ≥150 cut-off enriched for EV responders in both cohorts; in EV-301 with ORR of 45.8% vs. 20% (P = 0.001). High membranous expression based on the scoring (2+/3+) predicted response (ORR 55.1% vs. 25.5%; P < 0.001), with longer PFS (7.1 vs. 2.9 months; HR 0.45) and OS (12.3 vs. 6.9 months; HR 0.57), whereas cytoplasmic expression lacked predictive value. NECTIN4-amplified tumors showed particularly favorable outcomes (PFS 12.2 months; OS 30.1 months). An integrated three-tier model-amplified, non-amplified/high-membranous, and non-amplified/low-membranous-yielded ORRs of 77.2%, 42.9%, and 26.1% and separated survival outcomes. CONCLUSIONS: Our NECTIN4 scoring system integrating NECTIN4 amplification with membranous NECTIN4 expression accurately predicts outcomes, supporting combined genomic and membranous assessment as complementary biomarkers for optimizing EV selection.