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T. Taha

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Review Open access Jul 2026

DLL3-Targeted Strategies in Advanced Prostate Cancer: Current Evidence and Future Perspectives

Inhibition of androgen receptor (AR) signaling remains the cornerstone of systemic therapy for advanced prostate cancer (PC). However, a subset of aggressive tumors either arises de novo with neuroendocrine features or emerges under treatment pressure through lineage plasticity and AR independence. These lethal states are encompassed within the spectrum of aggressive-variant prostate cancer (AVPC), an umbrella term that includes both histologically confirmed neuroendocrine prostate cancer (NEPC)—comprising de novo NEPC and treatment-emergent NEPC (t-NEPC)—and clinically or molecularly defined AVPC lacking histologic confirmation but sharing neuroendocrine-like, AR-indifferent, or small-cell features. These phenotypes are characterized by rapid progression, visceral dissemination, low or discordant prostate-specific antigen (PSA) levels relative to tumor burden, and poor prognosis. Treatment options for NEPC/AVPC remain limited and largely rely on platinum-based chemotherapy, which usually provides only modest and transient benefit. This unmet need has intensified interest in lineage-associated vulnerabilities. Delta-like ligand 3 (DLL3), an inhibitory Notch ligand with restricted expression in normal adult tissues, is aberrantly upregulated in several neuroendocrine malignancies and has emerged as a clinically actionable target. In prostate cancer, DLL3 expression is enriched in neuroendocrine tumor cells, being detected in approximately 76.6% of castration-resistant NEPC compared with only 12.5% of castration-resistant adenocarcinoma, supporting its development as both a biomarker and therapeutic vulnerability. Clinical success of DLL3-targeted therapies in small-cell lung cancer further supports evaluation of DLL3-directed strategies in NEPC and related AVPC states. This review summarizes the biological rationale, translational evidence, and emerging clinical data supporting DLL3-targeted therapies in prostate cancer. Investigational platforms include antibody-drug conjugates, bispecific and trispecific T-cell engagers, and DLL3-directed radiopharmaceuticals. Early clinical studies suggest that activity is largely confined to DLL3-expressing neuroendocrine tumors, highlighting the importance of biomarker-guided patient selection. Delta-like ligand 3-directed therapies may reshape the management of DLL3-expressing prostate cancer if ongoing efforts to refine biomarkers improve patient enrichment and optimize trial design are successfully translated into clinical practice.

Giovanna Pecoraro, A. De Giorgi, G. Montelatici et al. · 0 citations
#explainable ai Sep 2026

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.

BACKGROUND Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs). Early identification of high-risk patients is needed. This proof-of-concept study evaluated whether machine learning (ML) with explainable AI (SHAP) could predict EV toxicities using real-world data. RESEARCH DESIGN AND METHODS Data from 542 patients (51 centers, 24 countries) were analyzed. Six outcomes were predicted including grade 3-4 AEs. Four ML algorithms were trained on an 80% split and tested on 20%. Performance was evaluated via standard metrics with SHAP for interpretability. RESULTS In this exploratory analyses, Random Forest achieved highest overall performance, yielding best AUC for diarrhea, severe AEs, and dose skipping. XGBoost led for cutaneous toxicity and diabetes; LASSO led for neuropathy (differences modest). Age was the most important associated variable, followed by prior immunotherapy and ECOG status. Liver metastases influenced diabetes and cutaneous toxicity; lung metastases impacted diarrhea, neuropathy, and skin toxicity. SHAP showed atezolizumab/nivolumab linked to lower cutaneous risk, and female sex to higher risk. CONCLUSION These preliminary, hypothesis-generating findings suggest ML may predict EV-related toxicities, but single train-test split, small event counts, and lack of external validation preclude clinical use. Prospective validation is essential.

K. Sridharan, Mattia Alberto Di Civita, G. Sivaramakrishnan et al. · 0 citations

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