Abstract PR005: Conditional Reprogramming Enables Rapid Establishment of Patient-Derived Neuroendocrine Tumor Models for Functional Precision Oncology
TL;DR
The findings demonstrate that conditional reprogramming provides a robust and scalable approach to generate clinically relevant NET models, enabling functional precision oncology, biomarker discovery, and therapeutic optimization, and may facilitate rapid clinical decision-making and accelerate the development of targeted strategies for patients with neuroendocrine malignancies.
Abstract
Neuroendocrine tumors (NETs), including aggressive subtypes such as neuroendocrine prostate cancer (NEPC) and small cell carcinomas, are characterized by marked heterogeneity, therapeutic resistance, and limited availability of clinically relevant models. The development of patient-derived systems that faithfully recapitulate tumor biology remains a major barrier to advancing precision oncology in this disease. Here, we applied conditional reprogramming (CR) technology to establish patient-derived neuroendocrine tumor cell cultures (CR-NETs) from primary and metastatic specimens. Using co-culture with irradiated feeder cells and ROCK inhibition, CR-NETs were rapidly expanded from small clinical samples, including biopsies and effusions, with high efficiency and short turnaround time. Comprehensive characterization demonstrated that CR-NETs retain key neuroendocrine features, including expression of canonical markers (e.g., CHGA, SYP), lineage plasticity signatures, and patient-specific genomic alterations. Functional assays revealed preserved tumor heterogeneity and differential responses to standard-of-care agents and targeted therapies. Notably, CR-NETs exhibited distinct vulnerabilities in epigenetic regulation and metabolic pathways, consistent with neuroendocrine differentiation states. Importantly, CR-NETs enabled real-time drug sensitivity testing, identifying patient-specific therapeutic susceptibilities. Collectively, our findings demonstrate that conditional reprogramming provides a robust and scalable approach to generate clinically relevant NET models, enabling functional precision oncology, biomarker discovery, and therapeutic optimization. This platform may facilitate rapid clinical decision-making and accelerate the development of targeted strategies for patients with neuroendocrine malignancies. Jenny Li, Akshay Sood, Debasish Sundi, Timothy Gauntner, Shang-Jui Wang, Lingbin Meng, Qingqing Wu, Peng Wang, Eric Singer, Anil V. Parwani, Cheryl Lee, Xuefeng Liu. Conditional Reprogramming Enables Rapid Establishment of Patient-Derived Neuroendocrine Tumor Models for Functional Precision Oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr PR005.