AI-powered histopathology classifiers identify transcriptomic biomarkers associated with cell cycle regulation and increased mitotic activity in poorly differentiated neuroendocrine carcinoma.

BACKGROUND: Neuroendocrine neoplasms (NENs) are a biologically heterogeneous group of epithelial malignancies arising from neuroendocrine cells across multiple organs. Although considered rare in comparison to other solid tumors, their incidence has increased more than six-fold in recent decades. A major clinical challenge is the accurate stratification between aggressive, poorly differentiated (PD) neuroendocrine carcinomas (NECs) and slow growing well-differentiated (WD) neuroendocrine tumors (NETs), which differ significantly in prognostic outcomes and treatment response. Conventional histopathology is often insufficient for reliable classification or determination of tumor origin, motivating the need for molecularly informed diagnostic approaches. METHODS: We performed transcriptomic profiling of 36 neuroendocrine neoplasms (NENs) including 21 well-differentiated neuroendocrine tumors (WD-NETs) and 15 poorly differentiated neuroendocrine carcinomas (PD-NECs) from 11 distinct anatomical sites. We developed an interpretable AI-based histology classifier achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.87-0.93. RESULTS: PD-NECs exhibited upregulation of genes involved in cell-cycle regulation and DNA damage response, including SFN, CHEK1, E2F1, CDC6, and TTK. In contrast, neuroendocrine lineage-associated genes such as CAMK2B and PAK3, which are preferentially expressed in neural tissues, were enriched in WD-NETs. AI-derived transcriptomic biomarkers distinguishing WD-NET and PD-NEC (PAK3 and EZH2, respectively) were orthogonally validated by immunohistochemistry. Our findings were further supported by independent validation in an publicly available NEN DNA methylation dataset. Moreover, DNA methylation-based mitotic age analysis demonstrated significantly higher mitotic activity and proliferative potential in lung NECs compared with lung NETs, consistent with our transcriptomic findings. Our analysis is publicly available at https://nen-pd-wd-hist.epigenomeocean.org/. CONCLUSIONS: This study establishes an integrative multi-omics framework for molecular classification of NENs that complements traditional histopathological assessment. By combining transcriptomic profiling, interpretable machine learning, and epigenetic validation, our approach enables improved tumor stratification, identification of mixed or misclassified cases and supports the development of biomarker-driven diagnostic and therapeutic strategies.