Pancreatic neuroendocrine tumors (NETs) are rare well-differentiated neoplasms with limited therapeutic options and unknown cells of origin. The current classification of pancreatic neuroendocrine tumors is based on proliferative grading, and guides therapeutic strategies, however, tumors within grades exhibit profound heterogeneity in clinical manifestation and outcome. Manifold studies have highlighted intra-patient differences in tumors at the genetic and transcriptomic levels. Molecular classification might become an alternative or complementary basis for treatment decisions and reflect tumor biology, actionable cellular processes. Here, we provide a comprehensive review of genomic, transcriptomic, proteomic and epigenomic studies of pancreatic NETs to elucidate patterns shared between proposed subtypes that could form a foundation for new classification. We denote four NET subtypes with distinct molecular features, which were consistently reproduced using various omics technologies.

Background: Neuroendocrine neoplasms are a rare and heterogeneous group of neoplasms. Small-sized (≤2 cm) pancreatic neuroendocrine tumors (PanNETs) are of particular interest as they are often associated with aggressive behavior, with no specific prognostic or progression markers. Methods: This article describes a clinical case characterized by a progressive growth of nonfunctional PanNET requiring surgical treatment in a patient with a germline FANCD2 mutation, previously not reported in PanNETs. The patient underwent whole exome sequencing and single-cell RNA sequencing. Results: The patient underwent surgical treatment. We confirmed the presence of the germline mutation FANCD2 and also detected the germline mutation WNT10A. The cellular composition of the PanNET was analyzed using single-cell sequencing, and the main cell clusters were identified. We analyzed the tumor genomics, and used the data to define the effect the germline FANCD2 mutation had. Conclusions: Analysis of the mutational status of patients with PanNET may provide additional data that may influence treatment tactics, refine the plan for monitoring such patients, and provide more information about the pathogenesis of PanNET. PanNET research using scRNA-seq data may help in predicting the effect of therapy on neuroendocrine cells with FANCD2 mutations.