Neuroendocrine neoplasms of the pancreatobiliary tract and liver are a heterogeneous group that encompass a spectrum of entities with distinct morphological, biological and clinical features. Although in the various anatomical sub-sites of this region they show specific characteristics, these tumors, as a whole, share several etiological and clinical aspects. This review systematically addresses NENs arising in the extrahepatic bile ducts, gallbladder, liver and pancreas, with the principal aim of pinpointing essential diagnostic and classification issues. In addition, the section on hepatic NENs has been expanded to include metastatic disease of unknown primary site.

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

Castleman disease (CD) is a rare lymphoproliferative disorder that includes various clinico-pathological subtypes. According to clinical course, CD is divided into unicentric CD (UCD) and multicentric CD (MCD). MCD is further distinguished based on the etiological driver in herpes virus-8-related MCD (that can occur in the setting of HIV); in MCD associated with POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes); and idiopathic MCD (iMCD). The latter can also be divided in iMCD-TAFRO (thrombocytopenia, anasarca, fever, myelofibrosis, organomegaly) and iMCD not otherwise specified. To date, CD pathogenesis is still uncertain, but CD may represent the histological and clinical result of heterogeneous pathomechanisms. Transcriptome investigations in CD lymph nodes have documented the expression and up-regulation of different cytokines; furthermore, few recent studies have shown alterations of different T-cell subsets in CD patients, suggesting a possible role of the nodal microenvironment in CD development. On this basis, our study aimed to investigate the distribution of T-cell subsets in the clinico-pathological spectrum of CD. We evaluated the CD4/CD8 ratio and the number of T-regulatory (T-reg) FOXP3+ cells in 28 CD cases. In total, 32% of cases showed a decreased CD4/CD8 ratio due to increased CD8+ T-cells, including both UCD, iMCD, and HHV8+ MCD cases. The T-reg subset analysis revealed a statistically significant (p < 0.0001) lower mean number of FOXP3+ T-reg cells in CD cases when compared with non-specific reactive lymph nodes. We did not find statistically significant differences in T-reg numbers between the different CD subtypes. These findings may suggest that alterations in T-cell subpopulations that can lead to disruption of immune system control may contribute to the numerous changes in different cellular compartments that characterize CD.

Well-differentiated neuroendocrine tumors, grade 3 (NETs G3) of the digestive system are high-grade neuroendocrine neoplasms characterized by well-differentiated neuroendocrine morphology and high proliferative activity. Their molecular identity and therapeutic vulnerabilities remain incompletely defined. We performed a transcriptomic analysis and a comprehensive genomic profiling of 40 cases, including 26 NETs G3, 8 NETs G1/G2, and 6 neuroendocrine carcinomas (NECs). Targeted sequencing assessed alterations at both the DNA and RNA level, and gene expression profiling was conducted using a targeted immune-oncology panel. NETs G3 retained recurrent alterations in chromatin-regulatory genes characteristic of well-differentiated tumors and largely lacked the consistent TP53 and RB1 inactivation typical of NECs. Transcriptomic and genomic analysis demonstrated separation of NECs from NETs, with NETs G3 partially overlapping with NETs G1/G2 supporting a model of molecular relatedness. However, when compared with NETs G1/G2, NETs G3 showed upregulation of extracellular matrix remodeling, metabolic reprogramming, and cytokine signaling pathways. NETs G3 were heterogeneous in terms of number of genomic alterations per case and involved genes. A distinctive genomic feature was the predominance of large-segment copy-number losses, frequently involving chromatin-regulatory regions on chromosomes X and 10. Potentially targetable alterations were detected in up to 25% of NETs G3. Analysis of immune microenvironment supported a poorly inflamed tumor status, with individual cases harboring microsatellite instability or enrichment in tumor-infiltrating lymphocytes potentially amenable to immunotherapy.