Carcinomas of the pancreatobiliary system confer an especially unfavorable prognosis. The differential diagnosis of intrahepatic cholangiocarcinoma (iCCA) and its subtypes versus liver metastasis of ductal adenocarcinoma of the pancreas (PDAC) is clinically important to allow the best possible therapy. We could previously show that E-cadherin and N-cadherin, transmembrane glycoproteins of adherens junctions, are characteristic features of hepatocytes and cholangiocytes. We therefore analyzed E-cadherin and N-cadherin in the embryonally related epithelia of the bile duct and pancreas, as well as in 312 iCCAs, 513 carcinomas of the extrahepatic bile ducts, 228 gallbladder carcinomas, 131 PDACs, and precursor lesions, with immunohistochemistry combined with image analysis, fluorescence microscopy, and immunoblots. In the physiological liver, N-cadherin colocalizes with E-cadherin in small intrahepatic bile ducts, whereas larger bile ducts and pancreatic ducts are positive for E-cadherin but contain decreasing amounts of N-cadherin. N-cadherin was highly expressed in most iCCAs, whereas in PDACs, N-cadherin was negative or only faintly expressed. E- and N-cadherin expression in tumors of the pancreaticobiliary tract recapitulate their expression in their normal tissue counterparts. N-cadherin is a helpful marker for the differential diagnosis between iCCA and PDAC, with a specificity of 96% and a sensitivity of 67% for small duct iCCAs and 50% for large duct iCCAs.

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Bitter taste receptors (T2Rs) are G protein-coupled receptors originally detected in the gustatory system. More recently, T2Rs have been shown to be expressed in extra-oral cells eliciting non-gustatory functions. Emerging evidence has suggested a potential role for T2R signaling in diverse pathophysiological conditions, including cancer. The aim of the present study was to evaluate the expression of T2R14 in pancreatic ductal adenocarcinoma (PDAC) and to assess its involvement in the anticancer effects induced by apigenin, a natural ligand of T2R14. For this purpose, T2R14 expression was explored in PDAC tumor tissue and tumor-derived cell lines. Using the cell lines expressing the highest levels of T2R14, its effects on chemoresponsiveness and migration upon activation with apigenin were investigated in vitro. To the best of our knowledge, the present study was the first to confirm the expression of the T2R family member T2R14 in PDAC. Patients with relatively high levels of T2R14 expression exhibited significantly prolonged overall survival compared with that of patients with low T2R14 expression. Furthermore, novel functions for apigenin were revealed; notably, apigenin was shown to elicit cytotoxic, anti-migratory and chemosensitizing effects to 5-fluoruracil (5-FU) and to 5-FU, leucovorin, irinotecan and oxaliplatin in pancreatic cancer cells. In conclusion, the present study extended the evidence for the anticancer effects of apigenin and strongly indicated the functional relevance of T2R14 in PDAC, even though their respective underlying pathways appear to be independent of each other.

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Introduction Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. Materials and methods In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non‐neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. Results Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. Conclusions Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.