Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision-making. However, rendering a correct diagnosis can be challenging and often requires the integration of clinical, radiological, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma (iCCA) from colorectal liver metastasis (CRM) as the most frequent primary and secondary forms of liver adenocarcinoma with clinical-grade accuracy using hematoxylin and eosin-stained whole-slide images. HEPNET was trained on 714 589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital. On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve (AUROC) of 0.994 (95% CI 0.989-1.000) and an accuracy of 96.522% (95% CI 94.521-98.694%) at the patient level. Validation on the external test set yielded an AUROC of 0.997 (95% CI 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI 96.907-100.000%). HEPNET surpassed the performance of six pathology experts with different levels of experience in a reader study of 50 patients (P=.0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses. Here, we provide a ready-to-use tool with a clinical-grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings.

The protein tyrosine phosphatase interacting protein 51 (PTPIP51) regulates and interconnects signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway and an abundance of different others, e.g., Akt signaling, NF-κB signaling, and the communication between different cell organelles. PTPIP51 acts as a scaffold protein for signaling proteins, e.g., Raf-1, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (Her2), as well as for other scaffold proteins, e.g., 14-3-3 proteins. These interactions are governed by the phosphorylation of serine and tyrosine residues of PTPIP51. The phosphorylation status is finely tuned by receptor tyrosine kinases (EGFR, Her2), non-receptor tyrosine kinases (c-Src) and the phosphatase protein tyrosine phosphatase 1B (PTP1B). This review addresses various diseases which display at least one alteration in these enzymes regulating PTPIP51-interactions. The objective of this review is to summarize the knowledge of the MAPK-related interactome of PTPIP51 for several tumor entities and metabolic disorders.

The present study shows a new connection of protein tyrosine phosphatase interacting protein 51 (PTPIP51) to the nuclear factor κB (NFκB) signalling pathway. PTPIP51 mRNA and protein expression is regulated by RelA. If bound to the PTPIP51 promoter, RelA repress the mRNA and protein expression of PTPIP51. The parallel treatment with pyrrolidine dithiocarbamate (PDTC) reversed the suppression of PTPIP51 protein expression induced by TNFα. Using the intensity correlation analysis PTPIP51 verified a co-localization with RelA, which is also regulated by TNFα administration. Moreover, the direct interaction of PTPIP51 and RelA was established using the DuoLink proximity ligation assay. IκBα, the known inhibitor of RelA, also interacted with PTPIP51. This hints to the fact that in un-stimulated conditions PTPIP51 forms a complex with RelA and IκBα. The PTPIP51/RelA/IκBα complex is modulated by TNFα. Interestingly, the impact on the mitogen activated protein kinase pathway was negligible except in highest TNFα concentration. Here, PTPIP51 and Raf-1 interactions were slightly repressed. The newly established relationship of PTPIP51 and the NFκB signaling pathway provides the basis for a possible therapeutic impact.