Immunohistochemical studies on formalin-fixed, paraffin-embedded (FFPE) tissue utilizing polyclonal antibodies form the cornerstone of many reports claiming to demonstrate erythropoietin receptor (EPOR) expression in malignant tissue. Recently, Elliott et al. (Blood 2006;107:1892–1895) reported that the antibodies commonly used to detect EPOR expression also detect non-EPOR proteins, and that their binding to EPOR was severely abrogated by two synthetic peptides based on the sequence of heat shock protein (HSP) 70, HSP70-2, and HSP70-5. We have investigated the specificity of the C20 antibody for detecting EPOR expression in non-small cell lung carcinoma (NSCLC) utilizing tissue microarrays. A total of 34 cases were available for study. Antibody absorbed with peptide resulted in marked suppression of cytoplasmic staining compared with nonabsorbed antibody. Four tumors that initially showed a membranous pattern of staining retained this pattern with absorbed antibody. Positive membranous immunoreactivity was also observed in 6 of 30 tumors that originally showed a predominantly cytoplasmic pattern of staining. Using the C20 antibody for Western blots, we detected three main bands, at 100, 66, and 59 kDa. Preincubation with either peptide caused abolition of the 66-kDa band, which contains non-EPOR sequences including heat shock peptides. These results call into question the significance of previous immunohistochemical studies of EPOR expression in malignancy and emphasize the need for more specific anti-EPOR antibodies to define the true extent of EPOR expression in neoplastic tissue.

Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.

Molecular biomarkers have come to constitute one of the cornerstones of oncological pathology. The method of classification not only directly affects the manner in which patients are diagnosed and treated, but also guides the development of drugs and of artificial intelligence tools. The aim of this article is to organise and update gastrointestinal molecular biomarkers in order to produce an easy‐to‐use guide for routine diagnostics. For this purpose, we have extracted and reorganised the molecular information on epithelial neoplasms included in the 2019 World Health Organization classification of tumours. Digestive system tumours, 5th edn.

Background: Ubiquitin Carboxyl-Terminal Hydrolase-L1 (UCH-L1) is a deubiquitinating enzyme that is highly expressed throughout the central and peripheral nervous system and in cells of the diffuse neuroendocrine system. Aberrant function of UCH-L1 has been associated with neurological disorders such as Parkinson's disease and Alzheimer's disease. Moreover, UCH-L1 exhibits a variable expression pattern in cancer, acting either as a tumour suppressor or promoter, depending on the type of cancer. In non-small cell lung carcinoma primary tumour samples, UCH-L1 is highly expressed and is associated with an advanced tumour stage. This suggests UCH-L1 may be involved in oncogenic transformation and tumour invasion in NSCLC. However, the functional significance of UCH-L1 in the progression of NSCLC is unclear. The aim of this study was to investigate the role of UCH-L1 using NSCLC cell line models and to determine if it is clinically relevant as a prognostic marker for advanced stage disease. Methods: UCH-L1 expression in NSCLC cell lines H838 and H157 was modulated by siRNA-knockdown, and the phenotypic changes were assessed by flow cytometry, haematoxylin & eosin (H&E) staining and poly (ADP-ribose) polymerase (PARP) cleavage. Metastatic potential was measured by the presence of phosphorylated myosin light chain (MLC2). Tumour microarrays were examined immunohistochemically for UCH-L1 expression. Kaplan-Meier curves were generated using UCH-L1 expression levels and patient survival data extracted from Gene Expression Omnibus data files. Results: Expression of UCH-L1 was decreased by siRNA in both cell lines, resulting in increased cell death in H838 adenocarcinoma cells but not in the H157 squamous cell line. However, metastatic potential was reduced in H157 cells. Immunohistochemical staining of UCH-L1 in patient tumours confirmed it was preferentially expressed in squamous cell carcinoma rather than adenocarcinoma. However the Kaplan-Meier curves generated showed no correlation between UCH-L1 expression levels and patient outcome. Conclusions: Although UCH-L1 appears to be involved in carcinogenic processes in NSCLC cell lines, the absence of correlation with patient survival indicates that caution is required in the use of UCH-L1 as a potential prognostic marker for advanced stage and metastasis in lung carcinoma.