Pathology International最新文献

Immunohistochemistry is primarily employed to visualize the localization of specific molecules in tissue samples. However, there is an increasing need for software-assisted quantitative assessment. In the present study, we performed inverted blue channel-based pseudoimmunofluorescence image analysis using original immunohistochemistry images. In human esophageal squamous cell carcinoma tissues, various humoral factors promote the phosphorylation of signaling proteins, including protein kinase B (Akt) and/or extracellular signal-regulated kinase 1/2 (ERK1/2), leading to tumor progression. Our method demonstrated applicability in the analysis of localized signaling proteins in histological sections. Relatively high phosphorylated Akt (p-Akt) intensity was observed in the cancer-stroma adjacent (Adj) and noncancerous regions of the superficial layer (SL). Furthermore, localized phosphorylated ERK1/2 (Thr202/Tyr204) was observed in the Adj of the SL and invasive front, distinct from the pattern of p-Akt (Ser473) and p-Akt (Thr308). In conclusion, pseudoimmunofluorescent immunohistochemistry image analysis is useful for the quantitative assessment and objective interpretation of localized signaling proteins in esophageal squamous cell carcinoma. The method can also be applied to analyze various immunohistochemistry images from diverse tissues.

A woman in her 60s with rheumatoid arthritis was admitted with fever and abdominal pain. Laparoscopic examination with the differential diagnosis of peritoneal neoplasm and infection revealed granulomatous phlebitis in the resected greater omentum. Amorphous eosinophilic deposits observed in the resected tissue exhibited focal, weak positivity for Congo red but were strongly positive for thioflavin S, confirming their focal amyloid properties. Marked degeneration of elastic fibers was also evident. Electron microscopy revealed deposits around the affected elastic fibers. Immunohistochemistry revealed the deposition of epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1) along with T-cell-predominant lymphocytic inflammation. The definitive diagnosis was granulomatous enterocolic lymphocytic phlebitis (ELP) associated with EFEMP1 deposition exhibiting focal amyloid properties (EFEMP1/AEFEMP1), supported by proteomics analysis. This type of vasculitis is similar to amyloid-β-related angiitis of the central nervous system. Thus, we speculate that granulomatous ELP also results from an immune response that recognizes EFEMP1/AEFEMP1 deposits as foreign material and attempts to remove them. Confirmation of EFEMP1/AEFEMP1 deposition with Congo red staining is challenging, particularly in the presence of inflammation, and warrants comprehensive evaluation.

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.