Whole-genome copy number and immunohistochemical analyses on surgically resected intracholecystic papillary neoplasms

Mucins 3A and 3B Are Expressed in the Epithelium of Human Large Airway

Aberrant mucus secretion is a hallmark of chronic obstructive pulmonary disease (COPD). Expression of the membrane-tethered mucins 3A and 3B (MUC3A, MUC3B) in human lung is largely unknown. In this observational cross-sectional study, we recruited subjects 45-65 years old from the general population of Stockholm, Sweden, during the years 2007-2011. Bronchial mucosal biopsies, bronchial brushings, and bronchoalveolar lavage fluid (BALF) were retrieved from COPD patients (n = 38), healthy never-smokers (n = 40), and smokers with normal lung function (n = 40). Protein expression of MUC3A and MUC3B in bronchial mucosal biopsies was assessed by immunohistochemical staining. In a subgroup of subjects (n = 28), MUC3A and MUC3B mRNAs were quantified in bronchial brushings using microarray. Non-parametric tests were used to perform correlation and group comparison analyses. A value of p < 0.05 was considered statistically significant. MUC3A and MUC3B immunohistochemical expression was localized to ciliated cells. MUC3B was also expressed in basal cells. MUC3A and MUC3B immunohistochemical expression was equal in all study groups but subjects with emphysema had higher MUC3A expression, compared to those without emphysema. Smokers had higher mRNA levels of MUC3A and MUC3B than non-smokers. MUC3A and MUC3B mRNA were higher in male subjects and correlated negatively with expiratory air flows. MUC3B mRNA correlated positively with total cell concentration and macrophage percentage, and negatively with CD4/CD8 T cell ratio in BALF. We concluded that MUC3A and MUC3B in large airways may be a marker of disease or may play a role in the pathophysiology of airway obstruction.

Molecular genetic characteristics of thymic epithelial tumors with distinct histological subtypes

BACKGROUND

Due to the low incidence and histological heterogeneity, the molecular features and underlying carcinogenic mechanisms of thymic epithelial tumors (TETs) are yet to be fully elucidated, especially for different subtypes of TETs.

METHODS

Tumor tissue samples of 43 TETs with distinct histological subtypes were collected. We analyzed the molecular characteristics in different subtypes based on whole exome sequencing data.

RESULTS

The mutational profiles of the different subtypes of TETs varied. Compared with thymomas, thymic carcinomas (TCs) had a higher mutation frequency of MYO16 (33% vs. 3%, p = 0.024) and a lower frequency of ZNF729 mutations (0% vs. 35%, p = 0.044). No significant difference was observed in the median tumor mutation burden across different subtypes. The value of copy number variation burden, weighted genome instability index, and the number of amplified segments were all higher in TCs than thymomas, and they also tended to be higher in B3 thymoma than in non-B3 thymomas, while they had no significant differences between B3 thymoma and TCs. Clustering analyses revealed that Wnt, MAPK, Hedgehog, AMPK, and cell junction assembly signaling pathways were exclusively enriched in non-B3 thymomas, lysine degradation pathway in B3 thymoma, and extracellular matrix-receptor (ECM-receptor) interaction, positive regulation of cell cycle process, and activation of innate immune response pathways in TCs.

CONCLUSIONS

This study revealed distinct molecular landscapes of different subtypes of TETs, suggesting diverse pathogenesis of non-B3 thymomas, B3 thymomas, and TCs. Our findings warrant further validation in future large-scale studies and may provide a theoretical basis for potential personalized therapeutic strategies.

Understanding the Clinical Significance of MUC5AC in Biliary Tract Cancers

Biliary tract cancers (BTC) arise from biliary epithelium and include cholangiocarcinomas or CCA (including intrahepatic (ICC) and extrahepatic (ECC)) and gallbladder cancers (GBC). They often have poor outcomes owing to limited treatment options, advanced presentations, frequent recurrence, and poor response to available systemic therapy. Mucin 5AC (MUC5AC) is rarely expressed in normal biliary epithelium, but can be upregulated in tissues of benign biliary disease, premalignant conditions (e.g., biliary intraepithelial neoplasia), and BTCs. This mucin's numerous glycoforms can be divided into less-glycosylated immature and heavily-glycosylated mature forms. Reported MUC5AC tissue expression in BTC varies widely, with some associations based on cancer location (e.g., perihilar vs. peripheral ICC). Study methods were variable regarding cancer subtypes, expression positivity thresholds, and MUC5AC glycoforms. MUC5AC can be detected in serum of BTC patients at high concentrations. The hesitancy in developing MUC5AC into a clinically useful biomarker in BTC management is due to variable evidence on the diagnostic and prognostic value. Concrete conclusions on tissue MUC5AC are difficult, but serum detection might be relevant for diagnosis and is associated with poor prognosis. Future studies are needed to further the understanding of the potential clinical value of MUC5AC in BTC, especially regarding predictive and therapeutic value.

Molecular regulation after mucosal injury and regeneration in ulcerative colitis

Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease with a complex etiology. Intestinal mucosal injury is an important pathological change in individuals with UC. Leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5+) intestinal stem cells (ISCs) exhibit self-renewal and high differentiation potential and play important roles in the repair of intestinal mucosal injury. Moreover, LGR5+ ISCs are intricately regulated by both the Wnt/β-catenin and Notch signaling pathways, which jointly maintain the function of LGR5+ ISCs. Combination therapy targeting multiple signaling pathways and transplantation of LGR5+ ISCs may lead to the development of new clinical therapies for UC.

Natural history of intracholecystic papillary neoplasm (ICPN): a rare case of ICPN whose natural history was closely followed by ultrasound

The natural history of intracholecystic papillary neoplasm (ICPN), especially the speed of growth from small benign to a carcinomatous lesion, is quite unrevealed. Here, we report an extremely rare case of ICPN, in which the papillary lesion was observed transforming from small and benign to malignant using abdominal ultrasound (AUS) over 2 years during routine health checks. A 44-year-old man underwent a routine health check-up. The initial AUS showed a small sessile polyp in the gallbladder, which enlarged slightly at the next AUS, a year later. In the third year, the polypoid lesion enlarged markedly, with a maximum diameter of 10 × 9 × 7 mm. Therefore, a laparoscopic cholecystectomy was performed. Microscopically, the 10 mm tumor had intracytoplasmic mucus, and a clear cytoplasm compatible with gastric-type features. Immunohistochemical analysis showed positive staining of atypical cells for MUC6 and PAS. These findings led to the diagnosis of ICPN with high-grade intraepithelial neoplasia of the gastric type. In conclusion, sessile polyps with rapid growth might be a crucial finding in the early stage of ICPN.

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients' overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune-Oncology Panel

Simple Summary

This research predicted the overall survival of patients and cell-of-origin molecular subtypes of diffuse large B-cell lymphoma from Tokai University using gene expression data. A pancancer immune profiling panel was analyzed using artificial neural networks, and high accuracy of prediction was found. Additionally, the results were explained with other machine learning techniques and conventional bioinformatics analyses.

Abstract

Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune-oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis, ARG1, TNFSF12, REL, and NRP1 correlated with favorable survival (hazard risks: 0.3–0.5), and IFNA8, CASP1, and CTSG, with poor survival (hazard risks = 1.0–2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (CD163), and MYD88 expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and MYC translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1). STAT6, TREM2, and REL were associated with the germinal center B-cell (GCB) subtype, and CD37, GNLY, CD46, and IL17B were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of MAPK3 with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.