Identifying DNA methylation biomarkers for non-endoscopic detection of Barrett’s esophagus

We report a biomarker-based non-endoscopic method for detecting Barrett’s esophagus (BE), based on detecting methylated DNAs retrieved via a swallowable balloon-based esophageal sampling device. BE is the precursor of, and a major recognized risk factor for, developing esophageal adenocarcinoma (EAC). Endoscopy, the current standard for BE detection, is not cost-effective for population screening. We performed genome-wide screening to ascertain regions targeted for recurrent aberrant cytosine methylation in BE, identifying high-frequency methylation within the CCNA1 locus. We tested CCNA1 DNA methylation as a BE biomarker in cytology brushings of the distal esophagus from 173 individuals with or without BE. CCNA1 DNA methylation demonstrated an area under the curve (AUC)=0.95 for discriminating BE-related metaplasia and neoplasia cases versus normal individuals, performing identically to methylation of VIM DNA, an established BE biomarker. When combined, the resulting two biomarker panel was 95% sensitive and 91% specific. These results were replicated in an independent validation cohort of 149 individuals, who were assayed using the same cutoff values for test positivity established in the training population. To progress toward non-endoscopic esophageal screening, we engineered a well-tolerated, swallowable, encapsulated balloon device able to selectively sample the distal esophagus within 5 minutes. In balloon samples from 86 individuals, tests of CCNA1 plus VIM DNA methylation detected BE metaplasia with 90.3% sensitivity and 91.7% specificity. Combining the balloon sampling device with molecular assays of CCNA1 plus VIM DNA methylation enables an efficient, well-tolerated, sensitive, and specific method of screening at-risk populations for BE

A reassessment of primary thyroid lymphoma: high-grade MALT-type lymphoma as a distinct subtype of diffuse large B-cell lymphoma

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73362/1/j.1365-2559.2000.00941.x.pd

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

Barrett’s esophagus (BE) is often asymptomatic and only a small portion of BE patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with BE. Familial aggregation of BE and esophageal adenocarcinoma (EAC), and the increased risk of EAC for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well-developed

RNA Sequencing Identifies Transcriptionally Viable Gene Fusions in Esophageal Adenocarcinomas

Esophageal adenocarcinoma (EAC) is a deadly cancer with increasing incidence in the U.S., but mechanisms underlying pathogenesis are still mostly elusive. In addressing this question, we assessed gene-fusion landscapes by comprehensive RNA sequencing (RNAseq) of 55 pre-treatment EAC and 49 non-malignant biopsy tissues from patients undergoing endoscopy for Barrett’s esophagus. In this cohort, we identified 21 novel candidate EAC-associated fusions occurring in 3.33%-11.67% of EACs. Two candidate fusions were selected for validation by PCR and Sanger sequencing in an independent set of pre-treatment EAC (N=115) and non-malignant (N=183) biopsy tissues. In particular, we observed RPS6KB1–VMP1 gene fusion as a recurrent event occurring in ~10% of EAC cases. Notably, EAC cases harboring RPS6KB1–VMP1 fusions exhibited significantly poorer overall survival as compared to fusion-negative cases. Mechanistic investigations established that the RPS6KB1–VMP1 transcript coded for a fusion protein which significantly enhanced the growth rate of non-dysplastic Barrett’s esophagus cells. Compared to the wild-type VMP1 protein, which mediates normal cellular autophagy, RPS6KB1–VMP1 fusion exhibited aberrant subcellular localization and was relatively ineffective in triggering autophagy. Overall, our findings identified RPS6KB1–VMP1 as a genetic fusion that promotes EAC by modulating autophagy-related processes, offering new insights into the molecular pathogenesis of esophageal adenocarcinomas

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction