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Gene expression profiling of Barrett’s esophageal tissues and esophageal adenocarcinoma specimens

ABSTRACT: Specimens were collected from esophageal adenocarcinoma patients undergoing esophagectomy for adenocarcinoma at the University of Michigan Health System between 1991 and 2004. Written consent was obtained from each patient according to the approval and guidelines of the University of Michigan institutional review board. Patients receiving treatment with chemotherapy and/or radiotherapy prior to surgery were excluded. Tissue samples were fresh-frozen in liquid nitrogen and stored at −80 °C until use. Cellularity of metaplastic, dysplastic, and tumor samples were assured to be greater than 70% before sample RNA was isolated. RNA from 31 Barrett’s and 15 adenocarcinoma samples was extracted and processed for hybridization on Affymetrix HG-U133A microarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: David Beer

PROVIDER: E-GEOD-37200 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Gene expression profiling of esophageal adenocarcinoma

Project description:Specimens were collected from esophageal adenocarcinoma patients undergoing esophagectomy for adenocarcinoma at the University of Michigan Health System between 1991 and 2004. Written consent was obtained from each patient according to the approval and guidelines of the University of Michigan institutional review board. Patients receiving treatment with chemotherapy and/or radiotherapy prior to surgery were excluded. Tissue samples were fresh-frozen in liquid nitrogen and stored at −80 °C until use. Cellularity of tumor samples were assured to be greater than 70% before sample RNA was isolated. RNA from 22 adenocarcinoma samples was extracted and processed for hybridization on Affymetrix Human Genome U133A 2.0 Array.

2012-04-10 | E-GEOD-37201 | biostudies-arrayexpress

Gene expression profiling of Barrett’s esophageal tissues and esophageal adenocarcinoma specimens

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Project description:Specimens were collected from esophageal adenocarcinoma patients undergoing esophagectomy for adenocarcinoma at the University of Michigan Health System between 1991 and 2012. Written consent was obtained from each patient according to the approval and guidelines of the University of Michigan institutional review board. Patients receiving treatment with chemotherapy and/or radiotherapy prior to surgery were excluded. Tissue samples were fresh-frozen in liquid nitrogen and stored at -80 °C until use.

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Barrett's esophageal tissues and esophageal adenocarcinoma specimens

Project description:This SuperSeries is composed of the following subset Series: GSE37200: Gene expression profiling of Barrett’s esophageal tissues and esophageal adenocarcinoma specimens GSE37201: Gene expression profiling of esophageal adenocarcinoma Refer to individual Series

2012-04-10 | E-GEOD-37203 | biostudies-arrayexpress

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

Project description:Background: Barrett’s esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic haematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n=15), LGD non-progressive (n=14), LGD progressive (n=14), and HGD (n=14). MSI experiments were conducted at 50x50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches.

2021-11-15 | PXD028949 | Pride

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