James Saller1, Kun Jiang1, Yin Xiong2, Sean J Yoder3, Kevin Neill1, Jose M Pimiento4, Luis Pena4, F Scott Corbett5, Anthony Magliocco1, Domenico Coppola6,7,8,9. 1. Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA. 2. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 3. Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 4. Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 5. Division of Florida Digestive Health Specialists, Gastroenterology Associates of Sarasota, Bradenton, FL, USA. 6. Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA. domenico.coppola@fdhs.com. 7. Division of Florida Digestive Health Specialists, Gastroenterology Associates of Sarasota, Bradenton, FL, USA. domenico.coppola@fdhs.com. 8. Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. domenico.coppola@fdhs.com. 9. Department of Chemical Biology and Molecular Medicine, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. domenico.coppola@fdhs.com.
Abstract
BACKGROUND: Progression of Barrett esophagus (BE) to esophageal adenocarcinoma occurs among a minority of BE patients. To date, BE behavior cannot be predicted on the basis of histologic features. AIMS: We compared BE samples that did not develop dysplasia or carcinoma upon follow-up of ≥ 7 years (BE nonprogressed [BEN]) with BE samples that developed carcinoma upon follow-up of 3 to 4 years (BE progressed [BEP]). METHODS: The NanoString nCounter miRNA assay was used to profile 24 biopsy samples of BE, including 13 BENs and 11 BEPs. Fifteen samples were randomly selected for miRNA prediction model training; nine were randomly selected for miRNA validation. RESULTS: Unpaired t tests with Welch's correction were performed on 800 measured miRNAs to identify the most differentially expressed miRNAs for cases of BEN and BEP. The top 12 miRNAs (P < .003) were selected for principal component analyses: miR-1278, miR-1301, miR-1304-5p, miR-517b-3p, miR-584-5p, miR-599, miR-103a-3p, miR-1197, miR-1256, miR-509-3-5p, miR-544b, miR-802. The 12-miRNA signature was first self-validated on the training dataset, resulting in 7 out of the 7 BEP samples being classified as BEP (100% sensitivity) and 7 out of the 8 BEN samples being classified as BEN (87.5% specificity). Upon validation, 4 out of the 4 BEP samples were classified as BEP (100% sensitivity) and 4 out of the 5 BEN samples were classified as BEN (80% specificity). Twenty-four samples were evaluated, and 22 cases were correctly classified. Overall accuracy was 91.67%. CONCLUSION: Using miRNA profiling, we have identified a 12-miRNA signature able to reliably differentiate cases of BEN from BEP.
BACKGROUND: Progression of Barrett esophagus (BE) to esophageal adenocarcinoma occurs among a minority of BE patients. To date, BE behavior cannot be predicted on the basis of histologic features. AIMS: We compared BE samples that did not develop dysplasia or carcinoma upon follow-up of ≥ 7 years (BE nonprogressed [BEN]) with BE samples that developed carcinoma upon follow-up of 3 to 4 years (BE progressed [BEP]). METHODS: The NanoString nCounter miRNA assay was used to profile 24 biopsy samples of BE, including 13 BENs and 11 BEPs. Fifteen samples were randomly selected for miRNA prediction model training; nine were randomly selected for miRNA validation. RESULTS: Unpaired t tests with Welch's correction were performed on 800 measured miRNAs to identify the most differentially expressed miRNAs for cases of BEN and BEP. The top 12 miRNAs (P < .003) were selected for principal component analyses: miR-1278, miR-1301, miR-1304-5p, miR-517b-3p, miR-584-5p, miR-599, miR-103a-3p, miR-1197, miR-1256, miR-509-3-5p, miR-544b, miR-802. The 12-miRNA signature was first self-validated on the training dataset, resulting in 7 out of the 7 BEP samples being classified as BEP (100% sensitivity) and 7 out of the 8 BEN samples being classified as BEN (87.5% specificity). Upon validation, 4 out of the 4 BEP samples were classified as BEP (100% sensitivity) and 4 out of the 5 BEN samples were classified as BEN (80% specificity). Twenty-four samples were evaluated, and 22 cases were correctly classified. Overall accuracy was 91.67%. CONCLUSION: Using miRNA profiling, we have identified a 12-miRNA signature able to reliably differentiate cases of BEN from BEP.
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