Randall E Brand1, Alex T Adai2, Barbara A Centeno3, Linda S Lee4, George Rateb5, Shivakumar Vignesh3, Charles Menard5, Anna Wiechowska-Kozłowska6, Hubert Bołdys7, Marek Hartleb7, Michael K Sanders1, Johanna B Munding8, Andrea Tannapfel8, Stephan A Hahn9, Ludomir Stefańczyk10, Gregory J Tsongalis11, David C Whitcomb1, Darwin L Conwell4, Jean A Morisset5, Timothy B Gardner11, Stuart R Gordon11, Arief A Suriawinata11, Maura B Lloyd2, Dennis Wylie2, Emmanuel Labourier2, Bernard F Andruss2, Anna E Szafranska-Schwarzbach12. 1. Department of Medicine, Division of Gastroenterology, Hepatology & Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. 2. Asuragen, Inc, Austin, Texas. 3. Department of Pathology and Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. 4. Center for Pancreatic Disease, Brigham and Women's Hospital, Boston, Massachusetts. 5. Department of Medicine, Division of Gastroenterology, University of Sherbrooke, Fleurimont, Quebec, Canada. 6. Department of Endoscopy, Hospital of the Ministry of Internal Affairs and Administration, Szczecin, Poland. 7. Department of Gastroenterology and Hepatology, Medical University of Silesia, Katowice, Poland. 8. Institute of Pathology, Ruhr-University of Bochum, Bochum, Germany. 9. Molecular GI-Oncology and Department of Pathology, Ruhr-University of Bochum, Bochum, Germany. 10. Department of Medicine, Division of Radiology, Medical University of Łódź, Łódz, Poland. 11. Department of Pathology and Department of Gasteroenterology and Hepatology, Dartmouth Hitchcock Medical Center and The Audrey and Theodor Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 12. Asuragen, Inc, Austin, Texas. Electronic address: aschwarzbach@asuragen.com.
Abstract
BACKGROUND & AIMS: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in combination with cytopathology is the optimal method for diagnosis and staging of pancreatic ductal adenocarcinoma (PDAC) and other pancreatic lesions. Its clinical utility, however, can be limited by high rates of indeterminate or false-negative results. We aimed to develop and validate a microRNA (miRNA)-based test to improve preoperative detection of PDAC. METHODS: Levels of miRNAs were analyzed in a centralized clinical laboratory by relative quantitative polymerase chain reaction in 95 formalin-fixed paraffin-embedded specimens and 228 samples collected by EUS-FNA during routine evaluations of patients with solid pancreatic masses at 4 institutions in the United States, 1 in Canada, and 1 in Poland. RESULTS: We developed a 5-miRNA expression classifier, consisting of MIR24, MIR130B, MIR135B, MIR148A, and MIR196, that could identify PDAC in well-characterized, formalin-fixed, paraffin-embedded specimens. Detection of PDAC in EUS-FNA samples increased from 78.8% by cytology analysis alone (95% confidence interval, 72.2%-84.5%) to 90.8% when combined with miRNA analysis (95% confidence interval, 85.6%-94.5%). The miRNA classifier correctly identified 22 additional true PDAC cases among 39 samples initially classified as benign, indeterminate, or nondiagnostic by cytology. Cytology and miRNA test results each were associated significantly with PDAC (P < .001), with positive predictive values greater than 99% (95% confidence interval, 96%-100%). CONCLUSIONS: We developed and validated a 5-miRNA classifier that can accurately predict which preoperative pancreatic EUS-FNA specimens contain PDAC. This test might aid in the diagnosis of pancreatic cancer by reducing the number of FNAs without a definitive adenocarcinoma diagnosis, thereby reducing the number of repeat EUS-FNA procedures.
BACKGROUND & AIMS: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in combination with cytopathology is the optimal method for diagnosis and staging of pancreatic ductal adenocarcinoma (PDAC) and other pancreatic lesions. Its clinical utility, however, can be limited by high rates of indeterminate or false-negative results. We aimed to develop and validate a microRNA (miRNA)-based test to improve preoperative detection of PDAC. METHODS: Levels of miRNAs were analyzed in a centralized clinical laboratory by relative quantitative polymerase chain reaction in 95 formalin-fixed paraffin-embedded specimens and 228 samples collected by EUS-FNA during routine evaluations of patients with solid pancreatic masses at 4 institutions in the United States, 1 in Canada, and 1 in Poland. RESULTS: We developed a 5-miRNA expression classifier, consisting of MIR24, MIR130B, MIR135B, MIR148A, and MIR196, that could identify PDAC in well-characterized, formalin-fixed, paraffin-embedded specimens. Detection of PDAC in EUS-FNA samples increased from 78.8% by cytology analysis alone (95% confidence interval, 72.2%-84.5%) to 90.8% when combined with miRNA analysis (95% confidence interval, 85.6%-94.5%). The miRNA classifier correctly identified 22 additional true PDAC cases among 39 samples initially classified as benign, indeterminate, or nondiagnostic by cytology. Cytology and miRNA test results each were associated significantly with PDAC (P < .001), with positive predictive values greater than 99% (95% confidence interval, 96%-100%). CONCLUSIONS: We developed and validated a 5-miRNA classifier that can accurately predict which preoperative pancreatic EUS-FNA specimens contain PDAC. This test might aid in the diagnosis of pancreatic cancer by reducing the number of FNAs without a definitive adenocarcinoma diagnosis, thereby reducing the number of repeat EUS-FNA procedures.
Authors: Lawrence Mj Best; Vishal Rawji; Stephen P Pereira; Brian R Davidson; Kurinchi Selvan Gurusamy Journal: Cochrane Database Syst Rev Date: 2017-04-17
Authors: Patrick W Underwood; Michael H Gerber; Kathy Nguyen; Daniel Delitto; Song Han; Ryan M Thomas; Christopher E Forsmark; Jose G Trevino; William E Gooding; Steven J Hughes Journal: J Am Coll Surg Date: 2019-10-28 Impact factor: 6.113
Authors: Adam E Frampton; Jonathan Krell; Mireia Mato Prado; Tamara M H Gall; Nima Abbassi-Ghadi; Giovanna Del Vecchio Blanco; Niccola Funel; Elisa Giovannetti; Leandro Castellano; Mohamed Basyouny; Nagy A Habib; Harry Kaltsidis; Panagiotis Vlavianos; Justin Stebbing; Long R Jiao Journal: Oncotarget Date: 2016-05-10