Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material
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Thomas M. Gress1,*, Ludwig Lausser2,*, Lyn-Rouven Schirra2,*, Lisa Ortmüller1, Ramona Diels1, Bo Kong3, Christoph W. Michalski3,4, Thilo Hackert4, Oliver Strobel4, Nathalia A. Giese4, Miriam Schenk4, Rita T. Lawlor5, Aldo Scarpa5, Hans A. Kestler2,* and Malte Buchholz1,*
1Clinic for Gastroenterology, Endocrinology and Metabolism, University Hospital, Philipps-Universität Marburg, Marburg, Germany
2Institute of Medical Systems Biology, University of Ulm, Ulm, Germany
3Department of Surgery, Technical University of Munich, Munich, Germany
4Department of Surgery, University of Heidelberg, Heidelberg, Germany
5ARC-Net Centre for Applied Research on Cancer and Department of Pathology, University of Verona, Verona, Italy
*These authors have contributed equally to this work
Thomas M. Gress, email: email@example.com
Keywords: pancreatic cancer; pancreatico-biliary tumors; molecular diagnostics; fine needle aspiration biopsy; microfluidic TaqMan arrays
Received: August 31, 2017 Accepted: October 30, 2017 Published: November 21, 2017
Pancreatic ductal adenocarcinoma (PDAC) continues to carry the lowest survival rates among all solid tumors. A marked resistance against available therapies, late clinical presentation and insufficient means for early diagnosis contribute to the dismal prognosis. Novel biomarkers are thus required to aid treatment decisions and improve patient outcomes.
We describe here a multi-omics molecular platform that allows for the first time to simultaneously analyze miRNA and mRNA expression patterns from minimal amounts of biopsy material on a single microfluidic TaqMan Array card. Expression profiles were generated from 113 prospectively collected fine needle aspiration biopsies (FNAB) from patients undergoing surgery for suspect masses in the pancreas. Molecular classifiers were constructed using support vector machines, and rigorously evaluated for diagnostic performance using 10x10fold cross validation. The final combined miRNA/mRNA classifier demonstrated a sensitivity of 91.7%, a specificity of 94.5%, and an overall diagnostic accuracy of 93.0% for the differentiation between PDAC and benign pancreatic masses, clearly outperfoming miRNA-only classifiers. The classification algorithm also performed very well in the diagnosis of other types of solid tumors (acinar cell carcinomas, ampullary cancer and distal bile duct carcinomas), but was less suited for the diagnostic analysis of cystic lesions.
We thus demonstrate that simultaneous analysis of miRNA and mRNA biomarkers from FNAB samples using multi-omics TaqMan Array cards is suitable to differentiate suspect solid pancreatic masses with high precision.
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