Cyst fluid biosignature to predict intraductal papillary mucinous neoplasms of the pancreas with high malignant potential
BACKGROUND: Current standard-of-care technologies, such as imaging and cyst fluid analysis, are unable to consistently distinguish intraductal papillary mucinous neoplasms (IPMNs) of the pancreas at high risk of pancreatic cancer from low-risk IPMNs. The objective was to create a single-platform ass...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
19 February 2019
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| In: |
Journal of the American College of Surgeons
Year: 2019, Volume: 228, Issue: 5, Pages: 721-729 |
| ISSN: | 1879-1190 |
| DOI: | 10.1016/j.jamcollsurg.2019.02.040 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1016/j.jamcollsurg.2019.02.040 Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S1072751519301498 
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| Author Notes: | Ajay V Maker, MD, FACS, Vincent Hu, PhD, Shrihari S Kadkol, MD, PhD, Lenny Hong, MS,William Brugge, MD, Jordan Winter, MD, FACS, Charles J Yeo, MD, FACS, Thilo Hackert, MD, Markus Büchler, MD, FACS, Rita T Lawlor, PhD, Roberto Salvia, MD, Aldo Scarpa, MD,Claudio Bassi, MD, FACS, Stefan Green, PhD |
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| 245 | 1 | 0 | |a Cyst fluid biosignature to predict intraductal papillary mucinous neoplasms of the pancreas with high malignant potential |c Ajay V Maker, MD, FACS, Vincent Hu, PhD, Shrihari S Kadkol, MD, PhD, Lenny Hong, MS,William Brugge, MD, Jordan Winter, MD, FACS, Charles J Yeo, MD, FACS, Thilo Hackert, MD, Markus Büchler, MD, FACS, Rita T Lawlor, PhD, Roberto Salvia, MD, Aldo Scarpa, MD,Claudio Bassi, MD, FACS, Stefan Green, PhD |
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| 520 | |a BACKGROUND: Current standard-of-care technologies, such as imaging and cyst fluid analysis, are unable to consistently distinguish intraductal papillary mucinous neoplasms (IPMNs) of the pancreas at high risk of pancreatic cancer from low-risk IPMNs. The objective was to create a single-platform assay to identify IPMNs that are at high risk for malignant progression. - STUDY DESIGN: Building on the Verona International Consensus Conference branch duct IPMN biomarker review, additional protein, cytokine, mucin, DNA, and microRNA cyst fluid targets were identified for creation of a quantitative polymerase chain reaction-based assay. This included messenger RNA markers: ERBB2, GNAS, interleukin 1β, KRAS, MUCs1, 2, 4, 5AC, 7, prostaglandin E2R, PTGER2, prostaglandin E synthase 2, prostaglandin E synthase 1, TP63; microRNA targets: miRs 101, 106b, 10a, 142, 155, 17, 18a, 21, 217, 24, 30a, 342, 532, 92a, and 99b; and GNAS and KRAS mutational analysis. A multi-institutional international collaborative contributed IPMN cyst fluid samples to validate this platform. Cyst fluid gene expression levels were normalized, z-transformed, and used in classification and regression analysis by a support vector machine training algorithm. - RESULTS: From cyst fluids of 59 IPMN patients, principal component analysis confirmed no institutional bias/clustering. Lasso (least absolute shrinkage and selection operator)-penalized logistic regression with binary classification and 5-fold cross-validation used area under the curve as the evaluation criterion to create the optimal signature to discriminate IPMNs as low risk (low/moderate dysplasia) or high risk (high-grade dysplasia/invasive cancer). The most predictive signature was achieved with interleukin 1β, MUC4, and prostaglandin E synthase 2 to accurately discriminate high-risk cysts from low-risk cysts with an area under the curve of up to 0.86 (p = 0.002). - CONCLUSIONS: We have identified a single-platform polymerase chain reaction-based assay of cyst fluid to accurately predict IPMNs with high malignant potential for additional studies. | ||
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| 700 | 1 | |a Kadkol, Shrihari S. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Hong, Lenny |e VerfasserIn |4 aut | |
| 700 | 1 | |a Brugge, William |e VerfasserIn |4 aut | |
| 700 | 1 | |a Winter, Jordan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Yeo, Charles J. |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Bassi, Claudio |e VerfasserIn |4 aut | |
| 700 | 1 | |a Green, Stefan |e VerfasserIn |4 aut | |
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