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Multi-institutional Validation Study of Pancreatic Cyst Fluid Protein Analysis for Prediction of High-risk Intraductal Papillary Mucinous Neoplasms of the Pancreas

Al Efishat, Mohammad A. MD*; Attiyeh, Marc A. MD*; Eaton, Anne A. MS; Gönen, Mithat PhD; Prosser, Denise BS; Lokshin, Anna E. PhD; Castillo, Carlos Fernández-del MD§; Lillemoe, Keith D. MD§; Ferrone, Cristina R. MD§; Pergolini, Ilaria MD§; Mino-Kenudson, Mari MD; Rezaee, Neda MD||; Dal Molin, Marco MD||; Weiss, Matthew J. MD||; Cameron, John L. MD||; Hruban, Ralph H. MD**; D’Angelica, Michael I. MD*; Kingham, T. Peter MD*; DeMatteo, Ronald P. MD*; Jarnagin, William R. MD*; Wolfgang, Christopher L. MD, PhD||; Allen, Peter J. MD*

doi: 10.1097/SLA.0000000000002421

Objective: Preliminary work by our group suggested that proteins within the pancreatic cyst fluid (CF) may discriminate degree of IPMN dysplasia. We sought to externally validate these markers and determine whether their inclusion in a preoperative clinical nomogram could increase diagnostic accuracy.

Summary Background Data: IPMN is the most common radiographically identifiable precursor to pancreatic cancer; however, the timing and frequency of its malignant progression are unknown, and there are currently no reliable preoperative tests that can determine the grade of dysplasia in IPMN.

Methods: Clinical and radiographic data, as well as CF samples, were obtained from 149 patients who underwent resection for IPMN at 1 of 3 institutions. High-risk disease was defined as the presence of high-grade dysplasia or invasive carcinoma. Multianalyte bead array analysis (Luminex) of CF was performed for 4 protein markers that were previously associated with high-risk disease. Logistic regression models were fit on training data, with and without adjustment for a previously developed clinical nomogram and validated with an external testing set. The models incorporating clinical risk score were presented graphically as nomograms.

Results: Within the group of 149 resected patients, 89 (60%) had low-risk disease, and 60 (40%) had high-risk disease. All 4 CF markers (MMP9, CA72–4, sFASL, and IL-4) were overexpressed in patients with high-risk IPMN (P < 0.05). Two predictive models based on preselected combinations of CF markers had concordance indices of 0.76 (Model-1) and 0.80 (Model-2). Integration of each CF marker model into a previously described clinical nomogram leads to increased discrimination compared with either the CF models or nomogram alone (c-indices of 0.84 and 0.83, respectively).

Conclusions: This multi-institutional study validated 2 CF protein marker models for preoperative identification of high-risk IPMN. When combined with a clinical nomogram, the ability to predict high-grade dysplasia was even stronger.

*Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY

Department of Pathology, University of Pittsburgh Cancer Institute, Pittsburgh, PA

§Department of Surgery, Massachusetts General Hospital, Boston, MA

Department of Pathology, Massachusetts General Hospital, Boston, MA

||Department of Surgery, Johns Hopkins Hospital, Baltimore, MD

**Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD.

Reprints: Peter J. Allen, MD, Professor of Surgery, Memorial Sloan-Kettering Cancer Center, 1275 York Ave- Office C896, New York, NY 10021. E-mail:

This study was supported in part by NIH/NCI research project (R01) grant, project 5R01CA182076-02, and the NCI SPORE grant CA62924.

This work has been presented as an oral presentation at the 2016 American College of Surgery (ACS) Clinical Congress, Washington, DC, October 16–20, 2016.

Authors’ contributions are as follows:

Conception and Design: Al Efishat, Attiyeh, Fernández-del Castillo, Gönen, Lokshin, Lillemoe, Weiss, Cameron, Hruban, D’Angelica, Kingham, DeMatteo, Jarnagin, Wolfgang, Allen.

Development of Methodology: Al Efishat, Gonen, Fernández-del Castillo, Lokshin, Wolfgang, Allen.

Acquisition of Data: Al Efishat, Attiyeh, Pergolini, Rezaee, Dal Molin, Mino-Kenudson, Prosser.

Analysis and Interpretation of Data: Al Efishat, Attiyeh, Gonen, Eaton, Allen.

Drafting of Manuscript: Al Efishat, Allen.

Review and Critical Revision of the Manuscript: Al Efishat, Attiyeh, Eaton, Gonen, Prosser, Lokshin, Lomakin, Fernández-del Castillo, Lillemoe, Ferrone, Mino-Kenudson, Pergolini, Rezaee, Dal Molin, Weiss, Cameron, Hruban, D’Angelica, Kingham, DeMatteo, Jarnagin, Wolfgang, Allen.

Administrative, Technical, and Material Support: Al Efishat, Attiyeh, Pergolini, Rezaee, Ferrone, Dal Molin, Mino-Kenudson, Prosser, Lomakin, Allen.

Study Supervision: Allen.

The authors declare no potential conflicts of interest.

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