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. 2010 May;251(5):937-45.
doi: 10.1097/SLA.0b013e3181d7738d.

Glycosylation variants of mucins and CEACAMs as candidate biomarkers for the diagnosis of pancreatic cystic neoplasms

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Glycosylation variants of mucins and CEACAMs as candidate biomarkers for the diagnosis of pancreatic cystic neoplasms

Brian B Haab et al. Ann Surg. 2010 May.

Abstract

Background and aims: Cystic lesions of the pancreas are increasingly being recognized due to the widespread use of high resolution abdominal imaging. Since certain cyst types are precursors to invasive cancer, this situation presents an opportunity to intervene prior to malignant progression. Effective implementation of that strategy has been hampered by difficulties in clearly distinguishing cystic lesions with no malignant potential from those with malignant potential. Here we explored whether glycosylation variants on specific proteins in cyst fluid samples could serve as biomarkers to aid in this diagnosis.

Methods: We used a novel antibody-lectin sandwich microarray method to measure the protein expression and glycosylation of mucin (MUC)1, MUC5AC, MUC16, carcinoembryonic antigen, and other proteins implicated in pancreatic neoplasia in cyst fluid samples. Fifty-three cyst fluid samples were obtained from patients with mucinous cystic neoplasms (n=17), intraductal papillary mucinous neoplasms (n=15), serous cystadenomas (n=12), or pseudocysts (n=9), with confirmation of histologic diagnosis at surgical resection.

Results: The detection of a glycan variant on MUC5AC using the lectin wheat-germ agglutinin discriminated mucin-producing cystic tumors (mucinous cystic neoplasms+intraductal papillary mucinous neoplasms) from benign cystic lesions (serous cystadenomas+pseudocysts) with a 78% sensitivity at 80% specificity, and when used in combination with cyst fluid CA 19-9 gave a sensitivity of 87% at 86% specificity. These biomarkers performed better than cyst fluid carcinoembryonic antigen (37%/80% sensitivity/specificity).

Conclusions: These results demonstrate the value of glycan variants for biomarker discovery and suggest that these biomarkers could greatly enhance the accuracy of differentiating pancreatic cystic tumors. Validation studies will be required to determine the clinical value of these markers.

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Figures

Figure 1
Figure 1
Protein and glycan detection on antibody arrays. a) Array-based sandwich assays for protein detection. Multiple antibodies are immobilized on a planar support, and the captured proteins are probed using biotinylated detection antibodies, followed by fluorescence detection using phycoerythrin-labeled streptavidin. b) Glycan detection on antibody arrays. This format is similar to above, but the detection reagents target the glycans on the capture proteins rather than the core proteins. The glycans on the immobilized antibodies are chemically derivatized to prevent lectin binding to those glycans. c) High-throughput sample processing. Forty-eight or sixty identical microarrays are printed on one microscope slide, segregated by hydrophobic boundaries. A set of serum samples is incubated on the arrays in a random order, and each slide is probed with a single antibody or lectin. d) Example antibody array results for specific capture antibodies (indicated at left) and detection reagents (indicated in the column labels), after incubation with the indicated samples.
Figure 2
Figure 2
Cluster analysis of antibody-lectin sandwich array results. Measurements showing significant differences (p < 0.02) between mucin-producing cystic neoplasms (MCN and IPMN) to non-mucinous cysts (SC and PC) are presented. Each square represents the signal level from a sample (indicated by the column labels) detected with a particular capture antibody and detection reagent (indicated by the row labels). Each column label gives the diagnosis and a patient identifier, and the color of the label indicates whether the sample is a mucin-producing cystic neoplasms (MCN or IPMN, red) or non-mucinous cyst (SC or PC, green). The fluorescence values were log-transformed (base 10) and median-centered along each row in order to clearly show the variation between the samples. The color bar gives the scale, in which each unit represents a 10-fold change.
Figure 3
Figure 3
Box plots indicating the levels of particular markers in each class. Each point represents an individual sample. The boxes indicate the quartiles, with the median indicated by the horizontal lines, and the vertical lines give the ranges. A) WGA detection at the MUC5AC capture antibody. B) CEA. C) CA 19-9. D) MUC1.
Figure 4
Figure 4
Comparisons of results from antibody microarrays to Western blots. A) Comparisons of MUC5AC levels. The antibody microarray results are represented by the column graphs for the indicated capture and detection antibodies and the indicated samples. The corresponding samples were separated by SDS-PAGE (with the lane order matching the column graphs), blotted, and probed using the indicated detection antibodies. Lysates from cell lines were analyzed in the right lanes. The region of the separations containing the molecular weights expected for MUC5AC are indicated by the blue boxes. B) Comparisons of CEA levels.
Figure 5
Figure 5
Discriminating patient groups using individual and combined markers. A) Scatter plot comparison of two biomarkers. Each point represents a sample, with the color of each point indicating its class, according to the legend. The y-axis represents the level of CA19-9, and the x-axis represents the level of WGA-MUC5AC. The dashed lines are the thresholds used to dichotomize the samples for each marker. B) Receiver-operator characteristic (ROC) curves for the discrimination of mucin-producing cystic tumors (MCN and IPMN) from non-mucinous (SC and PC) cysts. The area-under-the-curve (AUC) and 95% confidence interval are indicated for CEA, WGA-MUC5AC, and the combination of WGA-MUC5AC and CA 19-9. For the combined biomarker, each sample was classified as mucin-producing if the level of either biomarker was above its threshold indicated by the dashed lines in panel A.

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