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Glycans Related to the CA19-9 Antigen Are Increased in Distinct Subsets of Pancreatic Cancers and Improve Diagnostic Accuracy Over CA19-9

Background & Aims The cancer antigen 19-9 (CA19-9) is the current best biomarker for pancreatic cancer, but it is not increased in approximately 25% of pancreatic cancer patients at a cut-off value that provides a 25% false-positive rate. We hypothesized that antigens related to the CA19-9 antigen, which is a glycan called sialyl-Lewis A (sLeA), are increased in distinct subsets of pancreatic cancers. Methods We profiled the levels of multiple glycans and mucin glycoforms in plasma from 200 subjects with either pancreatic cancer or benign pancreatic disease, and we validated selected findings in additional cohorts of 116 and 100 subjects, the latter run with the investigators blinded to diagnoses and including cancers that exclusively were early stage. Results We found significant increases in 2 glycans: an isomer of sLeA called sialyl-Lewis X, present both in sulfated and nonsulfated forms, and the sialylated form of a marker for pluripotent stem cells, type 1 N-acetyl-lactosamine. The glycans performed as well as sLeA as individual markers and were increased in distinct groups of patients, resulting in a 3-marker panel that significantly improved upon any individual biomarker. The panel showed 85% sensitivity and 90% specificity in the combined discovery and validation cohorts, relative to 54% sensitivity and 86% specificity for sLeA; and it showed 80% sensitivity and 84% specificity in the independent test cohort, as opposed to 66% sensitivity and 72% specificity for sLeA. Conclusions Glycans related to sLeA are increased in distinct subsets of pancreatic cancers and yield improved diagnostic accuracy compared with CA19-9.

M any pancreatic cancers secrete glycoproteins and glycolipids that bear a glycan called sialyl-Lewis A (sLeA). 1,2 The sLeA glycan forms the basis for the Food and Drug Administration-approved cancer antigen 19-9 (CA19-9) test, named after the monoclonal antibody first developed against the sLeA antigen. 3 The test is used as an approximate indicator of extent of disease recurrence, but a problem with CA19-9 is that it is not increased in a substantial proportion of patients. By using a typical cut-off value of 37 U/mL, approximately 25%-35% of patients do not show increases, 4 rendering the test inconclusive for the diagnosis or monitoring of cancer in many patients. However, the test is very specific for cancer at high cut-off values. 4 Therefore, CA19-9 represents an important marker for pancreatic cancer and a good basis on which to build molecular indicators for cancer, but it needs to be improved. After many years of research since the discovery of CA19-9, a biomarker validated to perform better than CA19-9 for pancreatic cancer detection is not yet available. Identifying another marker to detect cancer among patients with low CA19-9 levels potentially could lead to an improved diagnostic test.
The sLeA glycan is part of a family of glycans called the Lewis antigens, named after the discoverer of a series of antigens found on red blood cells comprising a system of blood types. The Lewis glycans generally appear on the termini of oligosaccharides attached to both proteins and lipids. The common feature among the family members is a core N-acetyl-lactosamine (LacNAc), which is a disaccharide of galactose linked to N-acetylglucosamine. The monosaccharides fucose and sialic acid can be attached to the LacNAc in various linkages. A sulfate group also can be attached to the Galactose or N-Acetylglucosamine. In the normal pancreas, sLeA appears on the epithelial surfaces of the ducts, and in the cancerous pancreas, it can be heavily secreted into the lumen of the proliferating ducts. 5 The increase of sLeA in the blood likely results from accumulation in the stroma followed by leakage into the capillaries or lymph. 6 One reason for the lack of increases is genetics. A glycosyltransferase enzyme that is critical for the biosynthesis of sLeA, fucosyltransferase 3, is inactive in approximately 5% of the North American population as a result of homozygous mutations in the active part of the gene. 7 But the cause of low CA19-9 levels is not clear for patients with wild-type fucosytransferase 3.
Other members of the Lewis glycans besides sLeA also appear both in the normal and cancerous pancreas. An isomer of sLeA called sialyl Lewis X (sLeX) is up-regulated in the tissue of some pancreatic cancers, 8 and we 9,10 and others 11 found it increased in the circulation of many pancreatic cancer patients. Some patients have an increase in a glycan detected by the DUPAN-2 monoclonal antibody, 12,13 identified primarily as type 1 sialyl-LacNAc, 14,15 and our previous research also found indirect evidence for additional glycans by comparing patient increases between anti-sLeA antibodies with either broad or narrow specificity. 15 These observations raise the possibility that diversity exists between pancreatic cancers in the type of glycans they make and secrete into the blood. Potentially, a variety of glycans is secreted, with differences between individual cancers. Thus, to encompass the full range of pancreatic cancers, we may need to detect the various antigens that pancreatic cancers are expressing in addition to sLeA, and that are not normally increased under healthy or benign conditions. Assays to detect the additional cancer-associated glycans potentially could be used to identify a higher percentage of pancreatic cancer patients than sLeA alone. Therefore, in this research, we tested the hypothesis that certain glycans related to sLeA are increased in the plasma of pancreatic cancer patients and that they detect patients that have low levels of sLeA.

Human Plasma and Tissue Samples
All collections took place at the University of Pittsburgh Medical Center after obtaining informed consent from the participants and before any surgical or medical procedures. The donors consisted of patients with pancreatic cancer, pancreatitis, or benign biliary obstruction, and from healthy subjects (Table 1 and Supplementary Table 1). Resectable cancer included stages I and II, and nonresectable cancer included stages III and IV. The pancreatitis patients were a mixture of chronic and acute, and the healthy subjects had no evidence of pancreatic, biliary, or liver disease. All blood samples (EDTA plasma) were collected according to the standard operating procedure from the Early Detection

March 2016 Panel of Glycan Biomarkers
Research Network and were frozen at -70 C or colder within 4 hours of time of collection. Aliquots were shipped on dry ice and thawed no more than 3 times before analysis. In addition, the Van Andel Research Institute Biospecimen Facility provided formalin-fixed, paraffinembedded tissue from patients who underwent pancreatic resections at a regional hospital affiliate in Grand Rapids, Michigan. The Institutional Review Boards at the University of Pittsburgh Medical Center and the Van Andel Research Institute approved this research project (protocol #12008).
The antibodies and lectins were acquired from various sources (Supplementary Table 2). The capture antibodies to be printed onto microarray slides were purified by dialysis (Slide-A-Lyzer; Pierce Biotechnology, Rockford, IL) to 1Â PBS and ultracentrifuged. Biotinylation was performed using the EZ-Link-sulfo-NHS-LC-Biotin kit (Pierce Biotechnology) according to the manufacturer's instructions.

Antibody Array Fabrication and Use
The antibody array methods followed those presented earlier, [16][17][18] with slight modifications. We printed 48 identical arrays containing various antibodies (Supplementary  Table 2) onto glass microscope slides coated with ultrathin nitrocellulose (PATH Slides; Grace BioLabs, Bend, OR) using a contact printer (Aushon 2470; Aushon BioSystems, Billerica, MA). We printed 6 replicates of each antibody in randomized positions within each array. After printing, hydrophobic borders were imprinted onto the slides (Sli-deImprinter; The Gel Company, San Francisco, CA) to segregate the arrays and allow for individual sample incubations on each array. The arrays were blocked using 1% bovine serum albumin (BSA) in PBS plus 0.5% Tween-20 for 1 hour at room temperature. The slides were rinsed in 1Â PBS plus 0.5% Tween-20, washed in the same buffer for 15 minutes, and dried by brief centrifugation at 160 Â g, with printed arrays facing outside.
The plasma samples were diluted 2-fold into PBS containing 0.1% Tween-20, 0.1% Brij-35, an IgG blocking cocktail (200 mg/mL mouse and rabbit IgG and 100 mg/mL goat and sheep IgG; Jackson ImmunoResearch), and protease inhibitor (Complete Mini EDTA-free Tablet, Roche Applied Science). We applied 6 mL of each plasma sample to each array and let the sample incubate overnight at 4 C. Each unique sample was applied to 3 separate arrays. The arrays were washed in 3 changes of PBS/0.1% Tween-20 for 3 minutes each and dried by centrifugation (Eppendorf 5810R, Hauppauge, NY rotor A-4-62, 1500 Â g for 3 minutes), and a biotinylated lectin or antibody was incubated on the arrays for 1 hour at room temperature. The lectins and antibodies were prepared at 3 mg/mL in PBS with 0.1% BSA and 0.1% Tween-20, except for the anti-LeA (clone 7LE) antibody, which was at 15 mg/mL. For Coprinopsis cinerea lectin 2 (CCL2) detection, we pre-incubated the CCL2 with Cy5-conjugated streptavidin at a 4:1 molar ratio as described. 9 After washing and drying the arrays as described earlier, Cy5-conjugated streptavidin (Roche Applied Science) prepared at 2 mg/mL in PBS with 0.1% BSA and 0.1% Tween-20 was incubated for 1 hour at room temperature, followed by a final wash and dry. The arrays detected with precomplexed CCL2/streptavidin required only a final wash and dry. We scanned the slides for fluorescence using 633-nm excitation (LS Reloaded; Tecan, San Jose, CA).
We quantified the resulting images using in-house software written in Matlab (version R2014a; Mathworks, Natick, MA). We used a custom script to remove any outliers from the 6 replicate spots according to the Grubbs test. The script calculates the Grubbs statistic for the spot farthest from the mean of the replicates and rejects the spot if the Grubbs statistic exceeds a preset threshold, using P < .1 here. The script repeatedly removes spots until no outliers remain or to a minimum of 4 spots. It then calculates the geometric mean of the remaining replicate spots as the final output for each array.
The program also averages values between replicate arrays and reports the associated coefficient of variation. We repeated assays for measurements that had a CV greater than 0.4 for signals in the quantifiable response range of the assay (determined by dilution series of pooled samples). 19

Statistics and Analysis Methods
To characterize classification performance of individual biomarkers, nonparametric estimates of the receiver operating characteristic (ROC) curves were generated. Performance of each biomarker was compared with CA19-9 based on the area under the ROC curve (AUC). In particular, a nonparametric bootstrap procedure stratified on case and control status was performed with 500 bootstrap samples. Two-sided P values for testing the equivalence in AUC between a pair of biomarkers were computed based on a Wald test and bootstrap estimated standard error. Also reported were 95% confidence intervals of the difference in AUC based on bootstrap samples. All statistical calculations were performed using R program R-3.2.2 (https://cran.r-project. org/).
We selected marker panels using the Marker State Space method 20 with 10-fold cross-validation to select individual markers. The program limits the initial size of panels to 3 markers, with the option of adding markers iteratively. Marker State Space software is available upon request. We used GraphPad Prism (San Diego, CA) and Microsoft (Redmond, WA) Excel for graph preparation, and Canvas XIV (ACD Systems, Victoria, Canada) for figure preparation.

Immunohistochemistry, Glycan Array Analysis, and Cross-Validation
See Supplementary Materials and Methods for more detail.

Candidate Glycan Biomarkers for sLeA-Low Cancers
Several glycans are structurally similar to the CA19-9 antigen, sLeA ( Figure 1A), including variants of sialyl-Lewis X, which we previously showed was increased in a subset of pancreatic cancer patients. 9,10 To test for increases of glycans, we acquired lectins and antibodies targeting the glycans ( Figure 1B and Supplementary Table 2). Glycan array data were helpful for determining the specificities of the reagents. Some bind only 1 motif with high specificity, but others bind more, such as the 7LE antibody, which binds both Lewis A and nonfucosylated LacNAc type 1 (Supplementary Figure 1). The mouse E-selectin protein binds sLeA, sLeX, and sulfo-sLeX (Supplementary Figure 2), and we validated its use as a detection reagent using cell line and tissue specimens (Supplementary Figure 3). We previously showed that CCL2 is specific for glycans with 3' fucose, 9 mainly Lewis X variants including sulfated Lewis X.
We incubated each plasma sample on a microarray of antibodies targeting various mucins and glycans and then probed the glycans on the captured material with a glycanbinding antibody or lectin. Each sample was incubated on multiple arrays, with each array receiving a different detection reagent ( Figure 1C).
We did not have a reagent to optimally detect sialylated, nonfucosylated, type 1 N-acetyl-lactosamine structures (Siaa2,3Galb1,3GlcNAcb1-). We did, however, have 2 antibodies, called TRA-1-60 and 7LE ( Figure 1B), with good affinity to the nonsialylated variant. We therefore tested the use of sialidase to remove sialic acid before detecting with the antibodies (Figure 2A). We confirmed the ability to remove sialic acid on a captured glycoprotein and detect the underlying structure using a protein mixture with a high level of Mucin16 showing the sLeA glycan ( Figure 2B). The staining of tumor tissue in the regions of cancerous epithelia increased upon sialidase treatment ( Figure 2D), and the differentiation of cases from controls in a set of plasma samples was enhanced after enzyme treatment ( Figure 2C). Therefore, in subsequent experiments we used sialidase treatment before detection using the TRA-1-60 and 7LE antibodies.
We acquired measurements of candidate biomarkers in 3 sample cohorts, comprising discovery, validation, and test sets (Table 1 and Supplementary Table 1). Each measurement consisted of a capture antibody and a detection reagent, so with 9 capture antibodies and 12 detection reagents (Supplementary Table 2), we acquired 108 unique measurements of capture/detection pairs.
In the discovery cohort, 34 individual biomarkers had significant increases (Supplementary Table 3). Representative markers included 2 distinct glycoforms of MUC5AC, one showing type 1 sialyl-LacNAc, and the other showing sulfated and/or sialylated sLeA/sLeX ( Figure 3A). We tested a reduced set of 5 capture antibodies and 5 detection reagents (25 unique assays) in the validation cohort and observed significant increases in 19 (Supplementary  Table 3), including the glycoforms of MUC5AC ( Figure 3B). The markers mentioned earlier showed significant improvement in AUC over sLeA in the discovery set ( Figure 3C). The classification performance of sLeA in the validation set ( Figure 3D) was higher than in previous studies. A recent definitive characterization of CA19-9 showed an AUC of 0.77 for discriminating pancreatic cancer from chronic pancreatitis, with lower performance when including benign biliary obstruction, 21 so we viewed the performance in the validation set as an aberration.
Because the cancer patients tended to be older than the control subjects (Table 1), we tested associations with age for each marker within the cancer patients and within the control subjects. None showed an association with age except for the sLeA sandwich (the standard CA19-9 assay), with moderate significance (Supplementary Table 4). Thus, the markers examined here were not increased as a consequence of age.

Complementary Increases in the Markers
We next tested whether the individual markers provided complementary information to sLeA and to one anotherthat is, whether they showed increases in distinct subsets of patients and few increases in the controls. For each marker, we set a threshold to provide one false-positive increase, thus providing a view of increases that were specific to cancer. At such a threshold, CA19-9 was increased in only 22% of the cases in the discovery cohort. In contrast, several other markers showed a greater percentage of increases in the stages I-II and stages III-IV cancers, with differences between the markers in the patients with increases ( Figure 4A). The trends were similar in the validation cohort ( Figure 4B). These results suggested that the markers have increases in distinct groups of patients, independent of stage.
The results also suggested that a biomarker panel would perform better than any individual marker. By using all 316 samples from the combined discovery and validation cohorts, we found that a panel of 3 markers provided better sensitivity and specificity than sLeA ( Figure 4C). The panel (panel 1) consisted of a glycoform of MUC5AC showing sulfated-and sialyl-Lewis X (detected by CCL2); another glycoform of MUC5AC showing sialyl-LacNAc type 1 and sLeA (detected by the 7LE antibody after desialylation); and a sandwich assay consisting of the capture of sLeA and the detection of sulfated and/or sialylated sLeA/sLeX (detected by mouse E-selectin). An alternate panel (panel 2) differed by 1 marker. The marker selection program did not choose sLeA for inclusion in the panel, indicating that sLeA at best provided only marginal additional diagnostic information beyond what already was detected by the 3 markers. A notable feature of the panel is that it contains 3 classes of glycans: Lewis X variants, Lewis A/X variants, and sialylated type 1 N-acetyl-lactosamine.

Testing the Marker Panel in Blinded Samples
We applied the marker panels to a new, blinded set of 100 samples (ie, the test set), consisting of stages I-II cancer cases and patients with benign pancreatic diseases. The individual markers had robust and specific increases in cancer ( Figure 5A), and the ROC curve for a MUC5AC glycoform was improved significantly compared with sLeA (with an improvement in AUC of 0.14; 95% confidence interval, 0.04-0.26) ( Figure 5B). Furthermore, the relationships between the markers were similar to the previous sets; increases in the new markers occurred in patients who did not have sLeA increases ( Figure 5C). These observations confirmed the cancer-associated increases of the new biomarkers and their independent contributions to the patterns of increase.
In the blinded application of the panels to classify the samples, both panels 1 and 2 had higher sensitivity than sLeA, but without statistically significant improvement in overall performance (Supplementary Table 5). We reasoned that the thresholds defining increases for each individual marker were not set optimally, owing to the limited number of samples used for training. When we adjusted the thresholds, while keeping the classification rule the same, the accuracy was 82% for panel 1 compared with 69% for sLeA at its best threshold. All 3 markers of the panel showed increases in cancer patient samples that were not increased in sLeA even at the lower sLeA threshold ( Figure 5D). Furthermore, in 10-fold crossvalidation averaged over 3 trials, the average accuracy of the panel was 84%, whereas the average accuracy of the individual markers ranged from 43% to 60% ( Figure 5D). We concluded from these analyses that each of the new biomarkers was increased independently of sLeA at least in some patients, and that together they formed a biomarker panel with improved accuracy compared with sLeA.

Discussion
In this work we identified glycan biomarkers in addition to the CA19-9 antigen, sLeA, that characterizes subgroups of pancreatic cancer patients. Because the glycans do not have identical increases across patients, they can be used in combination to provide better biomarker performance than any individual marker including sLeA. The glycans can be divided into 3 structural categories, consisting of sialyl-Lewis X variants, sulfated and/or sialylated sLeA/sLeX variants, and nonfucosylated sialyl-LacNAc type 1. Each category has its own biosynthetic pathways, cell types on which the glycans are shown, and protein receptors, suggesting that the glycans reflect biological subtypes of cancer. Thus, their combined use could have value not only for improved diagnostic accuracy, but also for enhanced information about the disease. Such a capability could meet the need for improved diagnostic accuracy among symptomatic people. 22 Further research could address other needs in clinical practice, including surveillance among people with an increased risk for cancer, improving the determining likelihood of rapid progression after surgery, and monitoring the course of the disease after treatment.
Markers to subclassify pancreatic cancer cells would meet a gap in the application of molecular medicine to pancreatic cancer. Pancreatic cancers show huge diversity in histomorphologies and clinical courses, and finding a molecular basis for the differences has been difficult. For example, adenosquamous carcinomas harbor the same genetic mutations as the more common ductal adenocarcinomas. 23 Particular glycans may be better molecular indicators of the state of a cell than specific genetic alterations; DNA alterations provide information about the inception of the neoplasm, but glycans may indicate changes more clearly in cell identity and cell-environment interactions. We previously found evidence that the tumors showing high sLeA were better differentiated than tumors with high sLeX, 10 but a systematic study still is required to

March 2016
Panel of Glycan Biomarkers examine the molecular characteristics and clinical course of cancer cells showing the various glycans found here. Additional research will help determine the relationship between the glycan biomarkers and other promising candidates for the detection of resectable and early stage pancreatic cancer. A recent study showed that exosomes coated with the proteoglycan glypican-1 were increased in patients with resectable pancreatic cancer and may represent a viable biomarker for early diagnosis or detection. 24 Considering that the glycan side chains of glypican-1 are important in epithelial function and signaling, an interesting possibility is that the glycans found in the present work also are on cancer exosomes and could improve the information content of exosome detection. Other promising biomarkers include micro-RNAs, 25 DNA, 26 and tumor cells 27 in the circulation; proteins in the urine 28 ; and various types of biomarkers in the pancreatic juice or stool (reviewed by Chari et al 29 ), all of which could help define biological subtypes of pancreatic cancer.
Previous studies have shown possible origins and functions in cancer of the glycans found in this work. Particularly interesting is sialyl-LacNAc type 1, as detected by the TRA-1-60 and 7LE antibodies after desialylation. The target of the TRA-1-60 antibody, the nonsialylated version of the glycan, is an excellent marker for pluripotent stem cells. [30][31][32] Previous research found sialyl-LacNAc type 1 on glycolipids in malignant glioma 33 and embryonal carcinoma. 34 Pancreatic cancer cells frequently activate developmental pathways, [35][36][37] potentially leading to the expression of the sialyl-LacNAc type 1 epitope. Future research could test whether cancer cells showing sLacNAc t1 have active sonic hedgehog, notch, or b-catenin pathways.
Sulfated and sialylated Lewis X is found on activated and migrating lymphocytes 38,39 and are associated with an invasive phenotype in pancreatic cancer. 40 Studies in mice support a role for sLeX in invasion and modulation of immune responses. 41 Both sLeX and sLeA have the potential to promote metastasis through interactions with E-selectin receptors, 42,43 therefore the relative levels of sLeX and sLeA could affect cancer cell behavior, disease progression, and metastasis. In future work we hope to define the glycan structures and the level of sulfation more precisely, because sulfated versions of sLeX have increased affinity for E-selectin receptors. 42 In summary, we show here that glycans besides sLeA-the antigen detected by the CA19-9 assay-are increased in distinct groups of patients and contribute to the improved accuracy of a biomarker panel. The 3 types of glycans-sLeA, sLeX variants, and sialylated type 1 LacNAc-possess structures and functions associated with particular differentiation states. Thus, the new glycan biomarkers have the potential to improve the accuracy of diagnosing pancreatic cancer and to shed light on the molecular differences between tumors.

Conflicts of interest
The authors disclose no conflicts.

Funding
Supported by the National Cancer Institute, Early Detection Research Network (U01CA152653), and the Alliance of Glycobiologists for Cancer Detection (U01CA168896).

Immunohistochemistry With Sialidase Treatment
We used automated staining (Ventana Discovery Ultra) to perform immunohistochemistry (IHC) on sections cut from formalin-fixed, paraffin-embedded blocks. We performed antigen retrieval using the Ventana CC1 buffer for 36 minutes at 95 C. For the slides treated with sialidase, we incubated a 1:200 dilution of sialidase (a2-3,6,8 Neuraminidase , NEB P0720L, 50,000 U/mL) in 1X GlycoBuffer (5 mmol/L CaCl 2 , 50 mmol/L pH 5.5 sodium acetate) overnight at 37 C. The control slides received only the 1Â GlycoBuffer under the same conditions. The slides then were incubated with the TRA-1-60 antibody (NB100-730, Novus Biologicals, 500 ìg/mL diluted at 1:100) for 1 hour at RT, followed by the secondary antibody (Ventana Umap HRP-conjugated anti-mouse) for 12 minutes at 37 C. The development step used the diaminobenzadine chromagen according to preset parameters in the Ventana platform.

Glycan Array Analysis
The glycan synthesis and array core facility of the Consortium for Functional Glycomics (CFG) performed the glycan array experiments and the primary analysis according to the methods presented previously. 27 We downloaded data from www.functionalglycomics.org that previously had been obtained using lectins and glycanbinding antibodies supplied by various investigators. In addition, we sent the recombinant version of CCL2 with biotinylation at the C-terminus to the CFG core facility for processing on their glycan array version 5.2. For detailed analyses of the datasets, we used the GlycoSearch analysis program, 28 and for mining glycan array data to find particular lectins, we used the GlycanBinder database, 23 which derives information from the CFG website.

Cross-Validation
We performed 10-folded cross validation 3 times on each individual marker an on the panel, using the MSS program described in the main text. The program divides samples randomly into 10 groups; uses the samples from 9 groups to define optimal thresholds for discriminating cases from controls; and applies the thresholds to the remaining group to determine the accuracy of discrimination (calculated as the number of correct classifications divided by the total number of samples). The program repeats the process for each possible group of 9 (10 times in all), calculating an accuracy for each split and for each marker. For each marker, we averaged the accuracy over the 10 splits and over 3 repeats of the 10-fold cross validation.