The Prevalence and Nature of Glycan Alterations on Specific Proteins in Pancreatic Cancer Patients Revealed Using Antibody-Lectin Sandwich Arrays*

Changes to the glycan structures of proteins secreted by cancer cells are known to be functionally important and to have potential diagnostic value. However, an exploration of the population variation and prevalence of glycan alterations on specific proteins has been lacking because of limitations in conventional glycobiology methods. Here we report the use of a previously developed antibody-lectin sandwich array method to characterize both the protein and glycan levels of specific mucins and carcinoembryonic antigen-related proteins captured from the sera of pancreatic cancer patients (n = 23) and control subjects (n = 23). The MUC16 protein was frequently elevated in the cancer patients (65% of the patients) but showed no glycan alterations, whereas the MUC1 and MUC5AC proteins were less frequently elevated (30 and 35%, respectively) and showed highly prevalent (up to 65%) and distinct glycan alterations. The most frequent glycan elevations involved the Thomsen-Friedenreich antigen, fucose, and Lewis antigens. An unexpected increase in the exposure of α-linked mannose also was observed on MUC1 and MUC5ac, indicating possible N-glycan modifications. Because glycan alterations occurred independently from the protein levels, improved identification of the cancer samples was achieved using glycan measurements on specific proteins relative to using the core protein measurements. The most significant elevation was the cancer antigen 19-9 on MUC1, occurring in 19 of 23 (87%) of the cancer patients and one of 23 (4%) of the control subjects. This work gives insight into the prevalence and protein carriers of glycan alterations in pancreatic cancer and points to the potential of using glycan measurements on specific proteins for highly effective biomarkers.

Alterations to the glycan structures on extracellular proteins are a common feature of many types of epithelial cancer such as pancreatic, colon, and breast cancers (1,2). Cancer-associated glycan structures are thought to be functionally involved in many of the phenotypes characterizing cancer cells, including the ability to migrate, avoid apoptosis, evade immune destruction, and enter and exit the vasculature (3). Because proteins bearing cancer-associated glycans can be shed by tumor cells into the circulation, blood-based diagnostic tests using glycan detection may be possible. A potential advantage of using glycans for diagnostics is that carbohydrate modifications of particular proteins may be altered more frequently or more specifically in certain disease states than their underlying core protein concentrations. However, to evaluate and use such a strategy, the prevalence with which various structures appear and the specific proteins on which they appear must be better characterized.
Previous studies of cancer-associated glycosylation using enzymatic, chromatographic, and mass spectrometry methods have been very effective for providing detailed information about the glycan structures produced by cancer cells, but because of the requirements for large amounts of material and the time involved to analyze each sample, these studies generally used either cell culture material or a small number of patient samples. Therefore, while many cancer-associated glycans have been identified, much remains unknown about these glycans, including how often they appear, how closely they are associated with particular disease states, and the distribution of protein carriers on which they appear.
Affinity-based methods, using reagents such as lectins or glycan-binding antibodies, are a valuable complement to the above mentioned methods. Using antibodies or lectins that bind specific glycans, one may reproducibly measure the levels of those glycans over multiple samples. Although affinity-based glycosylation studies do not provide the structural detail provided by mass spectrometry and enzymatic methods, they can provide information about the biological variation of a particular motif.
Lectins and glycan-binding antibodies have been used extensively in immunohistochemistry, for example in studies to examine the tissue distribution in pancreatic tumors of certain blood group carbohydrates (4,5). Lectins have been valuable in immunoaffinity electrophoresis and blotting methods to identify cancer-associated glycan variants on major serum proteins such as ␣-fetoprotein (6), haptoglobin (7,8), ␣ 1 -acid glycoprotein (9), and ␣ 1 -antitrypsin (10). Antibodies raised against particular glycan groups, such as the Thomsen-Friedenreich antigens (11), the Lewis blood group structures (12), and underglycosylated MUC1 1 (13) also have been used to study the roles of glycans in cancer. As a means of quantifying glycans on specific proteins, lectins have been used in the capture or detection of proteins in microtiter plates (14).
We previously demonstrated an antibody-lectin sandwich array method (15) that is a valuable complement to the above methods and is ideal for profiling the prevalence of multiple glycans on multiple proteins. Glycan levels can be probed directly from biological samples, and many samples or detection conditions can be processed efficiently in a low volume, high throughput format (16). This method is complementary to lectin microarrays (17)(18)(19), which are useful for measuring glycan levels on individual, purified proteins; glycan microarrays (20,21), which are used to measure the recognition of carbohydrate structures by various glycanbinding reagents; and glycoprotein arrays (22) for examining glycosylation on proteins isolated from biological samples.
We applied this method to the study of glycan alterations on proteins in the circulation of pancreatic cancer patients. We sought to define the prevalence of various glycan alterations on particular protein carriers and to investigate whether those measurements have advantages for cancer diagnostics relative to measurements of core proteins. We designed antibody microarrays to target members of the mucin and carcinoembryonic antigen-related cell adhesion molecule (CEACAM) families because some of those proteins are known to carry cancer-associated glycans. Mucins are extracellular, long-chain glycoproteins involved in the control and protection of epithelial surfaces, and the expression and glycosylation of several mucins are often altered and functionally involved in cancer (23,24). The CEACAM family of proteins also is functionally involved in cancer, and they carry cancer-associated glycans (25,26), but the glycans on CEACAMs are less well studied than those on mucins. By measuring both glycan levels and the core protein levels of several of these molecules, we were able to investigate whether alterations to glycans can appear at a higher rate than changes to core protein abundances. The ability to test the presence of glycan structures on multiple protein carriers in multiple samples was critical to investigating these questions.

MATERIALS AND METHODS
Serum Samples-Serum samples from pancreatic cancer and healthy subjects were collected at Evanston Northwestern Healthcare under protocols approved by a local Institutional Review Board. The control samples were collected from high risk individuals from pancreatic cancer-prone families undergoing surveillance with endoscopic ultrasound or endoscopic retrograde cholangiopancreatography. The control subjects had no pancreatic lesions. A summary of the patient characteristics is given in supplemental Table 1. All samples were stored at Ϫ80°C and sent frozen on dry ice. Each aliquot had been thawed no more than three times before use.
Antibodies and Lectins-The antibodies, lectins, and carbohydrates were obtained from various sources (see Table 1). All antibodies were screened for reactivity and integrity, purified, and prepared at 0.5 mg/ml in pH 7.2 PBS. See supplemental Fig. 1 for information on the antibody preparation steps.
Microarray Fabrication-Approximate 350 pl of each antibody solution was spotted on the surfaces of ultrathin nitrocellulose-coated microscope slides (PATH slides, Gentel Biosciences) by a piezoelectric non-contact printer (Biochip Arrayer, PerkinElmer Life Sciences). Forty-eight identical arrays containing triplicates of all selected antibodies were printed on each slide. Hydrophobic borders were imprinted around each array using a stamping device (16) (SlideImprinter, The Gel Co., San Francisco, CA).
Microarray Assays-Microarray assays were performed to measure either protein levels (see Fig. 1a) or glycan levels (see Fig. 1b) on the captured proteins. Protein levels were determined using a dual antibody sandwich assay (Fig. 1a). The sandwich assay consisted of four 1-h incubations at room temperature with the following reagents: 1) blocking buffer (PBS containing 0.5% Tween 20 (PBST0.5) and 1% BSA); 2) a serum sample diluted 2-fold in 1ϫ TBS containing 0.08% Brij, 0.08 Tween 20, and 50 g/ml protease inhibitor mixture (Complete Protease Inhibitor Tablet, Roche Applied Science); 3) biotinylated detection antibody (2 g/ml) diluted in PBST0.1 containing 0.1% BSA; and 4) streptavidin-phycoerythrin (10 g/ml; Roche Applied Science) diluted in PBST0.1 containing 0.1% BSA. After each step, the slides were rinsed in three baths of PBST0.1 and dried by centrifugation (Eppendorf 5810R, rotor A-4-62, 1500 ϫ g). The measurement of glycans on the captured proteins ( Fig. 1b) was carried out as above except the glycans on the spotted antibodies were derivatized (15) prior to use (see supplemental methods), and the arrays were probed with glycan-binding lectins or antibodies.
Fluorescence emission from the phycoerythrin was detected at 570 nm using a microarray scanner (LS Reloaded, Tecan). All arrays within one slide were scanned at a single laser power and detector gain setting. The images were quantified using the software program GenePix Pro 5.0 (Molecular Devices, Sunnyvale, CA). Spots were identified using automated spot finding or manual adjustments for occasional irregularities. The local backgrounds were subtracted from the median intensity of each spot, and triplicate spots were averaged using the geometric mean. The coefficient of variation between replicate analyzed spots was 10 -15%.
Statistical Analysis-The area under the curves (AUCs) were calculated from the receiver-operator characteristic (ROC) analysis using a custom script. Pearson correlations and Student's t tests were calculated using Microsoft Excel. The Mann-Whitney rank-sum tests were performed using OriginPro 8. Clustering and visualization were performed using the programs Cluster, Treeview, and MultiExperiment Viewer.
Additional Methods-Details of the immunoprecipitation, Western blot, sugar competition, and antibody derivatization procedures are available in the supplemental materials.

RESULTS
Profiling Cancer-associated Glycans on Specific Proteins-Using a variant of standard sandwich methods to detect core protein levels (Fig. 1a), the glycans on selected mucins and CEACAMs captured by antibody arrays were probed with a variety of lectins or glycan-binding antibodies (Fig. 1b). The ability to print and process 48 or 60 antibody arrays on a single microscope slide enabled the efficient evaluation of multiple glycans in multiple samples (Fig. 1c). A set of 46 serum samples (n ϭ 23 from pancreatic cancer patients and 23 from healthy control subjects (supplemental Table 1)) was incubated on the arrays of one microscope slide (along with two arrays incubated with TBS buffer as negative controls), and each array was probed with a detection lectin or antibody. The sample set was run 35 times (on 35 microscope slides), each time detected with one of 28 different lectins or antibodies ( Fig. 1c and Table I). The arrays were probed both with antibodies targeting core proteins and with lectins targeting glycans, some of which produced clearly different binding patterns (Fig. 1d). Seventeen capture antibodies were used on each array (Table I). The specificities of the capture FIG. 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 that in a, 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 core protein detection (left) and glycan measurement (right). SA-PE, streptavidin-phycoerythrin.   . 2), and dilution curves of pooled serum samples confirmed the detection of the targeted proteins in the linear response range at a 1:2 serum dilution (supplemental Fig. 3). These data provided an opportunity to explore the prevalence of particular cancer-associated glycan alterations on specific proteins. For each capture antibody, we examined the patterns of lectin binding among the set of serum samples, for example at the anti-MUC5AC (ab1) capture antibody (Fig. 2a). Multiple detection reagents had elevated binding levels in some of the cancer patients. The glycan profiles at the anti-MUC16 capture antibody also showed multiple elevations in the cancer subjects, whereas the profiles at the anti-MUC1 capture antibody showed fewer elevations (supplemental Fig. 4).
To obtain an overview of which glycans on which proteins had the greatest differences between the cancer and control subjects, we quantified the differences between the cancer and control samples for every capture antibody and every detection reagent. We used the AUC in ROC analysis. In ROC analysis, the sensitivity and specificity of a marker are calculated at multiple thresholds scanning the range of values, and the AUC statistic gives a summary of the discriminating power of that marker over all thresholds. The matrix of AUC values from all the detection and capture reagents, represented as a cluster (Fig. 2b), revealed that the proteins showing the most differences between cancer and control were the mucins MUC5AC, MUC16, and MUC1. The detection reagents giving the most consistently elevated discrimination were the CA 19-9 antibody and the GSL-I, AAL, peanut agglutinin, and wheat germ agglutinin lectins. Clearly divergent patterns of lectin binding were evident among the capture antibodies. On the basis of this analysis, we focused subsequent analyses on the mucin proteins and their glycans.
The validity of these cancer-associated patterns in glycan expression is supported by the fact that each set of samples  Table I for lectin abbreviations. was run independently using several different batches of microarrays, each with independently randomized samples. Repeat analyses using several of the detection reagents showed that the expression patterns were highly reproducible (supplemental Fig. 5 and supplemental Table 2). In addition, sugar competition assays confirmed that the lectin binding was specific to particular glycans (supplemental Fig. 6 and supplemental Table 3). Furthermore immunoprecipitations of MUC1 followed by Western blot detection of the particular glycan levels confirmed the accuracy of the microarray measurements (supplemental Fig. 7). The glycan levels were not associated with the age or gender of the subjects (p Ͼ 0.05) as determined by examining the correlations between the glycan levels and age within each patient class or by performing a t test on the values grouped by gender (supplemental Table 4), indicating that these particular glycan alterations are more likely associated with cancer than with demographic or clinical factors.
Prevalence of Glycan Changes Relative to Core Protein Changes-The ability to obtain both protein and glycan measurements at the same capture antibodies (Fig. 1, a and b) enabled an exploration of the relationships between these levels. In particular, for the glycan measurements with altered levels in the cancer samples, we sought to determine whether the glycan structures were changing relative to the core protein levels or simply along with the protein levels. We calculated glycan:protein ratios by dividing each glycan measurement by its corresponding core protein measurement, and we compared the glycan:protein ratios between the cancer samples and the control samples. The core protein levels were measured using array-based sandwich assays (as depicted in Fig. 1a) for the mucins MUC1, MUC5AC, and MUC16 (Fig. 3a). All three had elevated levels in cancer (p ϭ 0.03, 0.008, and 0.001 for MUC1, MUC5AC, and MUC16, respectively; Mann-Whitney rank-sum test). The glycan levels, measured using detection by the jacalin lectin at each capture antibody (as depicted in Fig. 1b), were significantly elevated only on MUC5AC and MUC16 (Fig. 3b). In contrast, the glycan:protein ratios were elevated only for MUC5AC (Fig. 3c). This result indicates that the glycan target of jacalin was only elevated on MUC5AC, although all three core proteins showed cancerassociated elevations. The prevalence of the elevations, relative to healthy individuals, was estimated using a threshold set to the level of the second highest control subject (one false  FIG. 3. Glycan elevations relative to core protein elevations. a, core protein levels. Using an antibody sandwich assay (Fig. 1a), the protein levels of MUC1 (Ab CA 15-3 for capture and detection), MUC5AC (Ab 45M1 for capture and detection), and MUC16 (Ab x325 for capture and CA 125 for detection) were measured. Each gray diamond represents a sample from the healthy controls (H) or cancer patients (C). The box gives the upper and lower quartiles, the vertical lines give the ranges, and the horizontal line gives the median. The p value was calculated using the Mann-Whitney test, and the threshold for elevation in the cancer patients (dashed red line) is based on the second highest control subject. b, glycan levels at the same capture antibodies as in a using detection with the jacalin lectin. c, glycan:protein ratios. The glycan level for each sample in b was divided by the protein level of the corresponding sample and capture antibody in a. d, AUCs using the glycan:protein ratios. The AUC for discriminating cancer from control was calculated for each set of glycan:protein ratios, and the combined AUCs were hierarchically clustered. positive of 23, or 96% specificity). The prevalence of glycan elevations on MUC5AC (65%; Fig. 3b) was higher than the prevalence of MUC5AC protein elevations (35%; Fig. 3a), suggesting that some patients had glycan elevations on MUC5AC independent of protein elevations.
Similar comparisons were made for all the glycan:protein ratios. The AUC for the discrimination of cancer from control was calculated using each glycan:protein ratio (Fig. 3d) such that a high AUC indicates a cancer-associated elevation in the glycan relative to the protein. Certain ratios had AUCs near or above 0.8 and had significant (p Ͻ 0.05; Mann-Whitney test) elevations in cancer. The most prevalent elevation was the CA 19-9 on MUC1 (15 of 23 patients, or 65%), although MUC5AC showed the greatest number of different cancer-associated elevations. The pattern of cancer-associated glycan alterations on MUC5AC was clearly different from both MUC1 and MUC16. MUC1 shared elevations in the CA 19-9 and the glycan target of GSL-I but was missing elevations in the glycan targets of others, such as jacalin and AAL. MUC16, in contrast, showed no significant elevations in glycan:protein ratios.
Discrimination of Cancer from Control Using Glycan Detection-Next we examined whether it was possible to achieve more accurate discrimination of cancer from control using glycan measurements relative to using protein measurements. This question relates to whether glycan and protein elevations occur independently or together in the same patients. If glycan and protein elevations occur together in the same patients, minimal additional discrimination of cancer from control would be achieved using glycan detection. A comparison of the CA 19-9 on MUC5AC with the MUC5AC protein levels shows that 10 (45%) of the patients had glycan elevations without protein elevations (Fig. 4a). Of the 10 patients with elevations in the glycan:protein ratio, four also had protein elevations. Therefore, for this glycan on MUC5AC, elevation can occur independently of protein elevation but also coincide with protein elevation. A similar relationship was observed for the CA 19-9 on MUC1 (Fig. 4a). For MUC16, the strong correlation between the glycan level and the protein level indicates that the CA 19-9 is rarely altered in cancer relative to the core protein level (Fig. 4a).
This result indicates that, for certain proteins and glycans, the measurement of glycans on proteins could provide better cancer detection than just measuring the core protein levels. ROC curves comparing protein detection with glycan detection show that, in cases where the glycan can be up-regulated independently of the protein, better discrimination of cancer from control is achieved using glycan measurements (Fig. 4b).
At a threshold of 96% specificity (one false positive of 23 control subjects), 18 of 23 (78%) and 19 of 23 (83%) cancer subjects showed elevations of CA 19-9 on MUC5AC and MUC1, respectively. For MUC16, because glycans are not elevated when the protein is not elevated, no improvement in discrimination was observed.

Structural Insights from Lectin Binding Profiles-Knowl-
edge about the binding specificities of each lectin can provide insights into the similarities and differences between the normal and cancer-associated glycan structures. For each capture antibody, we organized the detection lectins and antibodies according to their primary specificities, and we examined the overall discrimination of cancer from control using both the total glycan level and the glycan:protein ratio (Fig. 5). An elevated glycan level indicates that the glycan was present on the protein in the cancer patients but not necessarily elevated relative to the core protein level, and an elevated glycan:protein ratio indicates that a particular glycan was elevated relative to the core protein. Notable differences and similarities were observed among the three mucins. The most consistent elevation in the glycan:protein ratio was in fucose with even MUC16 showing elevations using the lectin AAL. MUC5ac showed evidence of elevations in Gal-GalNAc, which forms the O-glycan core 1 structure, known as the TF Disciminating cancer from control using glycan or protein measurements. a, correlations between protein and glycan levels. Glycan levels are plotted on the horizontal axes, and corresponding protein levels are plotted on the vertical axes for the indicated proteins and glycans. The dashed lines represent thresholds based on the second highest control sample in each dimension. Each point represents a sample from the indicated patient group. The red ovals indicate samples with elevated glycan:protein ratios determined using the same calculation as in Fig. 3c. The values were log-transformed (base 10). b, comparison of biomarker performance using protein and glycan levels. Receiver-operator characteristic curves were generated using either the protein measurements (smooth curve) or a glycan measurement on the corresponding protein (solid diamonds) for the indicated capture antibodies and detection reagents. C, capture; D, detection.
antigen. MUC1 displayed the TF glycan in cancer but not strong elevations relative to the protein levels. Gal-GlcNAc disaccharides, which are characteristic of extended chains on both N-glycans and O-glycans, were found most strongly on MUC16 in cancer but may have been elevated most on MUC1. Terminal GalNAc, characteristic of truncated O-glycans displaying the Tn antigen, was present but not strongly elevated in cancer relative to the core protein as was terminal and poly(GlcNAc). Terminal mannose was found on all three mucins and was elevated relative to the protein level on MUC1 and MUC5AC. This result was unexpected because O-glycosylation, which does not contain mannose, is the main type of glycosylation found on mucins, and cancer-associated alterations to N-glycans on mucins were not previously recognized.

DISCUSSION
Despite repeated observations of cancer-associated modifications to structures of carbohydrate chains, the prevalence of specific glycan alterations and the relationships to their carrier proteins have not been characterized. Such information would be useful for determining potential involvement in cancer processes or usefulness for cancer detection. We have shown here the profiling of selected glycans and carrier proteins using a novel antibody-glycan sandwich array method, resulting in the characterization of the rate of alterations to particular glycans on MUC1, MUC5AC, and MUC16.
The results revealed significantly different behaviors between MUC1, MUC5AC, and MUC16. MUC16 was the most frequently elevated at the core protein level (in 65% of the patients), but it showed few glycan alterations. MUC5AC was elevated at the protein level (in 35% of the patients) and had the most glycan alterations, whereas MUC1 was weakly ele-vated and showed a few highly prevalent glycan alterations. The clear differences between the mucins in their regulation of glycosylation suggest distinct functions for these mucins and their glycans in cancer.
By running the assay either for protein detection or glycan detection, we were able to explore the relationships between those levels. The glycan alterations on MUC1 and MUC5AC occurred independently of protein elevations so that some patients showed glycan elevations without protein elevations and other patients showed the opposite trend. The most prevalent elevation in the glycan:protein ratio, the CA 19-9 on MUC1, occurred in 65% of the patients compared with a largely non-overlapping 35% of the patients with elevations in MUC1 core protein. These two behaviors might represent differential responses of cancers, either releasing greater amounts of mucins without altering the glycans or instead releasing less mucin but increasing a particular glycan epitope.
Because of the complementary relationship between protein and glycan elevations, measuring the glycan on the specific protein resulted in detecting a higher percentage of patients (83% for CA 19-9 on MUC1) relative to measuring just the protein. This fact points to the possible future usefulness of similar assays for cancer detection as suggested before in studies of fucosylated ␣-fetoprotein (27) and haptoglobin (7,8) for identifying liver and pancreatic cancers. Validation of potential markers will involve studies on larger, blinded sample sets including samples from conditions that might potentially give elevations, such as benign liver diseases and inflammatory states of the pancreas. In addition, it will be important to develop protein standards for potential markers. Standards are useful for calibrating the assays to achieve quantitative, absolute measurements and for comparing results between platforms and laboratories. The development of standards for glycan measurements on particular proteins is challenging because both the glycan and the protein must be well characterized so that task likely will be undertaken once the most valuable biomarkers have been identified and their glycan structures have been characterized. Future biomarker work may focus on glycan alterations of MUC5AC, which was not previously recognized as a serological marker for pancreatic cancer perhaps because the protein level alone did not provide good cancer detection. Because MUC5AC is not expressed in the normal pancreas but shows expression in premalignant pancreatic intraepithelial neoplasia lesions (29,30), it could have value for early detection if glycan structures can be found that are unique to incipient cancer.
Knowledge of the specificities of the lectins and detection antibodies provided insights into the nature of the structural alterations associated with cancer. The most frequent elevations involved fucose, the CA 19-9, the TF antigen (Gal␤1,3GalNAc), and terminal mannose. The elevation of the TF antigen relates to previous observations of truncated O- FIG. 5. Cancer associations of major structural features. The AUCs for discriminating cancer from control calculated using either glycan measurements or glycan:protein ratios are presented for each mucin and each detection reagent. The detection reagents are organized according to primary specificities, although some specificities are more complex or diverse than indicated here. See Table I for lectin  abbreviations. glycans on MUC1 expressed by cancer cells, resulting in the exposure of the TF and Tn (GalNAc-O-Ser/Thr) antigens (1,23,(31)(32)(33)(34)(35). The exposure of these antigens has been found in the tissue of about 90% of all carcinomas (36), whereas we found it on MUC5AC in 65% of the cancer patients (using jacalin detection). The prevalence in blood may be less than that in tissue because of incomplete release into the circulation. Our results suggested that both the TF and Tn antigens were present in cancer on all three mucins, but just the TF antigen was elevated relative to the core protein level. A dominance of the TF over the Tn antigen in pancreatic cancer, which was not previously noted, could indicate functional importance of the TF antigen. The increased exposure of the TF antigen can lead to increased stimulation through the c-Met or mitogen-activated protein kinase (MAPK) pathways (37) by endogenous lectins or lectins of bacterial or nutritional origin as in colon cancer (38,39). Galectin-3 is an endogenous lectin that can bind TF antigen on MUC and enhance the adhesion of cancer cells to endothelial cells possibly by the clustering of MUC1 to reveal underlying adhesion molecules (40). The increased exposure of the TF antigen on MUC5AC was not observed previously and could indicate a broader presentation of that epitope than previously appreciated.
The increased binding of the CA 19-9 antibody indicates increased levels of the sialyl-Lewis A carbohydrate antigen on MUC1 and MUC5AC. The Lewis structures are blood group carbohydrate antigens (antigens found on red blood cells whose structures are determined by genetic variants) on the termini of both O-and N-glycans. Elevated sialylated Lewis epitopes on mucins were observed previously on MUC1 (15,33,41), and here we found elevations also on MUC5AC. The Lewis antigens are ligands for selectin receptors on lymphocytes and endothelial cells (31) so the increased presence of Lewis antigens on the surfaces and secretions of cancer cells has implications for the ability of cancer cells to enter and exit the vasculature (42).
Because the Lewis antigens contain ␣1,3or ␣1,4-linked fucose, their elevation could relate to the elevated binding of the lectin AAL, which targets fucose and which showed strong binding on all three mucins. Another lectin that targets fucose, Ulex europaeus agglutinin, is specific to ␣1,2-linked fucose and was only elevated on MUC1 and MUC5AC. ␣1,2-Linked fucose is typically found at the core GlcNAc of N-glycans, suggesting alterations to N-glycans on MUC1 and MUC5AC. Generally increased levels of fucose groups, independent of Lewis antigens, have been seen in a variety of conditions, particularly on the protein haptoglobin (8).
An unexpected result was the elevated binding of the lectins concanavalin A and Lens culinaris agglutinin on MUC1 and MUC5AC. The mannose structures targeted by these lectins are typically found on N-glycans, which are present on mucins (43) but were not known to have cancer-associated alterations on mucins. This possibility of altered N-glycans is consistent with the observation noted above of altered 1,2linked fucose. Therefore, N-glycans as well as O-glycans may play roles in modifying the behaviors of MUC1 and MUC5AC in disease. Recently high mannose structures were found to be immunogenic cancer antigens on melanoma cells (44). Future studies could explore the functions of these various glycan structures found on secreted mucins in cancer patients.
Lectin binding patterns do not give precise information on glycan structures so it will be necessary in future studies to relate these data to results from other structural analysis methods, including mass spectrometry analyses or complementary technologies such as lectin arrays (17) or glycoprotein arrays (22). It also will be valuable to determine whether increases in certain structures are due to the glycosylation of an increased number of glycan sites, a possibility suggested by a previous study of MUC1 (45), or a shift in structures on the same number of sites.
Several factors might contribute to the glycan alteration or affect the lectin binding. Variation exists between individuals in native glycosyltransferase activities or abilities to elaborate certain structures, for example the blood group antigens on red blood cells. Certain individuals have genetic variants that prevent the addition of ␣1,3or ␣1,4-linked fucose on secreted proteins and thus would not be expected to produce elevated blood levels of the Lewis antigens (46). The individuals in this study that were negative for CA 19-9 elevations may represent those genotypes. Other glycans potentially could be used to complement CA 19-9, a strategy previously attempted using various related monoclonal antibodies (47). However, only limited detection of CA 19-9-negative patients was achieved. Similarly we did not find any glycans that significantly complemented CA 19-9 as certain patients (four of 23; 17%) were negative for all markers tested. An important research goal will be to improve the sensitivity of diagnostic assays by identifying markers that are complementary to CA 19-9 while achieving high specificity.
Another factor potentially influencing the assay is the fact that the capture antibody affinities might be affected by glycosylation status as observed for several MUC1 antibodies (48). Among the MUC1 antibodies used here, the VU-1101 clone is insensitive to the addition of a GalNAc or a Gal␤1,3GalNac to its peptide epitope (49); the SM3 clone has a preference for an epitope with short, non-branched Oglycan chains (known as the "core 1" structure) (50); and the sensitivity to glycosylation of the CA 15-3 antibody used for much of the analysis is not known. The main MUC5AC antibody used here (the 45M1 clone) binds an epitope in one of the cysteine-rich domains of MUC5AC (28) that is not heavily glycosylated, although antibody binding is affected by reduction (28), indicating the importance of the secondary structure. This diversity in antibody behavior highlights the value of testing multiple antibodies in a multiplexed setting. It also shows the need to regard each antibody as its own assay that measures a subset of all molecules of that type. It will be important to characterize the isoforms and glycan structures bound by particular antibodies to better determine which structures are most strongly associated with cancer.
In summary, the application of this new method to the study of sera from pancreatic cancer patients resulted in the characterization of the prevalence and nature of certain glycan alterations and their potential use for pancreatic cancer diagnostics. We identified clear differences between the mucins with MUC16 showing the strongest protein elevation but no glycan alterations, MUC5AC showing the most glycan alterations, and MUC1 showing slight protein elevations with selected, highly prevalent glycan alterations. The most prevalent glycan alterations were fucose, Lewis structures, the TF antigen, and, unexpectedly, terminal mannose. Because certain glycans on MUC1 and MUC5AC were altered independently of core protein elevations, the measurement of those glycans on the core proteins resulted in improved cancer detection relative to the measurement of core proteins alone. Future studies will build upon these findings to further characterize glycan alterations in pancreatic cancer and develop their uses for patient care. This technological approach should be useful for additional studies in biomarker discovery or basic glycobiology.