Katie Partyka

Enhanced Discrimination of Malignant from Benign Pancreatic Disease by Measuring the CA 19-9 Antigen on Specific Protein Carriers

The CA 19-9 assay detects a carbohydrate antigen on multiple protein carriers, some of which may be preferential carriers of the antigen in cancer. We tested the hypothesis that the measurement of the CA 19-9 antigen on individual proteins could improve performance over the standard CA 19-9 assay. We used antibody arrays to measure the levels of the CA 19-9 antigen on multiple proteins in serum or plasma samples from patients with pancreatic adenocarcinoma or pancreatitis. Sample sets from three different institutions were examined, comprising 531 individual samples. The measurement of the CA 19-9 antigen on any individual protein did not improve upon the performance of the standard CA 19-9 assay (82% sensitivity at 75% specificity for early-stage cancer), owing to diversity among patients in their CA 19-9 protein carriers. However, a subset of cancer patients with no elevation in the standard CA 19-9 assay showed elevations of the CA 19-9 antigen specifically on the proteins MUC5AC or MUC16 in all sample sets. By combining measurements of the standard CA 19-9 assay with detection of CA 19-9 on MUC5AC and MUC16, the sensitivity of cancer detection was improved relative to CA 19-9 alone in each sample set, achieving 67–80% sensitivity at 98% specificity. This finding demonstrates the value of measuring glycans on specific proteins for improving biomarker performance. Diagnostic tests with improved sensitivity for detecting pancreatic cancer could have important applications for improving the treatment and management of patients suffering from this disease.

Identification of blood-protein carriers of the CA 19-9 antigen and characterization of prevalence in pancreatic diseases.

The current best serum marker for pancreatic cancer, CA 19‐9, detects a carbohydrate antigen on multiple protein carriers. Better knowledge of the protein carriers of the CA 19‐9 antigen in various disease states may lead to improved diagnostic tests. To identify proteins that carry the CA 19‐9 antigen, we immunoprecipitated the CA 19‐9 antigen from pooled sera and identified the associated proteins using MS. Among the high‐confidence identifications, we confirmed the presence of the CA 19‐9 antigen on Apolipoprotein B‐100 by antibody arrays and Western blot and on kininogen, ARVCF, and Apolipoprotein E by antibody arrays. We characterized the frequency and levels of the CA 19‐9 antigen on the four proteins across various patient groups (pancreatic cancer, pancreatitis, and healthy controls) using antibody arrays. Nearly, 10–25% of the subjects showed elevations of the antigen on each protein, but the elevations were not associated with disease state or total CA 19‐9 levels. These results contribute to our knowledge of the carrier proteins of an important functional glycan and the rate at which the glycan is displayed. This work also demonstrates a strategy for using the complementary methods of MS and antibody microarrays to identify protein carriers of glycans and assess the diagnostic value of measuring glycans on individual proteins.

Diverse monoclonal antibodies against the CA 19-9 antigen show variation in binding specificity with consequences for clinical interpretation

The CA 19‐9 antigen is currently the best individual marker for the detection of pancreatic cancer. In order to optimize the CA 19‐9 assay and to develop approaches to further improve cancer detection, it is important to understand the specificity differences between CA 19‐9 antibodies and the consequential affect on biomarker performance. Antibody arrays enabled multiplexed comparisons between five different CA 19‐9 antibodies used in the analysis of plasma samples from pancreatic cancer patients and controls. Major differences were observed between antibodies in their detection of particular patient samples. Glycan array analysis revealed that certain antibodies were highly specific for the canonical CA 19‐9 epitope, sialyl‐Lewis A, while others bound sialyl‐Lewis A in addition to a related structure called sialyl‐Lewis C and modification with Nue5Gc. In a much larger patient cohort, we confirmed the binding of sialyl‐Lewis C glycan by one of the antibodies and showed that the broader specificity led to the detection of an increased number of cancer patients without increasing detection of pancreatitis patient samples. This work demonstrates that variation between antibody specificity for cancer‐associated glycans can have significant implications for biomarker performance and highlights the value of characterizing and detecting the range of glycan structures that are elevated in cancer.

Modulation of Glycan Detection on Specific Glycoproteins by Lectin Multimerization

Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents such as lectins and glycan-binding antibodies is a valuable complement to methods involving mass spectrometry and chromatography. Many lectins, however, are not useful as analytic tools due to low affinity in vitro. As an approach to increasing lectin avidity to targeted glycans, we tested the use of lectin multimerization. Several biotinylated lectins were linked together through streptavidin interactions. The binding of certain lectins for purified glycoproteins and glycoproteins captured directly out of biological solutions was increased using multimerization, resulting in the detection of lower concentrations of glycoprotein than possible using monomeric detection. The analysis of glycoproteins in plasma samples showed that the level of binding enhancement through multimerization was not equivalent across patient samples. Wheat germ agglutinin (WGA) reactive glycans on fibronectin and thrombospondin-5 were preferentially bound by multimers in pancreatic cancer patient samples relative to control samples, suggesting a cancer-associated change in glycan density that could be detected only through lectin multimerization. This strategy could lead to the more sensitive and informative detection of glycans in biological samples and a broader spectrum of lectins that are useful as analytical reagents.

Prediction of glycan motifs using quantitative analysis of multi-lectin binding: Motifs on MUC1 produced by cultured pancreatic cancer cells.

Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs.

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.

A Gastric Glycoform of MUC5AC Is a Biomarker of Mucinous Cysts of the Pancreas

Molecular indicators to specify the risk posed by a pancreatic cyst would benefit patients. Previously we showed that most cancer-precursor cysts, termed mucinous cysts, produce abnormal glycoforms of the proteins MUC5AC and endorepellin. Here we sought to validate the glycoforms as a biomarker of mucinous cysts and to specify the oligosaccharide linkages that characterize MUC5AC. We hypothesized that mucinous cysts secrete MUC5AC displaying terminal N-acetylglucosamine (GlcNAc) in either alpha or beta linkage. We used antibody-lectin sandwich assays to detect glycoforms of MUC5AC and endorepellin in cyst fluid samples from three independent cohorts of 49, 32, and 66 patients, and we used monoclonal antibodies to test for terminal, alpha-linked GlcNAc and the enzyme that produces it. A biomarker panel comprising the previously-identified glycoforms of MUC5AC and endorepellin gave 96%, 96%, and 87% accuracy for identifying mucinous cysts in the three cohorts with an average sensitivity of 92% and an average specificity of 94%. Glycan analysis showed that MUC5AC produced by a subset of mucinous cysts displays terminal alpha-GlcNAc, a motif expressed in stomach glands. The alpha-linked glycoform of MUC5AC was unique to intraductal papillary mucinous neoplasms (IPMN), whereas terminal beta-linked GlcNAc was increased in both IPMNs and mucinous cystic neoplasms (MCN). The enzyme that synthesizes alpha-GlcNAc, A4GNT, was expressed in the epithelia of mucinous cysts that expressed alpha-GlcNAc, especially in regions with high-grade dysplasia. Thus IPMNs secrete a gastric glycoform of MUC5AC that displays terminal alpha-GlcNAc, and the combined alpha-GlcNAc and beta-GlcNAc glycoforms form an accurate biomarker of mucinous cysts.

Glycan Motif Profiling Reveals Plasma Sialyl-Lewis X Elevations in Pancreatic Cancers That Are Negative for Sialyl-Lewis A

The sialyl-Lewis A (sLeA) glycan forms the basis of the CA19–9 assay and is the current best biomarker for pancreatic cancer, but because it is not elevated in ∼25% of pancreatic cancers, it is not useful for early diagnosis. We hypothesized that sLeA-low tumors secrete glycans that are related to sLeA but not detectable by CA19–9 antibodies. We used a method called motif profiling to predict that a structural isomer of sLeA called sialyl-Lewis X (sLeX) is elevated in the plasma of some sLeA-low cancers. We corroborated this prediction in a set of 48 plasma samples and in a blinded set of 200 samples. An antibody sandwich assay formed by the capture and detection of sLeX was elevated in 13 of 69 cancers that were not elevated in sLeA, and a novel hybrid assay of sLeA capture and sLeX detected 24 of 69 sLeA-low cancers. A two-marker panel based on combined sLeA and sLeX detection differentiated 109 pancreatic cancers from 91 benign pancreatic diseases with 79% accuracy (74% sensitivity and 78% specificity), significantly better than sLeA alone, which yielded 68% accuracy (65% sensitivity and 71% specificity). Furthermore, sLeX staining was evident in tumors that do not elevate plasma sLeA, including those with poorly differentiated ductal adenocarcinoma. Thus, glycan-based biomarkers could characterize distinct subgroups of patients. In addition, the combined use of sLeA and sLeX, or related glycans, could lead to a biomarker panel that is useful in the clinical diagnosis of pancreatic cancer. Précis: This paper shows that a structural isomer of the current best biomarker for pancreatic cancer, CA19–9, is elevated in the plasma of patients who are low in CA19–9, potentially enabling more comprehensive detection and classification of pancreatic cancers.

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines “marker states” based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

Using Antibody Arrays to Measure Protein Abundance and Glycosylation: Considerations for Optimal Performance

Antibody arrays provide a valuable method for obtaining multiple protein measurements from small volumes of biological samples. Antibody arrays can be designed to target not only core protein abundances (relative or absolute abundances, depending on the availability of standards for calibration), but also posttranslational modifications, provided antibodies or other affinity reagents are available to detect them. Glycosylation is a common modification that has important and diverse functions in both normal and disease biology. Significant progress has been made in developing methods for measuring glycan levels on multiple specific proteins using antibody arrays and glycan‐binding reagents. This unit describes practical approaches for developing, optimizing, and using antibody array assays to determine both protein abundance and glycosylation state. Low‐volume arrays can be used to reduce sample consumption, and a new way to improve the binding strength of particular glycan‐binding reagents through multimerization is discussed. These methods can be useful for a wide range of biological studies in which glycosylation may change and/or affect protein function. Curr. Protoc. Protein Sci. 73:27.6.1‐27.6.16.