Specific protein glycoforms may be uniquely informative about the pathological state of a cyst and may serve as accurate biomarkers. Here we tested that hypothesis using antibody-lectin sandwich arrays in broad screens of protein glycoforms and in targeted studies of candidate markers. We profiled 16 different glycoforms of proteins captured by 72 different antibodies in cyst fluid from mucinous and nonmucinous cysts (n ‫؍‬ 22), and we then tested a three-marker panel in 22 addition samples and 22 blinded samples. Glycan alterations were not widespread among the proteins and were mainly confined to MUC5AC and endorepellin. Specific glycoforms of these proteins, defined by reactivity with wheat germ agglutinin and a blood group H antibody, were significantly elevated in mucinous cysts, whereas the core protein levels were not significantly elevated. A three-marker panel based on these glycoforms distinguished mucinous from nonmucinous cysts with 93% accuracy (89% sensitivity, 100% specificity) in a prevalidation sample set (n ‫؍‬ 44) and with 91% accuracy (87% sensitivity, 100% specificity) in independent, blinded samples (n ‫؍‬ 22). Targeted lectin measurements and mass spectrometry analyses indicated that the higher wheat germ agglutinin and blood group H reactivity was due to oligosaccharides terminating in GlcNAc or N-acetyl-lactosamine with occasional ␣1,2-linked fucose. The results show that MUC5AC and endorepellin glycoforms may be highly specific and sensitive biomarkers for the differentiation of mucinous from nonmucinous pancreatic cysts. Molecular & Cellular

Summary The glycan array is a powerful tool for investigating the specificities of glycan-binding proteins. By incubating a glycan-binding protein on a glycan array, the relative binding to hundreds of different oligosaccharides can be quantified in parallel. Based on these data, much information can be obtained about the preference of a glycan-binding protein for specific subcomponents of oligosaccharides, or motifs. In many cases the analysis and interpretation of glycan array data can be time consuming and imprecise if done manually. Recently we developed software, called GlycoSearch, to facilitate the analysis and interpretation of glycan array data based on the previously developed methods called Motif Segregation and Outlier Motif Analysis. Here we describe the principles behind the software, the use of the software, and an example application. The automated, objective, and precise analysis of glycan array data should enhance the value of the data for a broad range of research applications.

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.

The early detection of a portion of pancreatic cancer precursors_cystic neoplasms of the pancreas_is possible through high-resolution abdominal imaging, providing an opportunity to successfully treat these lesions before they become invasive. The increased use of abdominal imaging has led to a higher rate of identifying pancreatic cysts, but currently it is not possible to accurately determine which cysts have high malignant potential and should be removed. Our previous work showed that the glycosylation and abundance of specific proteins in the cyst fluid was significantly different between potentially malignant cysts and benign cysts, providing leads for biomarkers to guide patient management. In this work we tested the hypothesis that a panel of specific protein glycoforms forms an accurate biomarker for pancreatic cysts with high malignant potential. Using antibody-lectin sandwich arrays, screens of nearly 100 different candidate markers and multiple glycoforms of each marker confirmed previous results and also showed that two distinct glycan structures are present on several proteins at high levels in the cyst fluid of cancer precursors (intraductal pancreatic mucinous neoplasms (IPMN) and mucinous cystic neoplasms (MCN)) but not in benign cysts (serous cystadenomas (SC) and pseudocysts (PC)). The glycans, N-acetyl glucosamine and the blood group H structure, previously have not been recognized as cancer biomarkers. A six-marker panel comprising the measurement of one of these glycans on MUC5AC, MUC16, endorepellin, and fibronectin correctly detected 37 of 39 (95%) potential cancer precursors and 16 of 18 (89%) of the benign cysts. This performance significantly surpasses current diagnostic accuracy. The validation of this biomarker panel could lead to more accurate treatment decisions for patients with potentially malignant cysts while sparing patients with benign cysts from unnecessary procedures. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4555. doi:1538-7445.AM2012-4555