Carbohydrate post-translational modifications on proteins are important determinants of protein function in both normal and disease biology. We have developed a method to allow the efficient, multiplexed study of glycans on individual proteins from complex mixtures, using antibody microarray capture of multiple proteins followed by detection with lectins or glycan-binding antibodies. Chemical derivatization of the glycans on the spotted antibodies prevented lectin binding to those glycans. Multiple lectins could be used as detection probes, each targeting different glycan groups, to build up lectin binding profiles of captured proteins. By profiling both protein and glycan variation in multiple samples using parallel sandwich and glycan-detection assays, we found cancer-associated glycan alteration on the proteins MUC1 and CEA in the serum of pancreatic cancer patients. Antibody arrays for glycan detection are highly effective for profiling variation in specific glycans on multiple proteins and should be useful in diverse areas of glycobiology research.

We used antibody microarrays to probe the associations of multiple serum proteins with pancreatic cancer and to explore the use of combined measurements for sample classification. Serum samples from pancreatic cancer patients (n = 61), patients with benign pancreatic disease (n = 31), and healthy control subjects (n = 50) were probed in replicate experiment sets by two-color, rolling circle amplification on microarrays containing 92 antibodies and control proteins. The antibodies that had reproducibly different binding levels between the patient classes revealed different types of alterations, reflecting inflammation (high C-reactive protein, A-1-antitrypsin, and serum amyloid A), immune response (high IgA), leakage of cell breakdown products (low plasma gelsolin), and possibly altered vitamin K usage or glucose regulation (high protein-induced vitamin K antagonist-II). The accuracy of the most significant antibody microarray measurements was confirmed through immunoblot and antigen dilution experiments. A logistic-regression algorithm distinguished the cancer samples from the healthy control samples with a 90% and 93% sensitivity and a 90% and 94% specificity in duplicate experiment sets. The cancer samples were distinguished from the benign disease samples with a 95% and 92% sensitivity and an 88% and 74% specificity in duplicate experiment sets. The classification accuracies were significantly improved over those achieved using individual antibodies. This study furthered the development of antibody microarrays for molecular profiling, provided insights into the nature of serum-protein alterations in pancreatic cancer patients, and showed the potential of combined measurements to improve sample classification accuracy. (Cancer Res 2005; 65(23): 11193-202)

The measurements of coordinated patterns of protein abundance using antibody microarrays could be used to gain insight into disease biology and to probe the use of combinations of proteins for disease classification. The correct use and interpretation of antibody microarray data requires proper normalization of the data, which has not yet been systematically studied. Therefore we undertook a study to determine the optimal normalization of data from antibody microarray profiling of proteins in human serum specimens. Forty-three serum samples collected from patients with pancreatic cancer and from control subjects were probed in triplicate on microarrays containing 48 different antibodies, using a direct labeling, twocolor comparative fluorescence detection format. Seven different normalization methods representing major classes of normalization for antibody microarray data were compared by their effects on reproducibility, accuracy, and trends in the data set. Normalization with ELISAdetermined concentrations of IgM resulted in the most accurate, reproducible, and reliable data. The other normalization methods were deficient in at least one of the criteria. Multiparametric classification of the samples based on the combined measurement of seven of the proteins demonstrated the potential for increased classification accuracy compared with the use of individual measurements. This study establishes reliable normalization for antibody microarray data, criteria for assessing normalization performance, and the capability of antibody microarrays for serum-protein profiling and multiparametric sample classification. Molecular & Cellular Proteomics 4:773-784, 2005.