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,
alpha-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.