An Integrated Approach Utilizing Liquid Separations, Protein Microarrays and Tandem Mass Spectrometry Towards Understanding Phosphorylation, Glycosylation and Humoral Response Changes in Cancer.

Abstract

The development and application of an integrated approach that utilizes liquid separations, protein microarrays and mass spectrometry with the goal of understanding breast, pancreatic and colon cancer progression is described. In order to understand breast cancer progression, two liquid separation techniques for the generation of protein microarrays from pre-malignant and malignant breast cancer cell lines which were subsequently probed for phosphoproteins was pursued. Out of 140 positively phosphorylated array spots, 85 were differentially phosphorylated. This corresponded to 75 unique proteins. 51 phosphorylation sites were identified by tandem MS/MS in 27 of these proteins. Phosphorylation changes were dominant in proteins involved in transcriptional and translational control as well as apoptosis and cytoskeletal regulation. A strategy for global glycoprotein profiling in serum and plasma was also developed. Glycoprotein microarrays of enriched and separated N-linked glycoproteins from serum and plasma from normal controls and patients with pancreatic cancer and colorectal cancer were generated. The specific sugar moieties on glycans were assessed using biotinylated lectins coupled with Alexa-flor conjugated streptavidin. In both cancers sialylation and fucosylation changes were dominant. Principal component analysis and hierarchical clustering analysis showed that this strategy successfully classified serum/plasma groups based on overall glycan expression profiles. The utility of protein microarrays for identifying potential markers of pancreatic cancer was also explored. Proteins from MIAPACA, a pancreatic cancer cell line, were fractionated in 2 dimensions and arrayed on nitrocellulose slides. The slides were hybridized with multiple serum samples from normal controls and patients diagnosed with pancreatic cancer in order to find specific cancer proteins that elicited an antigen-antibody response to the serum samples. Wilcoxon rank sum tests followed by the PAM classification algorithm highlighted 9 fractions that together classified serum samples with 87% accuracy and sensitivity and specificity of 93% and 80% respectively. Further validation experiments with recombinant proteins showed good correlation for two of the identified proteins, phosphoglycerate kinase and histone H4. A modified arraying technique utilizing CNBr digestion was also successful, suggesting that exposure of binding sites to serum during hybridization is critical in maximizing the sensitivity of the microarray.