Diagnosis of pancreatic cancer using serum proteomic profiling

In the United States, mortality rates from pancreatic cancer (PCa) have not changed significantly over the past 50 years. This is due, in part, to the lack of early detection methods for this particularly aggressive form of cancer. The objective of this study was to use high-throughput protein profiling technology to identify biomarkers in the serum proteome for the early detection of resectable PCa. Using surface-enhanced laser desorption/ionization mass spectrometry, protein profiles were generated from sera of 49 PCa patients and 54 unaffected individuals after fractionation on an anion exchange resin. The samples were randomly divided into a training set (69 samples) and test set (34 samples), and two multivariate analysis procedures, classification and regression tree and logistic regression, were used to develop classification models from these spectral data that could distinguish PCa from control serum samples. In the test set, both models correctly classified all of the PCa patient serum samples (100% sensitivity). Using the decision tree algorithm, a specificity of 93.5% was obtained, whereas the logistic regression model produced a specificity of 100%. These results suggest that high-throughput proteomics profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.