Detection and Diagnosis of Pancreatic Cancer

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DESCRIPTION (provided by applicant): Pancreatic cancer is one of the most insidious forms of cancer. Recent estimates of new cancer cases put pancreatic cancer at eighth and ninth among women and men, respectively. However, it is the fourth leading cause of all cancer deaths in both women and men, surpassed only by lung, colorectal, prostate, and breast cancers. Due largely to the location, and silent growth of the tumor, most patients present at a time when it is too late to undergo any realistic type of curative treatment. This has translated into a 5-year survival rate of less than 3%. In a recent report, "Pancreatic Cancer: An Agenda for Action", the NCI's Pancreatic cancer Progress Review Group drew specific attention to the urgent need for "better screening and diagnostic techniques" with a recommendation to "delineate and validate effective molecular biomarkers" for pancreatic cancer. With this in mind, we intend to develop and assess the utility of a PAM4, anti-MUC1 antibody-based immunoassay to detect, diagnose, and provide for the management of this disease. Although there have been several reports concerning anti-MUC1 MAbs reactive with several types of cancer, PAM4 appears to be unique, or nearly so, in terms of its specificity and the molecular characteristics of the epitope to which it is reactive. The long-term objective of this proposal is to identify specific biomarkers that can be combined with the PAM4 assay for enhanced diagnostic accuracy of pancreatic cancer. The specific aims of this proposal are Aim 1. To investigate the diagnostic accuracy of an immunoassay for the quantitation of PAM4-reactive MUC1 in biological fluids as a means for early detection and diagnosis of pancreatic cancer. Aim 2. To investigate the heterogeneity in expression of MUC1 epitope structures as a means to identify patterns that may be organ and/or tumor "specific/associated". Aim 3. To investigate whether inclusion of other mucin biomarkers can increase diagnostic accuracy through multivariate analyses.