The current best hope for the successful treatment of pancreatic cancer is the removal of pre-invasive lesions before they become malignant. The early detection of a portion of pancreatic cancer precursors- cystic neoplasms of the pancreas-is possible through high-resolution abdominal imaging. The increased use of abdominal imaging has led to a higher rate of identifying pancreatic cysts, but currently it is not possible to accurately determine which cysts have high malignant potential and should be removed. New biomarkers that could assist that determination would lead to more successful outcomes for patients with pancreatic cysts. Therefore, the goal of this research is to develop biomarkers for distinguishing pancreatic cysts with high malignant potential from those with low malignant potential. Our overall hypothesis is that the expression and glycosylation of specific proteins are significantly different between cysts with high malignant potential and cysts with low malignant potential, and that these molecules form accurate biomarkers for the diagnosis of pancreatic cysts. We have a comprehensive strategy for biomarker discovery, refinement, and validation. Multiple clinical sites will contribute the medical expertise and the high-quality sample sets, and experienced statistical expertise in biomarker research will guide the experimental design for biomarker discovery, pre-validation, and validation. Our technological strategy is built on the powerful combination of novel glycoproteomics biomarker discovery methods and complementary antibody array methods for the high-throughput and precise profiling of multiple protein and glycan candidates. Through the iterative characterization and testing of biomarker isoforms and glycoforms, the performance of the best candidate biomarkers will be refined and improved. Pivotal double-blind validation studies will provide accurate assessments of biomarker performance. The success of this project will result in biomarkers to be validated in clinical settings;high-quality sample sets to be used in ongoing EDRN-associated discovery and validation studies;and an improved understanding of the molecular alterations associated with pancreatic cystic neoplasms.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA152653-03
Application #
8293341
Study Section
Special Emphasis Panel (ZCA1-SRLB-C (M1))
Program Officer
Rinaudo, Jo Ann S
Project Start
2010-08-17
Project End
2015-06-30
Budget Start
2012-08-01
Budget End
2013-06-30
Support Year
3
Fiscal Year
2012
Total Cost
$224,231
Indirect Cost
$139,883
Name
Van Andel Research Institute
Department
Type
DUNS #
129273160
City
Grand Rapids
State
MI
Country
United States
Zip Code
49503
Tang, Huiyuan; Partyka, Katie; Hsueh, Peter et al. (2016) Glycans related to the CA19-9 antigen are elevated in distinct subsets of pancreatic cancers and improve diagnostic accuracy over CA19-9. Cell Mol Gastroenterol Hepatol 2:201-221.e15
Singh, Sudhir; Pal, Kuntal; Yadav, Jessica et al. (2015) Upregulation of glycans containing 3' fucose in a subset of pancreatic cancers uncovered using fusion-tagged lectins. J Proteome Res 14:2594-605
Tang, Huiyuan; Hsueh, Peter; Kletter, Doron et al. (2015) The detection and discovery of glycan motifs in biological samples using lectins and antibodies: new methods and opportunities. Adv Cancer Res 126:167-202
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Haab, Brian B; Huang, Ying; Balasenthil, Seetharaman et al. (2015) Definitive Characterization of CA 19-9 in Resectable Pancreatic Cancer Using a Reference Set of Serum and Plasma Specimens. PLoS One 10:e0139049
Tang, Huiyuan; Singh, Sudhir; Partyka, Katie et al. (2015) Glycan motif profiling reveals plasma sialyl-lewis x elevations in pancreatic cancers that are negative for sialyl-lewis A. Mol Cell Proteomics 14:1323-33
West, Matthew B; Partyka, Katie; Feasley, Christa L et al. (2014) Detection of distinct glycosylation patterns on human γ-glutamyl transpeptidase 1 using antibody-lectin sandwich array (ALSA) technology. BMC Biotechnol 14:101
Gbormittah, Francisca Owusu; Haab, Brian B; Partyka, Katie et al. (2014) Characterization of glycoproteins in pancreatic cyst fluid using a high-performance multiple lectin affinity chromatography platform. J Proteome Res 13:289-99
Gbormittah, Francisca O; Lee, Ling Y; Taylor, KyOnese et al. (2014) Comparative studies of the proteome, glycoproteome, and N-glycome of clear cell renal cell carcinoma plasma before and after curative nephrectomy. J Proteome Res 13:4889-900
Fallon, Brian P; Curnutte, Bryan; Maupin, Kevin A et al. (2013) The Marker State Space (MSS) method for classifying clinical samples. PLoS One 8:e65905

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