Jianjun Zhang

M.D., Ph.D.

Indiana University

Associate Professor

PLCO (Learn more about this study)

2020-0002

Apr 20, 2021

Longitudinal Proteomic and Metabolomic Predictors of Pancreatic Cyst Malignant Progression and Early Stage Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) has a dire prognosis mainly due to its late diagnosis. It is vital to identify early-stage PDAC and its precursors. One such precursor is intraductal papillary mucinous neoplasm (IPMN). International consensus guidelines recommend resection of IPMN with high malignancy risk and surveillance of IPMN without surgical indications. Based on radiologic/clinical findings, the guidelines have a dismal specificity for discerning benign from malignant IPMN and a poor accuracy of predicting IPMN malignant progression. It is urgent to identify biomarkers that predict malignant progression of presumed “low-risk” IPMN. The primary objective of the proposed study is to identify and validate protein and metabolite signatures and their longitudinal changes which can discriminate IPMN malignant progression and detect early-stage PDAC. Our central hypothesis is that the levels and trajectories of such signatures in plasma and/or pancreatic cyst fluid are predictive of IPMN malignant progression and early-stage PDAC. Specific Aims: 1. Investigate plasma and cyst fluid levels and trajectories of proteomic biomarkers and metabolomics signatures for prediction of IPMN malignant progression in a prospective surveillance cohort. 1A: Global proteomics and metabolomics analyses of pancreatic cyst fluid from 120 cases of resected low- and moderate-grade IPMN and 60 cases of resected high-grade and invasive IPMN to identify proteins and metabolites associated with high-grade and invasive IPMN. 1B: A prospective surveillance study of 500 presumed “low-risk” IPMN patients to evaluate and validate associations of baseline levels and trajectories of top-ranked proteins identified from 1A and 12 candidate proteins measured in serial plasma and cyst fluid samples with IPMN malignant progression. 1C: In the 1B population, top-ranked metabolites identified from 1A and 4 plasma metabolites correlated with IPMN dysplastic grade will be measured in plasma and cyst fluid samples collected at baseline and multiple time points to investigate and validate their associations with IPMN malignant progression. 1D: A risk prediction model for IPMN malignant progression will be built from proteins and metabolites identified and/or validated in 1A, 1B, and 1C, carbohydrate antigen 19-9 (CA 19-9), and clinical and imaging features. 2. Evaluate levels and trajectories of plasma proteomic biomarkers and metabolomics signatures for detection of early-stage PDAC in a PRoBE-compliant case-control study nested in the PLCO cohort. 2A: Levels and temporal changes of the top ranked proteins identified in 1A and the 12 biomarkers listed in 1B will be measured in serial prediagnostic plasma samples from 234 PDAC cases (including 76 early-stage disease) and 234 controls to evaluate and validate their associations with early-stage PDAC. 2B: In the 2A population, top-ranked metabolites identified from 1A, the 4 metabolites described in 1C, and 5 metabolites predictive of early-stage PDAC in our preliminary studies will be measured in serial prediagnostic plasma samples to investigate and validate the capacities of their levels and trajectories to discriminate early-stage PDAC from healthy controls. 2C: A risk prediction model for early-stage PDAC will be developed from proteins and metabolites identified in 2A and 2B, CA 19-9, and clinical and imaging features.