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), a type of pancreatic cyst. 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. Supported by preliminary data, 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: A global proteomics and metabolomics study of pancreatic cyst fluid in 160 IPMN surgical patients will be conducted to identify proteins and metabolites associated with high-grade and invasive IPMN. 1B: Top proteins identified from 1A and 6 proteins (THBS2, PGE2, LRG1, TIMP1, C1RL, & PTPRJ) discovered in our preliminary studies will be measured in serial plasma (n=3) and cyst fluid (n=~2.5) samples from 500 IPMN patients under surveillance. 1C: Top metabolites identified from 1A and 4 plasma metabolites correlated with IPMN dysplasia grade in our R21 study will be quantified in the 1B population. The levels and trajectories of proteins and metabolites measured in 1B and 1C will be evaluated in relation to IPMN malignant progression. 1D: A risk prediction model for IPMN malignant progression will be built from proteins and metabolites identified and validated in 1B and 1C, CA 19-9, and clinical/imaging features. 2. Evaluate levels and trajectories of plasma proteomic biomarkers and metabolomics signatures for detection of earlystage PDAC in a PRoBE-compliant case-control study nested in the PLCO cohort. 2A: proteins identified in 1A and 6 biomarkers listed in 1B will be measured in serial prediagnostic plasma samples (n=up to 3) from 242 PDAC cases (incl. 80 early-stage cases) and 242 matched controls. 2B: In the 2A population, top metabolites identified from 1A, 4 metabolites described in 1C, and 5 metabolites predicting early-stage PDAC in our pilot studies will be determined. 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/imaging features. Our expected results will allow clinicians to timely resect IPMNs with high malignant potential before progression to invasive cancer, while avoiding unnecessary surgeries. Detecting early-stage PDAC will substantially increase patient survival.