Pancreatic Cancer Risk Prediction: Integrating Individual-Level Clinical and Genetic Data

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Pancreatic ductal adenocarcinoma (PDAC) is highly deadly (5-year survival rates <10%), in large part because most cases are diagnosed at advanced stages when the cancer is inoperable and medical therapies are limited. In particular, PDAC among Blacks are discovered at later stages with higher incidence and mortality. To date, PDAC screening has focused on individuals with hereditary predispositions, neglecting the 90% that is sporadic. Universal PDAC screening in asymptomatic adults has been deemed infeasible, motivating the quest for new screening approaches that target high risk individuals. Susceptibility to PDAC is determined by genetic factors and clinical exposures, but current risk prediction tools to identify these individuals are inadequate as they do not incorporate genetic data from a trans-ancestry population and fail to comprehensively integrate clinical and genetic risk factors. To address this problem, we propose to improve the prediction of PDAC risk by leveraging data from local, national, and international biobanks/datasets, including the VA Corporate Data Warehouse, VA Million Veteran Program, Penn Medicine BioBank, UK Biobank, and pancreatic cancer consortia. We hypothesize that the combination of refined genotypic and phenotypic data will improve identification of high-risk Veterans for PDAC over traditional risk factor-based approaches. Our specific aims are 1) develop an electronic health record-based phenotype algorithm for PDAC using structured and unstructured data, 2) identify Veterans in the general population at high risk for PDAC by integrating genetics into a clinical prediction model and 3) perform a trans-ancestry genome-wide association study (GWAS) for PDAC in the Million Veteran Program, meta-analysis with extant PDAC data, and the first GWAS for individuals of African ancestry. Successful completion of this project will not only make early detection of PDAC possible in the multi-ancestry general population of Veterans, but also improve the predictive capability of genetics to ultimately help bridge the disparities in PDAC-related morbidity and mortality seen in Blacks. These results will serve as the foundation for developing personalized strategies for screening in PDAC and help actualize the promise of precision medicine for improving survival in PDAC. This proposal details a 5-year plan to promote the independent career of Dr. Louise Wang as a physician scientist in genetic risk prediction for early detection of pancreatic cancer. Dr. Wang is a third-year gastroenterology fellow who will complete formal training in clinical epidemiology prior to the funding period and will continue training in genetics and bioinformatics to augment her technical skills in precision medicine. Her training will be facilitated through a comprehensive mentorship plan consisting of the following: 1) weekly to monthly meetings with her mentorship team, 2) formal coursework in programming to visualize biomedical and clinical data, statistical methods for analyzing genetic data, and advanced prediction modeling to integrate genetics into clinical prediction through the Center for Clinical Epidemiology and Biostatistics (CCEB), Genomics & Computational Biology (GCB) graduate group, and Department of Biostatistics, Epidemiology and Informatics (DBEI) at the University of Pennsylvania, 3) structured research seminars and national conferences, and 4) development of future grants during the latter portion of the award period. Her long-term goal is to identify Veterans at risk for gastrointestinal malignancies by leveraging electronic health record-linked biobanks to integrate genetic and clinical risk factors.