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

> **NIH VA IK2** · VA CONNECTICUT HEALTHCARE SYSTEM · 2023 · —

## Abstract

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,...

## Key facts

- **NIH application ID:** 10478374
- **Project number:** 1IK2BX005891-01
- **Recipient organization:** VA CONNECTICUT HEALTHCARE SYSTEM
- **Principal Investigator:** Louise L Wang
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2023
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-10-01 → 2027-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478374

## Citation

> US National Institutes of Health, RePORTER application 10478374, Pancreatic Cancer Risk Prediction: Integrating Individual-Level Clinical and Genetic Data (1IK2BX005891-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10478374. Licensed CC0.

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