![Michael DeLong](/wp-content/uploads/2023/11/2023_seedgrant_Michael-Delong.jpg)
Associate Professor
Department of Surgery
University of California, Los Angeles
Los Angeles, CA
![O. Joe Hines](/wp-content/uploads/2016/06/OscarJoeHinesMD.jpg)
O. Joe Hines, MD
Professor and Chair
Department of Surgery
University of California, Los Angeles
Los Angeles, CA
Overview
Aim: Early Detection
Pancreatic ductal adenocarcinoma (PDAC) is a highly malignancy with a dismal 6% 5-year survival rate and is expected to become the second leading cause of cancer mortality in the United States in the next decade. The high mortality rate of PDAC is most directly related to the fact that over 80% of PDAC patients are diagnosed with late-stage disease with palliative treatment options only. A screening method to detect PDAC in its early curable stages would be the most impactful advancement in the management of pancreatic cancer. Considerable efforts to develop a cost effective, reliable, and generally applicable test for PDAC has been unsuccessful thus far. In this pilot proposal, we plan to use a novel technology that captures three levels of high definition omic data in patient blood (lipidomic, proteomic, and metabolomic) of patients with metastatic PDAC, resectable PDAC, and healthy controls in a streamlined, cost effective, one-shot assay. Downstream analysis will incorporate supervised machine learning algorithms to determine whether consistent differences among these patients can be reliably detected, with the goal of ultimately developing an inexpensive, noninvasive, focused multi-omic PDAC screening panel that would change the landscape of managing this deadly disease.