Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the deadliest form of pancreatic cancer. The lifetime risk of an individual in the United States developing pancreatic cancer is low, at an estimated 1. 7%, but the 5-year survival rate is a dismal 13%. High-risk individuals (HRIs) possess a heightened predisposition to the development of PDAC due to an inherited deleterious germline variant or due to a strong family history of pancreatic cancer. Pancreatic surveillance of HRIs leads to the detection of pancreatic precancerous abnormalities and early-stage cancer with improved survival. Most PDACs derive from microscopic precancerous lesions called pancreatic intraepithelial neoplasia, or PanIN. PanINs are undetectable with current non-invasive diagnostic tools. The absence of a reliable method to non-invasively assess PanIN burden complicates clinical decision-making, particularly regarding the timing of surgical intervention in HRIs. A deeper understanding of PanIN biology across diverse high-risk populations could improve patient outcomes by refining screening criteria and treatment guidelines. In this study, we employ a novel AI-powered digital pathology pipeline to analyze the organization of resected pancreata comparing familial and germline cases to age-, BMI-, and sex-matched controls. We assess histological sections of resected pancreata from 45 HRI patients who had surgery for suspected neoplasms and 90 matched controls who had pancreatic resection for non-ductal pancreatic conditions. By training a deep-learning algorithm to recognize 11 key pancreatic microanatomical features (including PanINs) in pathology slides from surgical resections, we find that HRIs contain a higher average neoplastic burden, higher interlobular fat, stromal and immune content, and lower acinar composition, reflecting acinar atrophy, fatty replacement, and fibrosis, possibly secondary to pancreatic ductal blockage due to PanINs. In addition to significantly greater PanIN burden than matched controls, PanIN from HRI were more likely to have high-grade dysplasia compared to matched controls. Notably, familial HRIs demonstrated a higher PanIN burden and higher pancreatic fat and fibrosis than germline variant carriers. Overall, HRIs displayed increased proportions of PanINs, adipose tissue, fibrosis and parenchymal atrophy, contributing to pronounced pancreatic tissue heterogeneity. Through ongoing molecular profiling using transcriptomic, proteomic and spatially-guided DNA sequencing, we aim to reveal relevant mechanisms of pancreatic tumorigenesis in high-risk populations. Citation Format: Lucie Dequiedt, Zhiyuan Ding, Brian A. Pedro, Youran Li, William Dhana, Kurtis Campbell, Mulan Bell, Courtney Cannon, Valentina Matos-Romero, Hassan Sinan, Ali Dbouk, Elizabeth Abou Diwan, Mohamad Dbouk, Ralph H. Hruban, Won Jin Ho, Luciane T. Kagohara, Michael G. Goggins, Denis Wirtz, Laura D. Wood, Marcia I. Canto, Ashley L. Kiemen. Quantitative, multiplex assessment of the pancreatic microenvironment in individuals at high-risk of pancreatic cancer reveals differences in tumorigenesis abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85 (18Suppl₃): Abstract nr A046.
Dequiedt et al. (Sun,) studied this question.
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