Transcriptomic, Genomic, and Microenvironmental Heterogeneity in pancreatic precancer: subtyping, classification, and cross-comparison

Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer that arises from precancerous lesions, including intraductal papillary mucinous neoplasms (IPMNs) and pancreatic intraepithelial neoplasia (PanIN)1,2. IPMN lesions are highly prevalent in the aging pancreas and detectable on early abdominal imaging, with only some progressing to PDAC3,4. Early detection and differentiation of progressive versus indolent precancers for therapeutic intervention is critical to improving PDAC outcomes. My long-term objective is to elucidate the molecular mechanisms driving precancer progression and develop tools to stratify high-risk lesions to enable early intervention and improve patient outcomes. To identify high-risk lesions, the heterogeneity of IPMNs must first be characterized through the definition of robust, multi-omic phenotypes from matched genomic and transcriptomic data to uncover transcriptional programs and genomic drivers of dysplastic progression. To distinguish between cohort-specific, tissue- specific, and pan-precancer phenotypes, I propose the cross-comparison of phenotypes from different tissues via the development of a novel classification algorithm and the dissemination of results in a public, web-based platform. Finally, I propose to adapt an algorithm developed in my Ph.D. to quantify single-cell degree of differentiation to the study of dysplastic progression through further integrative multi-omic analysis of IPMN data, including epigenomic information. By identifying drivers of de-differentiation and dysplastic progression and developing algorithms to characterize the highly heterogenous IPMN landscape, this project addresses a critical gap in early detection and prevention of PDAC, a cancer with a 5-year survival rate below 5%2. The innovative integration of multi-omics, computational biology, and spatial modeling will yield biomarkers and therapeutic targets for early intervention and serve as the foundation for my independent research program in cancer data science. Project Number: 1K99CA312671-01 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Kathleen Noller | Institution: UNIVERSITY OF MARYLAND BALTIMORE, BALTIMORE, MD | Award Amount: $123,823 | Activity Code: K99 | Study Section: Special Emphasis Panel[ZRG1 CDPT-P (56)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11351245