Abstract C058: A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions

Abstract Genetic or epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies, including pancreas cancer. Pinpointing genes affecting pancreas cancer risk holds promise for early detection, prevention, and effective therapies. Despite recent advances, linking enhancer elements to target genes and predicting the transcriptional outcome of enhancer dysfunction remain significant challenges. Using 3D chromatin assays, we generated an extensive enhancer interaction dataset for the human pancreas, spanning more than 20 donors and five major cell types, including both the exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating quantitative, genome-wide analysis of enhancer connectivity. Using the tree models, we developed a machine learning algorithm capable of estimating the impact of enhancer perturbations on cell-type specific gene expression in the human pancreas. Complementing this computational approach, we streamlined an experimental platform using CRISPR-interference and RNA-FISH coupled with high-throughput imaging to quantitatively measure the effect size of enhancer perturbations in primary human pancreas cells. Our enhancer tree models enabled functional annotation of genetic variants associated with pancreas diseases, revealing novel target genes in specific cell types. For pancreas cancer (PDAC), our data suggests a stronger association of disease susceptibility variants with acinar cells, even though ductal cells historically have been the focus of PDAC. This discovery linked three potentially functional variants associated with PDAC risk to acinar cell enhancers, revealing novel target genes to further investigation. By integrating genomics with mechanistic findings into enhancer function, our work offers a framework for understanding genetic basis of pancreas cancer disease risk. Citation Format: Li Wang, Songjoon Baek, Gauri Prasad, Thucnhi Truongvo, Jason Hoskins, Laufey Amundadottir, Efsun Arda. A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84 (17 Suppl₂): Abstract nr C058.