Abstract 3659: Enhancer profiling to identify novel drivers in the EMT subtype of gastric adenocarcinoma

Abstract

Abstract Recent large-scale omics characterization of gastric cancer (GC) has led to the delineation of distinct molecular subtypes with unique oncogenic mechanisms. Of particular interest is the consistent identification of an epithelial-to-mesenchymal (EMT) subtype, which is frequently associated with clinical aggressiveness and poor prognosis but lacks effective targeted treatment. Strikingly, this subtype has been observed to exhibit relatively high genomic integrity, suggesting the possible contribution of epigenomics and non-coding regulatory elements in driving oncogenic development. Transcriptional enhancers constitute a key class of non-coding DNA elements that regulate gene expression, especially in a tissue- or cell-type specific fashion. Importantly, dysregulated enhancer activation has been implicated in cancer pathogenesis, especially those occurring in close genomic proximity to form broad clusters known as super-enhancers (SEs). Therefore, the current study adopted the approach of subtype-specific enhancer and SE profiling in an effort to reveal novel vulnerabilities of the mesenchymal-like (Mes-like) subtype of GC. Using genome-wide H3K27ac chromatin immunoprecipitation (ChIP) sequencing of in vitro GC cell lines and primary fresh-frozen GC tissues, we sought to define the enhancer landscape specific to the Mes-like subtype. In view of recent studies that have highlighted the possibility of major stromal contribution to the mesenchymal/EMT phenotype, we employed the use of purely epithelial cell lines as our discovery cohort. We observed through unsupervised hierarchical clustering that the Mes-like cell lines shared a distinct enhancer and SE landscape. To support their clinical relevance, we confirmed that enhancers activated preferentially in Mes-like cell lines displayed correspondingly higher H3K27ac signals in Mes-like primary GC tissues compared to their epithelial-like (Epi-like) counterparts. We then focused our analysis on recurrent Mes-like associated SE regions, given the previously established strong association of SEs with key cell identity regulators and oncogenes. To evaluate the potential biological implications of recurrent Mes-like SEs, we employed the GREAT algorithm to predict enhancer-gene interactions, followed by various gene set enrichment analyses using MSigDB. Consistent with our knowledge of the Mes-like subtype, target genes of recurrent Mes-like SEs were enriched in phenotype-relevant signatures, including genes up-regulated in tissue stem cells, genes up-regulated in advanced GC relative to early GC and genes involved in locomotion and biological adhesion. Our preliminary results suggest that enhancer characterization could prove very useful in understanding the transcriptional circuitry underlying Mes-like features in GC. Future studies will evaluate the role of specific SEs and their downstream gene targets in an effort to uncover novel oncogenes or non-coding drivers. Citation Format: Shamaine Ho, Patrick Tan. Enhancer profiling to identify novel drivers in the EMT subtype of gastric adenocarcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3659.