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Abstract

Esophageal adenocarcinoma (EAC) is one of the deadliest cancers, with a five-year net survival rate of only 16% according to the Canadian Cancer Society. A key factor contributing to its high mortality is late-stage diagnosis. Barrett’s Esophagus (BE), an intestinal-type metaplasia arising in the distal esophagus in response to chronic inflammation, is a precursor lesion for EAC. Although the overall risk of progression from BE to EAC is low, it increases significantly following the development of high-grade dysplasia (HGD), a genetically unstable, precancerous dysplastic state. Currently, the detection of BE relies on endoscopic surveillance and histological assessment, and predicting which patients will progress to HGD before the phenotypic switch takes place is not possible. Beyond the classical oncogenic mutations (i.e. TP53, CDKN2A) which often precede catastrophic genomic events that drive progression, the genetic mutations that may initiate BE progression but are lost through to dysplasia, remain poorly characterized. The field of carcinogenesis is lacking a method by which to track clonal competition of BE stem cells in real time to identify potentially malignant clones before they drive phenotypic shifts. This project aims to develop and validate a method to uncover the clonal competition that drives clonal expansion of malignant clones in BE. To achieve this, we adapted a previously established DNA barcoding technology for use in our adult tissue resident stem cell (ASC) populations to track clonal competition in real time. To validate this tool’s ability to detect clones with a selective growth advantage, we introduced a faster-growing cell line carrying a known barcode sequence into a barcoded population of BE-ASCs. The faster-growing cell line could be informatically recovered, and sequencing data revealed clonal competition within BE-ASC populations, establishing an empirical basis to further explore the capability of this methodology to identify potentially malignant BE clones. This approach has the potential to identify previously unknown causative genes involved in BE progression, as well as improve early detection, and risk assessments of EAC; ultimately enabling the development of less invasive, cost-effective screening strategies for BE and improving the ability to detect high-risk individuals before the progression to cancer.