The Evolving Genomic Landscape of Barrett's Esophagus and Esophageal Adenocarcinoma

Abstract

We have recently gained unprecedented insight into genetic factors that determine risk for Barrett's esophagus (BE) and progression to esophageal adenocarcinoma (EA). Next-generation sequencing technologies have allowed us to identify somatic mutations that initiate BE and track genetic changes during development of tumors and invasive cancer. These technologies led to identification of mechanisms of tumorigenesis that challenge the current multistep model of progression to EA. Newer, cost-effective technologies create opportunities to rapidly translate the analysis of DNA into tools that can identify patients with BE at high risk for cancer, detect dysplastic lesions more reliably, and uncover mechanisms of carcinogenesis.

Keywords: Chromothripsis; Cytosponge; Esophagus; Genome-wide Association Study; Mutational Signature.

Figure 1. Somatic mutations and next-generation sequencing of cancer

A) Tumor tissues can have point mutations, structural variations, copy number alterations, and genome catastrophes. Possible mechanisms of mutation are shown in a chromosome (2 arms linked by a dark gray centromere); these can involve a large segment of genome (lettered rectangles) or single DNA base pairs. Structural variations can cause loss or gain of genetic material and result in copy number changes. Complex structural variations occur in regions of genome catastrophes such as chromothripsis and breakage fusion bridge cycles ^,,,. In cycles of breakage fusion bridge, an unprotected DNA end is generated following the loss of the telomeres (red) or a double-strand break. During anaphase the broken chromatids can fuse (anaphase bridge) and then tear unevenly when the 2 chromatids are pulled apart. This event can be repeated through several cycles, leading to amplification of oncogenes. B) Next-generation sequencing of DNA extracted from cancer cells can identify somatic mutations that arise during carcinogenesis.

Figure 2. Variants that Increase Risk for BE and EA and Genomic Alterations Frequently Detected in EAs

A) Circos plot of the loci associated with BE or EA risk in GWASs and in post-GWASs studies, reference to the first report followed by reference to confirmatory reports is shown in brackets. B) Circos plot of genomic alterations frequently detected in EAs. From the center of the circos to the outer ring: a) Significant regions of copy number losses (blue) according to the Gistic analysis (a tool to identify somatic copy number alterations; Broad Institute, US) reported by ^,, on their respective cohorts; b) Copy number gains (red) according to the criteria above; c) Most frequent recurrent gene hits by SVs reported by , fragile sites were excluded; d) Recurrent point mutations in driver genes according to Mutsig and MutsigCV (bioinformatic tools to identify driver mutations; Broad Institute, US) in >/= 10% of cases by ^,,. * Common Fragile Site Genes. For an extended annotation of the data shown, see Supplementary Table 2.

Figure 3. Mutational Signatures of Tumors

A) Mutational processes are biological activities (e.g.: aging, smoking, UV light exposure, unknown carcinogens) that generate patter of mutations (mutational signatures) through a damage of the DNA sequence and its attempt to repair it by DNA repair mechanisms. B) The mutational portrait is the the total pattern of genetic changes in cancer cell that derive from the sum of all the mutational signatures occurring in a lifetime . C) Mathematical approaches, such as non-negative matrix factorization (NMF), can be used to extract mutational signatures from the mutational portraits of groups of patient’s cancer genomes. The pattern includes all base substitutions and flanking nucleotides (96 possible combinations shown in bar charts). NMF estimates the relative contribution of each signature to the mutational portrait and can highlight cancers that are predominantly driven by some mutational signatures. A comprehensive catalogue of the signatures identified by Alexandrov et al is available on the catalogue of somatic mutations in cancer (COSMIC, www.cancer.sanger.ac.uk). Mutation signatures associated with EA include a) S17, also called an acid signature—there are 2 forms, S17A and B; b) S3, associated with defects in the BRCA1/2-led homologous recombination pathway; c) S1, associated with aging; d) S2, caused by APOBEC mutations, and e) S18, detected in gastric cancer and neuroblastoma, arises via an unknown mechanism^,.

Figure 4. Paths of BE Progression to EA

Findings from next-generation sequencing studies indicate BE progression can accelerate via genome doubling, genome catastrophes, and other unknown mechanisms—even at early stages of tumor progression. The main path represent the multistep progression of BE to EA through dysplasia. BE and EA pathogenesisis include genetic risk factors (each flag indicate GWAS identified regions), exposure to environmental risk factors (e.g. acid reflux) and the accumulation of different types of driver and passenger mutations. Genomic catastrophes such as chromothripsis and whole genome doubling can occour at any stange and dramatically accelerate progression of BE.

Figure 5. Translating Findings from Genetic Studies Into Clinical Practice

Genetic data can be used to determine an individual’s risk for developing BE or EA, and to manage patients at different stages of disease progression. Test are available for use in primary (pink) secondary (light blue), and tertiary (orange) care settings. For each group (left), we provide example of clinical applications. The most suitable technology for each test is presented in the bottom row. The left column indicates the group size relative to the general population.