Massively Parallel Sequencing of Esophageal Brushings Enables an Aneuploidy-Based Classification of Patients With Barrett's Esophagus

Abstract

Background & aims: Aneuploidy has been proposed as a tool to assess progression in patients with Barrett's esophagus (BE), but has heretofore required multiple biopsies. We assessed whether a single esophageal brushing that widely sampled the esophagus could be combined with massively parallel sequencing to characterize aneuploidy and identify patients with disease progression to dysplasia or cancer.

Methods: Esophageal brushings were obtained from patients without BE, with non-dysplastic BE (NDBE), low-grade dysplasia (LGD), high-grade dysplasia (HGD), or adenocarcinoma (EAC). To assess aneuploidy, we used RealSeqS, a technique that uses a single primer pair to interrogate ∼350,000 genome-spanning regions and identify specific chromosome arm alterations. A classifier to distinguish NDBE from EAC was trained on results from 79 patients. An independent validation cohort of 268 subjects was used to test the classifier at distinguishing patients at successive phases of BE progression.

Results: Aneuploidy progression was associated with gains of 1q, 12p, and 20q and losses on 9p and 17p. The entire chromosome 8q was often gained in NDBE, whereas focal gain of 8q24 was identified only when there was dysplasia. Among validation subjects, a classifier incorporating these features with a global measure of aneuploidy scored positive in 96% of EAC, 68% of HGD, but only 7% of NDBE.

Conclusions: RealSeqS analysis of esophageal brushings provides a practical and sensitive method to determine aneuploidy in BE patients. It identifies specific chromosome changes that occur early in NDBE and others that occur late and mark progression to dysplasia. The clinical implications of this approach can now be tested in prospective trials.

Keywords: Aneuploidy; Barrett’s Esophagus; Chromosome 8q; RealSeqS.

Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1:. Overview of the RealSeqS approach.

A) A single primer pair concomitantly amplifies ∼350,000 unique loci spread throughout the genome. B) The patient sample is matched to the 7 closest control samples C) The statistical significance of gains and losses for each of the 39 non-acrocentric chromosome arms is calculated. D) The 39 chromosome arms are integrated into a Genome Aneuploid Score (GAS) using a supervised machine learning model. E) Chromosome arm levels can be quantified and focal changes of interest queried.

Figure 2:. Performance of the Genome Aneuploid Score (GAS) to discriminate samples from patients with HGD or EAC from samples from individuals with NDBE.

(A) The Receiver Operating Characteristic (ROC) curve and area under the curve (AUC) for the GAS metric as applied to the Training Set. (B) Violin plot of the GAS distribution among the clinical subsets of the Training Set. Individuals with LGD were excluded from the Training Set. (C) ROC curve and AUC for the GAS metric as applied to the Validation Set. (D) Violin plot of the GAS distribution among the clinical subsets of the Validation Set.

Figure 3.. The BAD Molecular Classification of progression to dysplasia in patients with Barrett’s Esophagus.

A) The BAD Decision Tree Algorithm. B) Heatmap of predictive features used in the BAD classifier depicted for Training Set samples that do or do not meet criteria as Very-BAD. C) Heatmap of predictive features used in the BAD classifier depicted for Validation Set samples that do or do not meet criteria as Very-BAD.

Figure 4:

Schematic of the progression of aneuploidy, chromosomal arm alterations, and the BAD classification going successively from Non-dysplastic Barrett’s Esophagus to Adenocarcinoma. Chromosome alterations shown in red contribute to the BAD classifier algorithm.