Purpose:

Current endoscopy-based screening and surveillance programs have not been proven effective at decreasing esophageal adenocarcinoma (EAC) mortality, creating an unmet need for effective molecular tests for early detection of this highly lethal cancer. We conducted a genome-wide methylation screen to identify novel methylation markers that distinguish EAC and high-grade dysplasia (HGD) from normal squamous epithelium (SQ) or nondysplastic Barrett's esophagus (NDBE).

Experimental Design:

DNA methylation profiling of samples from SQ, NDBE, HGD, and EAC was performed using HM450 methylation arrays (Illumina) and reduced-representation bisulfate sequencing. Ultrasensitive methylation-specific droplet digital PCR and next-generation sequencing (NGS)-based bisulfite-sequencing assays were developed to detect the methylation level of candidate CpGs in independent esophageal biopsy and endoscopic brushing samples.

Results:

Five candidate methylation markers were significantly hypermethylated in HGD/EAC samples compared with SQ or NDBE (P < 0.01) in both esophageal biopsy and endoscopic brushing samples. In an independent set of brushing samples used to construct biomarker panels, a four-marker panel (model 1) demonstrated sensitivity of 85.0% and 90.8% for HGD and EACs respectively, with 84.2% and 97.9% specificity for NDBE and SQ respectively. In a validation set of brushing samples, the panel achieved sensitivity of 80% and 82.5% for HGD and EAC respectively, at specificity of 67.6% and 96.3% for NDBE and SQ samples.

Conclusions:

A novel DNA methylation marker panel differentiates HGD/EAC from SQ/NDBE. DNA-methylation–based molecular assays hold promise for the detection of HGD/EAC using esophageal brushing samples.

Translational Relevance

Esophageal adenocarcinoma (EAC) is a common and lethal cancer of the gastrointestinal (GI) tract which arises from Barrett's esophagus, a metaplastic alteration of the esophageal mucosa. Patients with Barrett's esophagus are at risk for EAC and are placed in surveillance programs requiring upper endoscopy. The current screening and surveillance program for Barrett's esophagus and EAC is suboptimal because it uses an expensive, invasive surveillance method with modest accuracy and limited efficacy. With recent advances in the management of high-grade dysplasia (HGD) and early EAC with well-tolerated endoscopic therapies, there is a need for more convenient and sensitive nonendoscopic surveillance methods that can save costs, avoid harm, and reduce cancer incidence and related deaths. Newer methods that use swallowed esophageal cytology collection devices (Cytosponge and Esocheck) and molecular biomarkers have the potential for the noninvasive detection of HGD and early EAC and to meet these unmet needs. In our study, we have discovered and validated novel methylated DNA biomarkers for the detection of HGD and EAC.

Esophageal adenocarcinoma (EAC) incidence is rapidly increasing in the United States and affects approximately 20,000 people each year (1). It has a poor prognosis with 5-year survival rates of less than 20% (2). Survival rates dramatically improve if EAC is detected at an early stage, when it can be cured by surgical or endoscopic resection. Importantly, virtually all EAC is believed to arise from a precancerous condition called Barrett's esophagus, which can evolve into EAC over time through a Barrett's esophagus→low-grade dysplasia (LGD)→ high-grade dysplasia (HGD)→ EAC progression sequence. Important recent developments in the treatment of HGD and early EAC are radiofrequency ablation (RFA) and endoscopic mucosal resection (EMR), which are endoscopic therapies that can remove these lesions, allowing in-growth of neosquamous tissue, and dramatically reducing cancer risk (3, 4). These endoscopic treatments demonstrate low morbidity and mortality, substantially reducing the risk for EAC therapy-related death. Given these low morbidity and mortality treatment options, assays that can detect HGD and early-stage EAC are of high value and have the potential to reduce EAC-related death and preserve the quality of life of patients with Barrett's esophagus when included in a surveillance program.

Aberrant DNA methylation has been shown to be a common molecular feature of Barrett's esophagus and EAC (5–8). Although the functional significance of the majority of these DNA methylation events is unclear, they have proven to be highly promising as biomarkers (9–12). Aberrantly methylated DNA biomarkers have been used to develop a “molecular cytology” assay based on methylated VIM in DNA samples from esophageal cytology brushings obtained during endoscopies of 322 individuals, divided into training and validation cohorts (11). The assay showed 91% sensitivity and 93% specificity for detecting Barrett's esophagus, Barrett's esophagus with dysplasia, and EAC, with essentially identical results obtained in both the training and validation cohorts. In a subsequent study that used an assay to detect mVIM and mCCNA1 in samples collected via a noninvasive device, EsoCheck, the assay detected 90.3% of patients with Barrett's esophagus with 91.7% specificity (10).

In light of these studies, we conducted a genome-wide methylation screen to identify potential biomarkers specific for HGD and EAC. We discovered and validated two models that have potential to be used as DNA-methylation–based molecular assays for the minimally invasive detection of HGD and EAC. These results demonstrate a novel assay to detect non-Barrett's esophagus–related DNA methylation that is specific for HGD and EAC.

Study design

This was a nonrandomized observational study. Study size was not prespecified, and results are reported for esophageal brushing samples accrued from June 2011 to April 2019. The primary endpoint of detection of Barrett's esophagus and more advanced lesions were prespecified before study initiation. All brushing samples were assayed by investigators blinded to the clinical status of the subjects from whom the samples were obtained. The clinical trial registration number at ClinicalTrials.gov is NCT00288119 for the endoscopic cytology brushings study. See Fig. 1 for the overall study design and workflow.

Figure 1.

Development of DNA-methylation biomarkers for HGD/EAC early detection (workflow). BE, Barrett's esophagus.

Figure 1.

Development of DNA-methylation biomarkers for HGD/EAC early detection (workflow). BE, Barrett's esophagus.

Close modal

Tissue samples

For the Illumina HumanMethylation450 BeadChip arrays (HM450 arrays), the discovery set of formalin-fixed, paraffin-embedded (FFPE) slides included a total of 53 normal squamous tissue (SQ), 71 Barrett's esophagus, 20 HGD, and 23 EAC samples. They were obtained at the time of endoscopic exam or surgical resection (with no neoadjuvant therapy) from Case Western Reserve University/University Hospitals of Cleveland (Cleveland, OH), Cleveland Clinic (Cleveland, OH), University of North Carolina School of Medicine (Chapel Hill, NC), and University of Washington Medical Center (UWMC; Seattle, WA), following protocols approved by the Institutional Review Board of each institution. Hematoxylin and eosin (H&E)-stained slides were created for each sample and examined by an expert gastrointestinal pathologist (J.E. Willis or D. Reddi) to confirm diagnosis and identify precise areas with the histologic subtypes of interest. Unstained slides were then matched with the annotated H&E slides and a sterile razor blade was used to remove the tissue for DNA extraction. In cases with mixed histology, special care was taken to separate histologic subtypes before extraction.

For the reduced-representation bisulfite-sequencing (RRBS) study preformed as previously described (10), the discovery set consisted of 26 biopsy pairs of EACs with respective matched SQ samples, 8 Barrett's esophagus biopsies, 7 Barrett's esophagus endoscopic brushings, and 5 esophageal cancer cell lines. The biopsies and brushings were collected at Case Western Reserve University, Washington University, and John Hopkins University (Baltimore, MD). Four esophageal cell lines (SKGT4, FLO1, OE19, OE33) were obtained from Sigma-Aldrich. JH-Esoad1 esophageal cancer cell line was a kind gift from Drs. Anirban Maitra and James Eshleman (13). Mycoplasma testing was performed using a MycoAlert kit obtained from Lonza, on all cell lines after 10 passages. All cell lines were used at less than 20 passages and discarded at passage 20. Next-generation sequencing (NGS)-based methylation assays were designed for the top candidate loci identified by RRBS analysis.

The droplet digital PCR (ddPCR) assay for the top candidate markers identified via array analysis was tested on an independent set of endoscopic biopsies and EMR samples obtained from UWMC, with a total of 20 squamous, 17 nondysplastic Barrett's esophagus (NDBE), 20 HGD, and 19 EAC samples. DNA from squamous samples was extracted from fresh-frozen tissue and all other tissue types were extracted from FFPE slides examined by an expert gastrointestinal pathologist, D. Reddi. This sample set was used to test individual markers with mass spectrometry (MS)-ddPCR assays and construct logistic regression model.

All brushing specimens were obtained at the time of the endoscopic exam using a through-the-scope cytology brush, prior to passage of the endoscope through the distal esophagus. Demographic information on the subjects in each sample set can be found in Supplementary Table S1. Control subjects had no endoscopic evidence of Barrett's esophagus and no histologic evidence of intestinal metaplasia. In the brushing sample sets, junctional cancer cases (JCA) were analyzed with EAC cases jointly.

Bisulfite DNA preparation

Genomic DNA was extracted from fresh-frozen tissues with the DNeasy Blood and Tissue Kit, following manufacturer's instructions and eluted into a total volume of 100 μL. The QIAamp DNA FFPE Tissue Kit (Qiagen) was used to extract genomic DNA from FFPE tissues according to manufacturer's instructions with the modification of lysing all tissues overnight. Samples were then eluted into 25 to 100 μL, dependent upon tissue size and kit protocol recommendations. Quant-iT PicoGreen DNA assay kit (Life Technologies), or Qubit HS DNA kit (Thermo Fisher Scientific) were used to quantify genomic DNA before bisulfite conversion. The EZ DNA Methylation Kit (ZymoResearch) or the QIAGEN Epitect kit were used for bisulfite conversion.

HM450 arrays and genome-wide differential methylation analysis

The Infinium HD FFPE DNA Restore Kit (Illumina Inc.) was used to process bisulfite-converted DNA according to the manufacturer's instructions. DNA samples were submitted to the Genomics Core at the Fred Hutchinson Cancer Research Center (FHCRC; Seattle, WA) for processing and subsequently run on the HM450 arrays according to manufacturer's instructions (Illumina Inc.). Data acquisition, normalization, filtering, and analysis were conducted as previously (6, 9). Each CpG site was evaluated for differential methylation between SQ/Barrett's esophagus and HGD/EAC by comparing mean “β values” for the two groups (0.0 = 0% methylation, 1.0 = 100% methylation) using R Limma with adjustment for the sample age and gender. Differentially methylated genomic regions were also analyzed between the two groups using R minfi bumphunter (14). CpG sites with statistically significant higher methylation in HGD/EAC versus SQ/Barrett's esophagus with the adjusted P < 0.001, β difference > 0.15, baseline β < 0.25, and located in differentially methylated regions were considered for assay development.

Methylation-specific ddPCR

MethyLight PCR assays were designed for three CpGs, referred to as Cg6522, YPEL3, and POU3F1 (cg4156522, cg16348385, and cg38512601, respectively) and methylation-specific ddPCR was run on esophageal biopsy and brushing DNA samples. ABI Primer Express Software version 3.0.1 primer/probe test tool was used to manually design the primer and probe sequences for each region. The C-LESS-C1 assay was used as a methylation-independent control as previously described (15–17). A list of all primer/probe sequences used can be found in Supplementary Table S2.

Methylation-specific ddPCR reactions contained 2x ddPCR Supermix for Probes (no dUTP; Bio-Rad), locus-specific primers (900 nmol/L), and locus-specific probes (250 nmol/L). Each reaction was done in duplex with the CpG of interest and the control assay, as previously described (16, 17). Bisulfite-converted samples were used as template DNA, 100% methylated EpiTect Methyl DNA was used as positive control, and 100% unmethylated EpiTect Unmethyl DNA (Qiagen) was used as negative control; all samples were run in duplicate. Reactions underwent droplet generation using the QX200 droplet generator (Bio-Rad). Thermocycler conditions were performed in the T100 Thermal Cycler (Bio-Rad): 95°C for 10 minutes, 45 cycles of 94°C for 30 seconds followed by 60°C for 1 minute, 98°C for 10 minutes, and hold at 4°C indefinitely. Results were generated using the QX200 Droplet Reader (Bio-Rad) and data were analyzed with QuantaSoft Software as previously described (16, 17). Methylation status in both validation sets was reported as relative methylation percentage (RM%), calculated as a ratio percentage of the amount of target methylated alleles (Cg6522, YPEL3, or POU3F1) over total DNA measured by the C-LESS-C1. RM% values were determined as means of duplicates.

Bisulfate sequencing—based methylation detection

Bisulfite-specific, methylation-indifferent PCR primers were constructed as a mixture of primers against converted products of fully methylated or fully unmethylated templates and were used to amplify differentially methylated regions of General Receptor for Phosphoinositides 1-associated Scaffold Protein (GRASP) and MAF bZIP transcription factor B (MAFB; Supplementary Table S3). Platinum Taq reaction mix (Invitrogen) was supplemented with 1 mmol/L MgCl2, 0.2 mmol/L dNTP mix (New England Biolabs), 0.5M Betaine (Sigma), and a mix of the four primers, each at 0.1-μmol/L final concentration. PCR was performed using a touchdown protocol as follows: the activation of Taq polymerase at 95°C for 5 minutes, the initial cycling conditions were: 95°C for 45 seconds, 67°C for 45 seconds, 72°C for 45 seconds. The annealing temperature was decreased by 3°C every three cycles, to a final of 55°C. An additional 35 cycles of PCR were performed at the annealing temperature of 55°C. Successful amplification was confirmed by agarose gel electrophoresis. PCR products were purified using NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel), and quantitated by Qubit. The NEXTflex Rapid DNA-seq kit (BIOO Scientific) was used to prepare indexed libraries for NGS sequencing (Illumina-compatible; based on indexed adapters), and NGS was performed at the McGill University and Génome Québec Innovation Centre (Montréal, Canada), as previously described (10).

Classification/prediction for HGD/EAC using maker methylation cut-off values

An ROC curve analysis of Barrett's esophagus (controls) versus EAC (cases) was performed on the training set of brushings samples. The cut-offs were chosen above the ROC-derived cut-off to maximize the specificity over sensitivity.

Classification/prediction for HGD/EAC using a logistic regression model

A logistic regression model was trained on the biopsy sample set (training set) as \scale88%{|logit( p )\ = \ \log ( {p/( {1 - p} )} )\ = \ {\beta }_0 + {\beta }_1{x}_1 + \ldots + {\beta }_n{x}_n$|⁠, where p and β stand for the probability score of the specified sample being positive and the intercept or coefficient of each marker. The trained model was then tested on the two brushing samples sets. Different combinations of the three candidate markers were compared based on area under the ROC curve (AUC), with the largest AUC indicating the best performance. The diagnostic sensitivity, specificity, and accuracy, defined as (true positives + true negatives)/total sample size, were calculated in both the brushing training and testing sets (Supplementary Fig. S2).

Statistical analysis

Differential methylation analysis of the discovery HM450 array data was performed using R minfi and limma. Results shown here from the training and validation sample sets comparing methylation levels between different histologic groups were generated using GraphPad Prism version 7 (GraphPad Software Inc.). χ2 test and ANOVA were used to test difference of categoric and numeric clinical variables among different tissue samples.

Data availability

A summarized version of the RRBS data and of the HM450 array data supporting the findings of this study are available within the article's supplementary files (Supplementary Tables S10 and S11). The raw sequencing data and HM450 array data are not publicly available due to patient privacy requirements but are available upon request from the corresponding author. Other data generated in this study are available within the article and its supplementary data files.

DNA methylation array discovery of biomarkers for detection of esophageal high-grade dysplastic and malignant lesions

We conducted genome-wide DNA methylation profiling using Illumina HumanMethylation450 (HM450) Beadchip arrays on a set of DNA samples from SQ (N = 53), NDBE (N = 71), HGD (N = 20), and EAC (N = 23). After data normalization and filtering as previously described (6, 9), there were 426,464 CpGs for further evaluation. We performed differentially methylated probe and differentially methylated region (DMP/DMR) analyses using limma & minfi bumphunter (see Methods and Fig. 1 for overall study design and workflow). Twenty-four CpGs were identified to be significantly hypermethylated in the HGD and EAC samples compared with the NDBE or SQ samples, using the following cutoff values: difference in mean β > 0.15 (HGD and EAC vs. Barrett's esophagus and SQ), baseline β values in SQ or NDBE < 0.25, and FDR q < 0.001. These 24 top candidate CpGs were selected for further assessment and validation (Supplementary Table S4).

We assessed the association of the methylation status of CpG sites with potential confounding factors, such as age and gender, using the univariate linear regression model (P < 0.05 was considered significant). We did not find age or gender to be significant confounding variables. We did not assess race as a confounding variate because the majority of the subjects from which our samples were obtained are Caucasians (Supplementary Table S1).

We designed ddPCR assays that measure the methylation level of the approximately 150-bp region surrounding the target CpGs on the HM450 arrays for the top candidate CpGs (Supplementary Table S4). We determined the limit of detection and limit of quantification for each assay, selected the best performing assays for Cg6522, YPEL3, and POU3F1 and then used the assays on DNA extracted from tissue samples. The primer and probe sequences for these MS-ddPCR assays are listed in Supplementary Table S2.

Next, we used the MS-ddPCR assays for Cg6522, YPEL3, and POU3F1 to assess their methylation status in an independent set of DNA samples extracted from endoscopic esophageal biopsies (SQ = 20, NDBE = 17, HGD = 20, EAC = 19). As shown in Supplementary Fig. S1, the mean methylation levels of all three genes were significantly elevated in EAC samples and for POU3F1 in HGD samples (P < 0.01, compared with the SQ or NDBE).

RRBS discovery of DNA-methylation–based biomarkers for detection of esophageal high-grade dysplastic and malignant lesions

In a separate discovery approach, we performed RRBS analysis as previously described (10), on a set of 26 EAC biopsies and their respective matched SQs, 15 biopsy or brushing samples of Barrett's esophagus, and 5 esophageal cancer cell lines. Out of 3,091,193 analyzable CpGs, 21,911 CpGs showed a methylation level below 5% in all the informative Barrett's esophagus samples (requiring at least three informative Barrett's esophagi each having sequencing depth of at least 20×), and additionally had more than 90% of the informative SQ samples at methylation level below 12% (requiring at least 4 informative SQ samples, each having sequencing depth of at least 20×). Three hundred and nineteen of these CpGs additionally showed a level more than 20% methylation in at least five of the informative EAC cases (all having sequencing depth of more than 20×).

These 319 CpGs that were differentially methylated between Barrett's esophagus and SQ versus EAC samples were clustered into 194 differentially methylated CpG patches (defined as clusters of differentially methylated CpGs each less than 400 bp apart). Fifty-one of these patches were selected for further inspection (Supplementary Table S5). Of these, the top candidates for discriminating NDBE lesions from esophageal cancer were (i) Up10, a 2-CpG patch located on chromosome 12, in the CpG island spanning the promoter and 5′-untranslated region (UTR) of GRASP, and (ii) Up35, comprising two patches, one of three CpGs (up35-1), another of single CpG (up35-2), both located 844-bp apart in the CpG island of MFAB gene on chromosome 20.

To further interrogate the Up10- and Up35-associated DNA methylation patches, we designed an NGS-based assay for targeted resequencing of these differentially methylated regions (Supplementary Table S3) as previously described (10). We used this method to characterize a set of esophageal cytology brushings and to compare the performance of DNA methylation at the Up10 and Up35 loci. An Up10 read was considered methylated if any 26 out of 32 CpGs in the Up10 patch were methylated in a single read. For Up35, a read was methylated if methylation was observed in at least 14 out of 20 CpGs in the amplicon. The samples were classified as methylated for a given marker if more than 1% of total observed marker reads were methylated.

Assessment of top candidate methylated DNA biomarkers in a common training set of esophageal brushing samples

To assess the performance of individual markers and to construct the optimal marker panel for detecting HGD and/or early EAC, we examined the marker performance in a common set of cytology brushings from 202 individuals composed of 48 controls with normal esophageal endoscopic exams, 22 with NDBE, 20 with LGD, 20 with HGD, and 92 cases with EAC (Fig. 2; Table 1; Supplementary Table S7). The methylation level of each individual marker was significantly increased in brushings from EAC cases, and Cg6522 and POU3F1 methylation was also significantly increased in brushings from HGD, when compared with brushings obtained from normal squamous tissue (P < 0.01).

Figure 2.

Dot-plot graphs of individual markers Cg6522, YPEL3, POU3F1, Up35-1, and Up10 (A, B, C, D, E, respectively) in DNA extracted from esophageal cytology brushing sample set 1 (training brushing sample set). Each sample was defined by histologic diagnosis, shown on x-axis, as determined by pathologist D. Reddi. The y-axis shows the relative methylation percent when compared with the reference gene C-LESS for total DNA, as measured by MS-ddPCR assays for each marker in (A), (B), (C). The fraction of methylated reads is plotted on the y-axis in D and E. One-way ANOVA was used to determine statistical significance between histologic groups based on relative methylation percent. P values < 0.01 when compared with SQ/Barrett's esophagus tissue are considered significant (*). BE, Barrett's esophagus.

Figure 2.

Dot-plot graphs of individual markers Cg6522, YPEL3, POU3F1, Up35-1, and Up10 (A, B, C, D, E, respectively) in DNA extracted from esophageal cytology brushing sample set 1 (training brushing sample set). Each sample was defined by histologic diagnosis, shown on x-axis, as determined by pathologist D. Reddi. The y-axis shows the relative methylation percent when compared with the reference gene C-LESS for total DNA, as measured by MS-ddPCR assays for each marker in (A), (B), (C). The fraction of methylated reads is plotted on the y-axis in D and E. One-way ANOVA was used to determine statistical significance between histologic groups based on relative methylation percent. P values < 0.01 when compared with SQ/Barrett's esophagus tissue are considered significant (*). BE, Barrett's esophagus.

Close modal
Table 1.

Performance of model 1 and model 2 in training set of esophageal brushings.

Performance of individual markersModel 1Model 2
Samples (n)Up10Up35-1Cg6522YPEL3POU3F1Up10 + Up35-1 + Cg6522 + YPEL3Prob _Cg6522 + POU3F1
Cut-off for positivity  0.01 0.01 40 2.5 12 Cut-offs as for individual markers 0.45 
% Specificity SQ (95% CI) 48 100.0 100.0 97.9 (93.8–100) 100.0 100.0 97.9 (93.8–100.0) 85.4 (74.6–96.2) 
% Specificity BE (95% CI) 19 100.0 84.2 (66.3–100.0) 100.0 100.0 100.0 84.2 (66.3–100.0) 94.7 (84.4–100.0) 
% Sens LGD (95% CI) 20 20.0 (2.0–38.0) 35.0 (0–70.0) 5.0 (0–47.7) 15.0 (0–37.1) 25.0 (0–63.0) 50.0 (19.0–81.0) 45.0 (12.5–77.5) 
% Sens HGD (95% CI) 20 25.0 (0–63.0) 75.0 (53.1–96.9) 25.0 (0–63.0) 15.0 (0–55.4) 35.0 (0–70.0) 85.0 (68.0–100.0) 80.0 (60.4–99.6) 
% Sens early EAC (95% CI) 32 42.9 (18.6–67.1) 46.4 (21.1–71.7) 50.0 (23.8–76.2) 21.4 (4.3–38.6) 25.0 (6.5–43.5) 80.8 (46.2–100.0) 89.2 (54.3–100.0) 
% Sens EAC (95% CI) 87 34.5 (17.5–51.5) 51.7 (37.1–66.3) 50.6 (35.8–65.3) 43.7 (27.9–59.5) 16.1 (0–35.3) 90.8 (84.4–97.2) 87.4 (79.9–94.8) 
Performance of individual markersModel 1Model 2
Samples (n)Up10Up35-1Cg6522YPEL3POU3F1Up10 + Up35-1 + Cg6522 + YPEL3Prob _Cg6522 + POU3F1
Cut-off for positivity  0.01 0.01 40 2.5 12 Cut-offs as for individual markers 0.45 
% Specificity SQ (95% CI) 48 100.0 100.0 97.9 (93.8–100) 100.0 100.0 97.9 (93.8–100.0) 85.4 (74.6–96.2) 
% Specificity BE (95% CI) 19 100.0 84.2 (66.3–100.0) 100.0 100.0 100.0 84.2 (66.3–100.0) 94.7 (84.4–100.0) 
% Sens LGD (95% CI) 20 20.0 (2.0–38.0) 35.0 (0–70.0) 5.0 (0–47.7) 15.0 (0–37.1) 25.0 (0–63.0) 50.0 (19.0–81.0) 45.0 (12.5–77.5) 
% Sens HGD (95% CI) 20 25.0 (0–63.0) 75.0 (53.1–96.9) 25.0 (0–63.0) 15.0 (0–55.4) 35.0 (0–70.0) 85.0 (68.0–100.0) 80.0 (60.4–99.6) 
% Sens early EAC (95% CI) 32 42.9 (18.6–67.1) 46.4 (21.1–71.7) 50.0 (23.8–76.2) 21.4 (4.3–38.6) 25.0 (6.5–43.5) 80.8 (46.2–100.0) 89.2 (54.3–100.0) 
% Sens EAC (95% CI) 87 34.5 (17.5–51.5) 51.7 (37.1–66.3) 50.6 (35.8–65.3) 43.7 (27.9–59.5) 16.1 (0–35.3) 90.8 (84.4–97.2) 87.4 (79.9–94.8) 

Abbreviations: BE, Barrett's esophagus; Sens, sensitivity.

Next, we determined the sensitivity and specificity of each biomarker when used singly and in combination as a panel, and constructed a model with combination of four markers: Up10, Up35-1, Cg6522, and YPEL3 (model 1). With each marker at the cut-off values (Table 1) and calling a sample positive if any individual marker scored as methylated, model 1 demonstrated a high sensitivity for both HGD and EAC at 85% [95% confidence interval (CI), 68.0–100.0] and 90.8% (95% CI, 84.4–97.2), respectively, with specificity 84.2% (95% CI, 66.3–100.0) for NDBE. As an alternative approach using a logistic regression model and AUC for individual marker and marker combinations (Supplementary Table S8), we constructed a second model composed of Cg6522 and POU3F1, which was logit(p) = −3.75 + 0.1130xCg6522 + 0.1926x POU3F1. When the cut-off P was set at 0.45, the AUC in the brushing training set was 0.93 with a sensitivity of detecting HGD and EAC at 80% (95% CI, 60.4–99.6) and 87.4% (95% CI, 79.9–94.8) respectively, with specificity for SQ and NDBE at 85.4% (95% CI, 74.6–96.2) and 94.7% (95% CI, 84.4–100.0), respectively (Table 1; Supplementary Fig. S2A). Both models detected roughly half of LDG cases [50% (95% CI, 19.0–81.0) for model 1, 45% (95% CI, 12.5–77.5) for model 2].

We also collected tumor stage information in this training brushing sample set to assess the accuracy of the biomarker panel for the detection of early EAC cases (tumor stage T0 and T1). In the training set of brushing samples, model 1 achieved sensitivity of 80.8% for early EAC, with 97.9% and 84.2% specificity for SQ and NDBE; model 2 achieved sensitivity of 89.2% for early EAC, with 85.4% and 94.7% specificity for SQ and NDBE (Table 1).

Assessment of models in an independent set of validation bushing sample set

To validate the performance of the two models, we examined the methylation levels of individual markers Cg6522, YPEL3, POU3F1, Up35-1, and Up10 in a second independent set of esophageal cytology brushings from 27 control patients with normal esophageal endoscopic exams, 37 with NDBE, 10 with LGD, 15 with HGD, and 40 cases with EAC (Fig. 3; Supplementary Table S9). Models 1 and 2 were compared side-by-side in a core set of samples where all markers have passed quality control and using the same cut-off values as in the training set. In the validation set, the four-marker panel (model 1) achieved sensitivity of 80% (95% CI, 57.4–100.0) and 82.5% (95% CI, 61.0–100.0) for HGD and EAC, at specificity of 67.6% (95% CI, 49.2–85.9) and 96.3% (95% CI, 89.0–100.0) for NDBE and SQ tissues (Table 2). In the same validation samples, the logistic regression model composed of Cg6522 and POU3F1 (model 2) displays a higher specificity for NDBE at 83.8% (95% CI, 70.8–96.8), yet its sensitivity of detecting HGD decreased to 67% (95% CI, 42.9–90.5). The AUC in the brushing validation set was 0.90 (Supplementary Fig. S2B). Both models performed equivalently for detection of EAC at sensitivity of 82.5% (model 1: 95% CI, 61.0–100; model 2: 95% CI, 69.5–95.5) and discriminating EAC from NDBE. For the small subset of early EAC cases (stage T0 and T1; N = 12), model 1 demonstrated sensitivity of 70% (95% CI, 18.1–100) while model 2 performed slightly better with sensitivity of 80% (95% CI, 24.6–100).

Figure 3.

Dot-plot graphs of individual markers Cg6522, YPEL3, POU3F1, Up35-1, and Up10 (A, B, C, D, E, respectively) in DNA extracted from esophageal cytology brushing sample set 2 (validation brushing sample set). Each sample was defined by histologic diagnosis, shown on x-axis, as determined by pathologist D. Reddi. The y-axis shows the relative methylation percent when compared with the reference gene C-LESS for total DNA, as measured by MS-ddPCR assays for each marker in panels A, B, and C. The fraction of methylated reads is plotted on the y-axis in panels D and E. One-way ANOVA was used to determine statistical significance between histologic groups based on relative methylation percent. P values < 0.01 when compared with SQ/Barrett's esophagus tissue are considered significant (*). BE, Barrett's esophagus.

Figure 3.

Dot-plot graphs of individual markers Cg6522, YPEL3, POU3F1, Up35-1, and Up10 (A, B, C, D, E, respectively) in DNA extracted from esophageal cytology brushing sample set 2 (validation brushing sample set). Each sample was defined by histologic diagnosis, shown on x-axis, as determined by pathologist D. Reddi. The y-axis shows the relative methylation percent when compared with the reference gene C-LESS for total DNA, as measured by MS-ddPCR assays for each marker in panels A, B, and C. The fraction of methylated reads is plotted on the y-axis in panels D and E. One-way ANOVA was used to determine statistical significance between histologic groups based on relative methylation percent. P values < 0.01 when compared with SQ/Barrett's esophagus tissue are considered significant (*). BE, Barrett's esophagus.

Close modal
Table 2.

Performance of model 1 and model 2 in validation set of esophageal brushings.

Common core set of samplesModel 1Model 2
Samples (n)Up10Up35-1Cg6522YPEL3Up10 + Up35-1 + Cg6522 + YPEL3Prob _Cg6522 + POU3F1
Cut-off  0.01 0.01 40 2.5 Cut-offs as for individual markers 0.45 
% Specificity SQ (95% CI) 27 100.0 100.0 100.0 96.3 (89.0–100.6) 96.3 (89.0–100.0) 92.6 (82.3–100.0) 
% Specificity BE (95% CI) 37 91.9 (82.3–100.0) 83.8 (70.8–96.8) 100.0 (91.0–100.0) 91.9 (82.7–100.0) 67.6 (49.2–85.9) 83.8 (70.8–96.8) 
% Sens LGD (95% CI) 10 10.0 (0–70.0) 30.0 (0–80.0) 10.0 (0–68.8) 10.0 (0–68.0) 50 (6.2–93.8) 40 (0–88.0) 
% Sens HGD (95% CI) 15 27.0 (0–67.0) 60.0 (28.0–92.0) 20.0 (0–65.3) 13.0 (0–59.9) 80 (57.4–100.0) 67 (37.9–96.1) 
% Sens early EAC (95% CI) 12 60.0 (11.9–100.0) 60.0 (11.9–100.0) 20.0 (0–47.7) 20.0 (0–47.7) 70.0 (18.1–100) 80.0 (24.6–100.0) 
% Sens EAC (95% CI) 40 52.5 (31.1–73.9) 40.0 (16.0–64.0) 45.0 (22.0–68.0) 35.0 (10.0–60.0) 82.5 (61.0–100.0) 82.5 (69.5–95.5) 
Common core set of samplesModel 1Model 2
Samples (n)Up10Up35-1Cg6522YPEL3Up10 + Up35-1 + Cg6522 + YPEL3Prob _Cg6522 + POU3F1
Cut-off  0.01 0.01 40 2.5 Cut-offs as for individual markers 0.45 
% Specificity SQ (95% CI) 27 100.0 100.0 100.0 96.3 (89.0–100.6) 96.3 (89.0–100.0) 92.6 (82.3–100.0) 
% Specificity BE (95% CI) 37 91.9 (82.3–100.0) 83.8 (70.8–96.8) 100.0 (91.0–100.0) 91.9 (82.7–100.0) 67.6 (49.2–85.9) 83.8 (70.8–96.8) 
% Sens LGD (95% CI) 10 10.0 (0–70.0) 30.0 (0–80.0) 10.0 (0–68.8) 10.0 (0–68.0) 50 (6.2–93.8) 40 (0–88.0) 
% Sens HGD (95% CI) 15 27.0 (0–67.0) 60.0 (28.0–92.0) 20.0 (0–65.3) 13.0 (0–59.9) 80 (57.4–100.0) 67 (37.9–96.1) 
% Sens early EAC (95% CI) 12 60.0 (11.9–100.0) 60.0 (11.9–100.0) 20.0 (0–47.7) 20.0 (0–47.7) 70.0 (18.1–100) 80.0 (24.6–100.0) 
% Sens EAC (95% CI) 40 52.5 (31.1–73.9) 40.0 (16.0–64.0) 45.0 (22.0–68.0) 35.0 (10.0–60.0) 82.5 (61.0–100.0) 82.5 (69.5–95.5) 

Abbreviations: BE, Barrett's esophagus.; Sens, sensitivity.

The need to improve detection of Barrett's esophagus progression in surveillance of patients with Barrett's esophagus led us to determine whether DNA methylated biomarkers in cytology brushing samples have the potential to detect HGD or early EAC. In this study we first conducted a genome-wide methylation analysis by two independent methods, followed by an exhaustive candidate biomarker search. We discovered a list of candidate methylated DNA biomarkers that showed significantly higher methylation levels in HGD and EAC compared with the normal squamous tissue and Barrett's esophagus samples. Ultrasensitive methylation-specific ddPCR assays and bisulfite-sequencing–based assays were designed for each individual marker. We determined the sensitivity and specificity of each biomarker when used singly and in combination as an assay panel and constructed two different models for discrimination of NDBE from HGD and EAC, in two independent sets of endoscopic cytology brushings. We found a four-marker panel composed of two ddPCR assays (Cg6522 and YPEL3) and two NGS assays (Up10 and Up35-1) achieved sensitivity of 80% and 82.5% for HGD and EAC, at specificity of 67.6% and 96.3% for NDBE and normal squamous tissues in a validation set of brushing samples. These findings demonstrate the ability of DNA methylation markers to discriminate NDBE from HGD and EAC and the potential of these markers to be used in Barrett's esophagus surveillance.

ddPCR is a relatively new technology that enables the precise and sensitive detection and absolute quantification of nucleic acid targets in various clinical specimens. We and others have demonstrated its superior performance over conventional Methylight PCR for DNA methylation studies (15–17, 18). In this study, we developed methylation-specific ddPCR assays for the top candidate CpG sites from the HM450 array studies. We demonstrated that MS-ddPCR assays can accurately quantify methylated Cg6522, YPEL3, and POU3F1 in as little as 4 ng of DNA from esophageal brushing samples. Since cytologic sampling yields limited amount of DNA with mixed cell types, our results support the feasibility of MS-ddPCR based assays for the development of DNA methylation-based molecular cytology assays for HGD and EAC detection. Moreover, ddPCR has been approved by the FDA for use in clinical assays (e.g., Bio-Rad Laboratories’ QXDx BCR-ABL %IS kit and QXDX AutoDG ddPCR system), which helps minimize the barriers of our assay for adoption in the clinical setting. However, it is also important to note that a limitation of MS-ddPCR is that it can only determine the methylation status of a small number of CpGs, which can prevent the creation of technically robust assays for some promising DNA methylation-based biomarkers. The NGS approach allows for determining the methylation status for each CpG in an amplicon across a single DNA strand, thus an evaluation of a larger stretch of CpGs within the amplicon. The patch-based algorithm suppresses background from random methylation of individual CpG and provides enhanced discrimination of normal squamous versus diseased tissue. The NGS-based assay also allows for greater flexibility when designing the assays in the difficult to amplify genomic regions, as is often the case with GC-rich templates found in the CpG-dense areas. For example, while the MAFB region containing DNA methylation patch UP35 was identified as differentially methylated in both methylation array, and the RRBS screens, an adequately performing MS-ddPCR assay was difficult to design for the specific CpGs identified in the array screen, while an NGS-based assay performed well. We propose that both assay technologies should be considered when designing methylated DNA-based assays. We believe novel DNA methylation-based biomarkers combined with nonendoscopic sample devices, such as Cytosponge or Esocheck, hold promise to be a low-cost and minimally invasive surveillance method for individuals with Barrett's esophagus for the early detection of HGD/EAC.

The strength of this best-performing biomarker panel lies in its capability to differentiate HGD and EAC from normal squamous tissue and NDBE. There are no existing assays that can accomplish this beyond tissue histology, which requires endoscopy, although NGS assays assessing TP53 mutations may have this potential (19). If combined with promising Barrett's esophagus markers identified and validated by our group and others, we propose that these markers have potential to enable molecular enhancement of EAC screening and Barrett's esophagus surveillance. The limitation of our studies includes lack of a prespecified sample size at discovery and the relatively small sample size of the training and validation sets of esophageal brushing samples, thus requires further validation in larger cohorts. Furthermore, given the emerging technical advances in swallowable collection devices (20), we plan to validate the DNA-methylation–based molecular assay in esophageal balloon–collected samples, as this sample collection method appears to have a high likelihood of near-term adoption into clinical practice for at least Barrett's esophagus screening. Finally, it is worth noting that the primary focus of this study is to identify and develop methylation markers that distinguish EAC/HGD from normal squamous epithelium or NDBE. Our results raise the question of whether these biomarkers can be used to identify patients with HGD who will develop EAC in the future. Although intriguing, our current study design and sample collections preclude the assessment of the Barrett's esophagus progression biomarkers, which is beyond the scope of this study.

In summary, these findings establish the proof of principal that DNA methylation can detect progression of esophageal neoplasia to cancer and lay the molecular foundation for further trials of DNA-methylation–based tests for the detection of HGD and EAC.

M. Yu reports a patent for 63/317,906 pending. H.R. Moinova reports personal fees and other support from Lucid Diagnostics, as well as grants from NIH during the conduct of the study; in addition, H.R. Moinova has a patent for WO2016/109712 A1 pending and licensed to Lucid Diagnostics. J. Inadomi reports personal fees from Cernostics outside the submitted work. P.G. Iyer reports grants and personal fees from Exact Sciences and Cernostics, as well as personal fees and nonfinancial support from CDX Medical during the conduct of the study. P.G. Iyer also reports personal fees from Ambu and Medtronic, as well as grants and personal fees from Pentax Medical outside the submitted work. M.I. Canto reports other support from NIH/NCI during the conduct of the study. N.J. Shaheen reports grants from Lucid Diagnostics, Medtronic, Interpace Diagnostics, and CDx Diagnostics, as well as personal fees from Castle Biosciences and Exact Sciences during the conduct of the study. N.J. Shaheen also reports grants from Steris and Pentax, as well as personal fees from Aqua Sciences and Cook Medical outside the submitted work. J.E. Willis reports grants from NCI during the conduct of the study, as well as personal fees from Lucid Diagnostics outside the submitted work; in addition, J.E. Willis has a patent for Lucid Diagnostics issued and licensed. A. Chak reports grants, personal fees, and other support from Lucid Diagnostics during the conduct of the study, as well as personal fees from CDX Diagnostics outside the submitted work; in addition, A. Chak has a patent for WO2016/109712A1 pending and licensed to Lucid Diagnostics. S.D. Markowitz reports grants, personal fees, and other support from Lucid Diagnostics during the conduct of the study. S.D. Markowitz also reports other support from Exact Sciences, as well as personal fees and other support from Amgen outside the submitted work; in addition, S.D. Markowitz has a patent for WO 2016/109712 A1 pending and licensed to Lucid Diagnostics. W.M. Grady reports personal fees from Guardant Health, Freenome, DiaCarta, and Nephron, as well as nonfinancial support from Lucid Technologies and Tempus during the conduct of the study. W.M. Grady also reports personal fees from SEngine, WebMD, Guidepoint, and GLG, as well as other support from Janssen outside the submitted work; in addition, W.M. Grady has a patent for Methylated gene biomarker for esophageal cancer pending to FHCRC. No disclosures were reported by the other authors.

M. Yu: Conceptualization, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. H.R. Moinova: Conceptualization, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. A. Willbanks: Data curation, formal analysis, investigation, writing–review and editing. V.K. Cannon: Data curation, formal analysis, investigation, writing–review and editing. T. Wang: Data curation, formal analysis, methodology, writing–review and editing. K. Carter: Resources, investigation, writing–review and editing. A. Kaz: Resources, writing–review and editing. D. Reddi: Formal analysis, investigation, writing–review and editing. J. Inadomi: Resources, funding acquisition, writing–review and editing. G. Luebeck: Funding acquisition, writing–review and editing. P.G. Iyer: Resources. M.I. Canto: Resources. J.S. Wang: Resources. N.J. Shaheen: Resources. P.N. Thota: Resources. J.E. Willis: Resources, formal analysis, writing–review and editing. T. LaFramboise: Data curation, formal analysis, investigation. A. Chak: Conceptualization, resources, formal analysis, funding acquisition, writing–review and editing. S.D. Markowitz: Conceptualization, resources, data curation, supervision, funding acquisition, investigation, writing–original draft, writing–review and editing. W.M. Grady: Conceptualization, resources, data curation, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

These studies were supported by funding from the NCI (R50CA233042 to M. Yu), NIH (UO1CA152756, RO1CA220004, P30CA015704, U54CA163060, UO1CA086402, UO1CA182940, the Prevent Cancer Foundation to W.M. Grady; P50CA150964 and UO1CA152756 to S.D. Markowitz; U54CA163060 and P30DK097948 to A. Chak). P.G. Iyer receives funding from Exact Sciences, Pentax Medical, and Cernostics. Funding is also provided by the Cottrell Family Fund, Evergreen Fund, and Listwin Foundation to W.M. Grady.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

1.
Hur
C
,
Miller
M
,
Kong
CY
,
Dowling
EC
,
Nattinger
KJ
,
Dunn
M
, et al
.
Trends in esophageal adenocarcinoma incidence and mortality
.
Cancer
2013
;
119
:
1149
58
.
2.
Smyth
EC
,
Lagergren
J
,
Fitzgerald
RC
,
Lordick
F
,
Shah
MA
,
Lagergren
P
, et al
.
Oesophageal cancer
.
Nat Rev Dis Primers
2017
;
3
:
17048
.
3.
Phoa
KN
,
Van Vilsteren
FGI
,
Weusten
BLAM
,
Bisschops
R
,
Schoon
EJ
,
Ragunath
K
, et al
.
Radiofrequency ablation vs endoscopic surveillance for patients with Barrett esophagus and low-grade dysplasia: a randomized clinical trial
.
JAMA
2014
;
311
:
1209
17
.
4.
Shaheen
NJ
,
Overholt
BF
,
Sampliner
RE
,
Wolfsen
HC
,
Wang
KK
,
Fleischer
DE
, et al
.
Durability of radiofrequency ablation in Barrett's esophagus with dysplasia
.
Gastroenterology
2011
;
141
:
460
8
.
5.
Kaz
AM
,
Grady
WM
,
Stachler
MD
,
Bass
AJ
.
Genetic and epigenetic alterations in Barrett's esophagus and esophageal adenocarcinoma
.
Gastroenterol Clin North Am
2015
;
44
:
473
89
.
6.
Yu
M
,
Maden
SK
,
Stachler
M
,
Kaz
AM
,
Ayers
J
,
Guo
Y
, et al
.
Subtypes of Barrett's oesophagus and oesophageal adenocarcinoma based on genome-wide methylation analysis
.
Gut
2019
;
68
:
389
99
.
7.
Jammula
S
,
Katz-Summercorn
AC
,
Li
X
,
Linossi
C
,
Smyth
E
,
Killcoyne
S
, et al
.
Identification of subtypes of Barrett's esophagus and esophageal adenocarcinoma based on DNA methylation profiles and integration of transcriptome and genome data
.
Gastroenterology
2020
;
158
:
1682
97
.
8.
Kuester
D
,
El-Rifai
W'E
,
Peng
D
,
Ruemmele
P
,
Kroeckel
I
,
Peters
B
, et al
.
Silencing of MGMT expression by promoter hypermethylation in the metaplasia-dysplasia-carcinoma sequence of Barrett's esophagus
.
Cancer Lett
2009
;
275
:
117
26
.
9.
Yu
M
,
O'Leary
RM
,
Kaz
AM
,
Morris
SM
,
Carter
KT
,
Chak
A
, et al
.
Methylated B3GAT2 and ZNF793 are potential detection biomarkers for Barrett's esophagus
.
Cancer Epidemiol Biomarkers Prev
2015
;
24
:
1890
7
.
10.
Moinova
HR
,
Laframboise
T
,
Lutterbaugh
JD
,
Chandar
AK
,
Dumot
J
,
Faulx
A
, et al
.
Identifying DNA methylation biomarkers for non-endoscopic detection of Barrett's esophagus
.
Sci Transl Med
2018
;
10
:
eaao5848
.
11.
Moinova
H
,
Leidner
RS
,
Ravi
L
,
Lutterbaugh
J
,
Barnholtz-Sloan
JS
,
Chen
Y
, et al
.
Aberrant vimentin methylation is characteristic of upper gastrointestinal pathologies
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
594
600
.
12.
Wang
Z
,
Kambhampati
S
,
Cheng
Y
,
Ma
K
,
Simsek
C
,
Tieu
AH
, et al
.
Methylation biomarker panel performance in EsophaCap cytology samples for diagnosing Barrett's esophagus: a prospective validation study
.
Clin Cancer Res
2019
;
25
:
2127
35
.
13.
Alvarez
H
,
Koorstra
JB
,
Hong
SM
,
Boonstra
JJ
,
Dinjens
WNM
,
Foratiere
AA
, et al
.
Establishment and characterization of a bona fide Barrett esophagus-associated adenocarcinoma cell line
.
Cancer Biol Ther
2008
;
7
:
1753
5
.
14.
Aryee
MJ
,
Jaffe
AE
,
Corrada-Bravo
H
,
Ladd-Acosta
C
,
Feinberg
AP
,
Hansen
KD
, et al
.
Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays
.
Bioinformatics
2014
;
30
:
1363
9
.
15.
Weisenberger
DJ
,
Trinh
BN
,
Campan
M
,
Sharma
S
,
Long
TI
,
Ananthnarayan
S
, et al
.
DNA methylation analysis by digital bisulfite genomic sequencing and digital MethyLight
.
Nucleic Acids Res
2008
;
36
:
4689
98
.
16.
Yu
M
,
Heinzerling
TJ
,
Grady
WM
.
DNA methylation analysis using droplet digital PCR
.
Methods Mol Biol
2018
;
1768
:
363
83
.
17.
Yu
M
,
Carter
KT
,
Makar
KW
,
Vickers
K
,
Ulrich
CM
,
Schoen
RE
, et al
.
MethyLight droplet digital PCR for detection and absolute quantification of infrequently methylated alleles
.
Epigenetics
2015
;
10
:
803
9
.
18.
Van Wesenbeeck
L
,
Janssens
L
,
Meeuws
H
,
Lagatie
O
,
Stuyver
L
.
Droplet digital PCR is an accurate method to assess methylation status on FFPE samples
.
Epigenetics
2018
;
13
:
207
13
.
19.
Weaver
JMJ
,
Ross-Innes
CS
,
Shannon
N
,
Lynch
AG
,
Forshew
T
,
Barbera
M
, et al
.
Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis
.
Nat Genet
2014
;
46
:
837
43
.
20.
Kaz
AM
,
Grady
WM
.
Novel Barrett's esophagus screening assays based on swallowable devices: will they change the game?
Transl Gastroenterol Hepatol
2019
;
4
:
25
.

Supplementary data