Methylation Biomarker Panel Performance in EsophaCap Cytology Samples for Diagnosing Barrett's Esophagus: A Prospective Validation Study

Abstract

Purpose: Barrett's esophagus is the only known precursor of esophageal adenocarcinoma (EAC). Although endoscopy and biopsy are standard methods for Barrett's esophagus diagnosis, their high cost and risk limit their use as a screening modality. Here, we sought to develop a Barrett's esophagus detection method based on methylation status in cytology samples captured by EsophaCap using a streamlined sensitive technique, methylation on beads (MOB).

Experimental design: We conducted a prospective cohort study on 80 patients (52 in the training set; 28 in the test set). We used MOB to extract and bisulfite-convert DNA, followed by quantitative methylation-specific PCR to assess methylation levels of 8 previously selected candidate markers. Lasso regression was applied to establish a prediction model in the training set, which was then tested on the independent test set.

Results: In the training set, five of eight candidate methylation biomarkers (p16, HPP1, NELL1, TAC1, and AKAP12) were significantly higher in Barrett's esophagus patients than in controls. We built a four-biomarker-plus-age lasso regression model for Barrett's esophagus diagnosis. The AUC was 0.894, with sensitivity 94.4% [95% confidence interval (CI), 71%-99%] and specificity 62.2% (95% CI, 44.6%-77.3%) in the training set. This model also performed with high accuracy for Barrett's esophagus diagnosis in an independent test set: AUC = 0.929 (P < 0.001; 95% CI, 0.810%-1%), with sensitivity=78.6% (95% CI, 48.8%-94.3%) and specificity = 92.8% (95% CI, 64.1%-99.6%).

Conclusions: EsophaCap, in combination with an epigenetic biomarker panel and the MOB method, is a promising, well-tolerated, low-cost esophageal sampling strategy for Barrett's esophagus diagnosis. This approach merits further prospective studies in larger populations.

Figure 1:

Summary flow of the study. * 9 cases in training set were unable to swallow EsophaCap. # 5 cases in test set were unable to swallow EsophaCap.

Figure 2:

The EsophaCapTM Swallowable Sponge Device. The collapsible black plastic sponge is tethered to a white filament and compressed in a soluble gelatin capsule(A). The end of the filament is held outside the mouth while the capsule is swallowed. Once inside the stomach, the capsule dissolves after several minutes and the sponge expands(B). It is then retrieved by pulling on the filament. Cytologic material attaches during exit, including cells from BE and normal esophagus.

Figure 3:

Methylation values of 8 genes in sponge DNAs from BE cases vs. control subjects. Methylation values of p16(A), NELL1(B), TAC1(C), AKAP12(D) and HPP1(E) were significantly higher in BE than controls (p<0.0001, p=0.0004, p=0.0064, p=0.0015, and p=0.0366, respectively), but differences for SST(F), RUNX3(G), and CDH13(H) were not significant (p=0.327, p=0.979, and p=0.999).

Figure 4:

ROC curves showing individual diagnostic performance of p16, NELL1, AKAP12, TAC1, HPP1, SST, RUNX3, CDH13 and age in sponge DNAs from BE cases vs. controls in the training set (A and B). AUCs = 0.855 (p16), 0.760 (NELL1), 0.768 (AKAP12), 0.732 (TAC1), 0.672 (HPP1), 0.585 (SST), 0.528 (RUNX3), 0.485 (CDH13) and 0.658 (age); P=0.000, 0.002, 0.002, 0.006, 0.043, 0.317, 0.774, 0.863 and 0.062, respectively.

Figure 5:

ROC curves showing diagnostic performance of logistic regression and LASSO models in sponge samples from BE cases and controls in the training set. (A) AUC for logistic regression model based on p16 (CDKN2A) and NELL1 = 0.883, P<0.001; (B) AUC for LASSO model based on 4 biomarkers plus age = 0.894, P<0.001.

Figure 6:

Diagnostic performance of the 4-biomarker-plus-age LASSO model and logistic model in the test set (28 cases). (A) AUC of LASSO model based on 4 biomarkers plus age = 0.929, P<0.001; (B) AUC of logistic model = 0.760, P=0.019.