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 ¼ 0.929 (P < 0.001; 95% CI, 0.810%–1%), with sensitivity¼78.6% (95% CI, 48.8%–94.3%) and specificity ¼ 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.
| Original language | English |
|---|---|
| Pages (from-to) | 2127-2135 |
| Number of pages | 9 |
| Journal | Clinical Cancer Research |
| Volume | 25 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Apr 2019 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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