Increased PXR and Suppressed T-Cell Signaling Are Associated With Malignant Degeneration of Barrett’s Esophagus

Background and Aims: Barrett's esophagus (BE) is the precursor lesion for esophageal adenocarcinoma (EAC). To detect EAC in early stage, patients with BE undergo endoscopic surveillance. Surveillance cohorts largely consist of nondysplastic BE (NDBE) patients with a low annual progression risk (<0.5%). Predictive biomarkers for malignant progression of NDBE could improve efficacy of surveillance. Biomarker research has mostly focused on aberrant protein expression on BE epithelial cells. Moreover, insight in cell signaling driving malignant transformation is unknown. This study uses a data-driven approach to analyze tumor-stroma interaction in NDBE which progressed to high-grade dysplasia or EAC. Methods: In this case-control study, we performed RNA sequencing analysis on index NDBE biopsies from 6 patients who, during long-term follow-up, progressed and 7 who did not progress to high-grade dysplasia/EAC. For control samples, squamous and duodenum tissues from BE patients were analyzed. For validation, we used quantitative PCR. Results: Significant differences in BE transcriptomic profiles between progressors and nonprogressors were found by principal component and differential expression analyses. Ingenuity pathway analysis indicated that 8 cell signaling pathways were significantly upregulated in the progressors, and 14 pathways were significantly downregulated. The most interesting finding was the upregulation of the xenobiotic metabolism pregnane X receptor signaling pathway in the progressor cohort, while of the downregulated pathways in progressors, several were related to the immune system. Conclusion: These novel transcriptomic insights are fundamental for developing (chemo-)preventive therapies. These could be therapies, which protect against toxins, including biles, responsible for pregnane X receptor activation or which enhance protective immune mechanisms. The identified RNA markers are promising biomarkers for improving risk stratification in surveillance programs.