Abstract
BackgroundEsophageal adenocarcinoma (EAC) often presents at a late, incurable stage, and mortality has increased substantially, due to an increase in incidence of EAC arising out of Barrett’s esophagus. When diagnosed early, however, the combination of surgery and adjuvant therapies is associated with high cure rates. Metabolomics provides a means for non- invasive screening of early tumor-associated perturbations in cellular metabolism.MethodsUrine samples from patients with esophageal carcinoma (n = 44), Barrett’s esophagus (n = 31), and healthy controls (n = 75) were examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx software permitted quantification of 66 distinct metabolites. Unsupervised (principal component analysis) and supervised (orthogonal partial least-squares discriminant analysis OPLS-DA) multivariate pattern recognition techniques were applied to discriminate between samples using SIMCA-P+ software. Model specificity was also confirmed through comparison with a pancreatic cancer cohort (n = 32).ResultsClear distinctions between esophageal cancer, Barrett’s esophagus and healthy controls were noted when OPLS-DA was applied. Model validity was confirmed using two established methods of internal validation, cross-validation and response permutation. Sensitivity and specificity of the multivariate OPLS-DA models were summarized using a receiver operating characteristic curve analysis and revealed excellent predictive power (area under the curve = 0.9810 and 0.9627 for esophageal cancer and Barrett’s esophagus, respectively). The metabolite expression profiles of esophageal cancer and pancreatic cancer were also clearly distinguishable with an area under the receiver operating characteristics curve (AUROC) = 0.8954.ConclusionsUrinary metabolomics identified discrete metabolic signatures that clearly distinguished both Barrett’s esophagus and esophageal cancer from controls. The metabolite expression profile of esophageal cancer was also discrete from its precursor lesion, Barrett’s esophagus. The cancer-specific nature of this profile was confirmed through comparison with pancreatic cancer. These preliminary results suggest that urinary metabolomics may have a future potential role in non-invasive screening in these conditions.
Highlights
Esophageal adenocarcinoma (EAC) often presents at a late, incurable stage, and mortality has increased substantially, due to an increase in incidence of EAC arising out of Barrett’s esophagus
The majority, if not all cases of EAC arise from a region of Barrett’s esophagus (BE), a well-recognized premalignant condition and common complication of gastro-esophageal reflux disease (GERD) affecting 12% to 20% of patients suffering from reflux [2,5,8,9]
Identification of a discrete, urinary metabolomic signature associated with esophageal cancer and its precursor lesion, could allow for non-invasive screening in targeted, high-risk populations while helping to further elucidate underlying molecular pathogenesis and progression of disease
Summary
Esophageal adenocarcinoma (EAC) often presents at a late, incurable stage, and mortality has increased substantially, due to an increase in incidence of EAC arising out of Barrett’s esophagus. Metabolomics provides a means for non- invasive screening of early tumor-associated perturbations in cellular metabolism. Esophageal adenocarcinoma (EAC) represents over 50% of all esophageal cancers in the western world, and the incidence of EAC arising from Barrett’s esophagus (BE) continues to increase at an alarming rate [1,3,4]. Urinary metabolomics may offer one such potential for non-invasive screening of early tumor-associated perturbations in cellular metabolism. Identification of a discrete, urinary metabolomic signature associated with esophageal cancer and its precursor lesion, could allow for non-invasive screening in targeted, high-risk populations while helping to further elucidate underlying molecular pathogenesis and progression of disease. Knowledge gained from metabolomics-based research could help advance personalized therapeutic approaches guided by early metabolic responses before phenotypic changes develop
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