Excel - 12 MB, Supplemental Table S3. Pearson correlations between circulating metabolites in serum and plasma during ADC1 and ADC2 (Excel Spreadsheet, four tabs in the following order: ADC1 plasma, ADC1 serum, ADC2 plasma, and ADC2 serum).
ARTICLE ABSTRACT
Background: Untargeted metabolomics was used in case–control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.Methods: Serum and plasma were collected and used in two independent case–control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).Results: Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.Conclusion: This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.Impact: These biomarkers could improve early detection and diagnosis of lung cancer. Cancer Epidemiol Biomarkers Prev; 24(11); 1716–23. ©2015 AACR.
