Summary of study ST000386

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR000302. The data can be accessed directly via it's Project DOI: 10.21228/M80W30 This work is supported by NIH grant, U2C- DK119886.


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Study IDST000386
Study TitleInvestigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer (test/validation)
Study SummaryRecently, the National Lung Cancer Screen Trial (NLST) demonstrated that low-dose CT (LDCT) screening could reduce mortality due to lung cancer by 20%. However, LDCT screening is largely hindered by high false-positive rates (96%), particularly in high-risk populations (heavy smokers), due to the low prevalence rates (less than 2%) of malignant tumors and high incidence of benign lung nodules. Consequently, complementary biomarkers that can be used in conjunction with LDCT screening to improve diagnostic capacities and reduce false-positive rates are highly desirable. Preferably, such complementary tools should be noninvasive and exhibit high sensitivity and specificity. The application of “-omic” sciences (genomics, transcriptomics, proteomics, and metabolomics) represents valuable tools for the discovery and validation of potential biomarkers that can be used for detection of NSCLC. Of these omic sciences, metabolomics has received considerable attention for its application in cancer. Metabolomics is the assessment of small molecules and biochemical intermediates (metabolites) using analytic instrumentation. Metabolites in blood are the product of all cellular processes, which are highly responsive to conditions of disease and environment, and represent the final output products of all organs forming a detailed systemic representation of an individual's current physiologic state. In this study, we used an untargeted metabolomics approach using gas chromatography time-of-flight mass spectrometry (GCTOFMS) to analyze the metabolome of serum and plasma samples both collected from the same patients that were organized into two independent case–control studies (ADC1 and ADC2). In both studies, only NSCLC adenocarcinoma was investigated. The overall objectives were to (i) determine whether individual or combinations of metabolites could be used as a diagnostic test to distinguish NSCLC adenocarcinoma from controls and (ii) to determine which, plasma or serum, provides more accurate classifiers for the detection of lung cancer. We developed individual and multimetabolite classifiers using a training test from the ADC1 study and evaluated the performance of the constructed classifiers, individually or in combination, in an independent test/validation study (ADC2). 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.
University of California, Davis
DepartmentGenome and Biomedical Sciences Facility
LaboratoryWCMC Metabolomics Core
Last NameFiehn
First NameOliver
Address1315 Genome and Biomedical Sciences Facility, 451 Health Sciences Drive, Davis, CA 95616
Phone(530) 754-8258
Submit Date2016-10-04
Study CommentsSS = Sigma sample and is used as a quality control The first set (ADC1) used for biomarker development consisted of serum and plasma samples obtained from 52 stages I–IV NSCLC adenocarcinoma patients (52 plasma and 49 serum), and 31 healthy controls (31 pairs of serum and plasma) for a total of 163 samples. This set was regarded as the training set for biomarker discovery and classifier development. A second, independent case–control study (ADC2) consisting of serum and plasma samples collected from 43 stage I–IV NSCLC adenocarcinoma patients and 43 healthy controls (total 172 samples) was used as an independent test set for biomarker evaluation. A limitation of this study is the relatively small sample size for each cohort (52 cases, 31 controls for ADC1, and 43 cases and 43 controls for ADC2) because patient variability can be a big factor in smaller studies.
Raw Data AvailableYes
Raw Data File Type(s).peg
Analysis Type DetailGC-MS
Release Date2016-04-25
Release Version1
Oliver Fiehn Oliver Fiehn application/zip

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Subject ID:SU000407
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Species Group:Human