Summary of Study ST000396

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 PR000309. The data can be accessed directly via it's Project DOI: 10.21228/M86G6V This work is supported by NIH grant, U2C- DK119886.


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Study IDST000396
Study TitleLung Cancer Plasma Discovery
Study SummaryRecently, major efforts have been directed toward early detection of lung cancer through low-dose computed tomography (LDCT) scanning. Data from the National Lung Screening Trial (NLST) suggest that yearly screening with thoracic LDCT scanning for high-risk current and former smokers reduces lung cancer mortality by 20% and total mortality by 7%. However, issues including indeterminate nodules detected by LDCT and radiation exposure impact the practicality of LDCT-based screening on a national and global basis. A blood-based biomarker or multiplexed marker panel that could complement LDCT would represent a major advance in implementing lung cancer screening. Efforts to develop blood-based biomarkers for lung cancer early detection using a variety of methodologies are currently ongoing. Proteomic studies have led to the identification of several candidate markers including pro-surfactantproteinB(pro-SFTPB), a target of a lineage-survival oncogene in lung cancer, NKX2-1.Validation studies using blood samples collected at the time of LDCT screening for lung cancer substantiated the performance of pro-SFTPB. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB. The area under the curve (AUC) values of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95%CI, 0.620 to 0.717), respectively (difference in AUC, P_.001). Single markers are unlikely to have sufficient performance for implementation in a screening setting, hence the need to explore several discovery platforms to identify markers that provide complementary performance. Metabolomics represents a global unbiased approach to the profiling of small molecules and has been established as a platform for biomarker discovery for a variety of human biofluids and tissues. Here we used an untargeted liquid chromatography/mass spectrometry (MS) metabolomics approach to identify metabolites that distinguish human sera collected before the diagnosis of lung cancer from matched control sera in a prospective cohort of highrisk patients from the Beta-Carotene and Retinol Efficacy Trial (CARET).
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 Date2016-05-10
Raw Data AvailableYes
Raw Data File Type(s)cdf
Analysis Type DetailGC-MS
Release Date2016-06-18
Release Version2
Release CommentsUpdated study design factors
Oliver Fiehn Oliver Fiehn application/zip

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Combined analysis:

Analysis ID AN000633
Analysis type MS
Chromatography type GC
Chromatography system Agilent 6890N
Column Restek Corporation Rtx-5Sil MS
MS Type EI
MS instrument type GC Ion Trap
MS instrument name Varian 210-MS GC Ion Trap
Units counts


Chromatography ID:CH000458
Methods Filename:Data_Dictionary_Fiehn_laboratory_GCTOF_MS_primary_metabolism_10-15-2013_general.pdf
Instrument Name:Agilent 6890N
Column Name:Restek Corporation Rtx-5Sil MS
Column Pressure:7.7 PSI
Column Temperature:50-330C
Flow Rate:1 ml/min
Injection Temperature:50 C ramped to 250 C by 12 C/s
Sample Injection:0.5 uL
Oven Temperature:50°C for 1 min, then ramped at 20°C/min to 330°C, held constant for 5 min
Transferline Temperature:230C
Washing Buffer:Ethyl Acetate
Sample Loop Size:30 m length x 0.25 mm internal diameter
Randomization Order:Excel generated
Chromatography Type:GC