Summary of study ST000369

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000293. The data can be accessed directly via it's Project DOI: 10.21228/M85P57 This work is supported by NIH grant, U2C- DK119886.

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Study IDST000369
Study TitleInvestigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer (part II)
Study TypeLung cancer case control biomarker discovery
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.
Institute
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
Emailofiehn@ucdavis.edu
Phone(530) 754-8258
Submit Date2016-04-13
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 Date2016-04-03
Release Version1
Oliver Fiehn Oliver Fiehn
https://dx.doi.org/10.21228/M85P57
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:


Project:

Project ID:PR000293
Project DOI:doi: 10.21228/M85P57
Project Title:Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer
Project Summary:Recently, 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.
Institute:University of California, Davis
Department:Genome and Biomedical Sciences Facility
Laboratory:WCMC Metabolomics Core
Last Name:Fiehn
First Name:Oliver
Address:1315 Genome and Biomedical Sciences Facility, 451 Health Sciences Drive, Davis, CA 95616
Email:ofiehn@ucdavis.edu
Phone:(530) 754-8258
Funding Source:NIH U24DK097154

Subject:

Subject ID:SU000390
Subject Type:Human
Subject Species:Homo sapiens
Gender:M/F
Human Smoking Status:Former/Current
Species Group:Human

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Organ Cancer status Smoker Gender
SA016713130730dlvsa37_3Plasma Adenocarcinoma Current F
SA016714130730dlvsa28_1Plasma Adenocarcinoma Current F
SA016715130729dlvsa13_1Plasma Adenocarcinoma Current F
SA016716130729dlvsa46_3Plasma Adenocarcinoma Current F
SA016717130729dlvsa28_1Plasma Adenocarcinoma Current F
SA016718130729dlvsa48_2Plasma Adenocarcinoma Current F
SA016719130731dlvsa26_1Plasma Adenocarcinoma Current F
SA016720130731dlvsa44_1Plasma Adenocarcinoma Current F
SA016721130729dlvsa17_1Plasma Adenocarcinoma Current F
SA016722130729dlvsa30_2Plasma Adenocarcinoma Current M
SA016723130730dlvsa13_1Plasma Adenocarcinoma Current M
SA016724130729dlvsa36_2Plasma Adenocarcinoma Current M
SA016725130731dlvsa36_1Plasma Adenocarcinoma Former F
SA016726130729dlvsa07_1Plasma Adenocarcinoma Former F
SA016727130730dlvsa16_1Plasma Adenocarcinoma Former F
SA016728130730dlvsa32_1Plasma Adenocarcinoma Former F
SA016729130730dlvsa26_1Plasma Adenocarcinoma Former F
SA016730130730dlvsa46_1Plasma Adenocarcinoma Former F
SA016731130731dlvsa28_1Plasma Adenocarcinoma Former F
SA016732130731dlvsa14_1Plasma Adenocarcinoma Former F
SA016733130731dlvsa02_1Plasma Adenocarcinoma Former F
SA016734130731dlvsa32_1Plasma Adenocarcinoma Former F
SA016735130729dlvsa09_1Plasma Adenocarcinoma Former F
SA016736130731dlvsa34_1Plasma Adenocarcinoma Former F
SA016737130730dlvsa18_1Plasma Adenocarcinoma Former F
SA016738130729dlvsa50_2Plasma Adenocarcinoma Former F
SA016739130730dlvsa01_2Plasma Adenocarcinoma Former F
SA016740130801dlvsa02_1Plasma Adenocarcinoma Former F
SA016741130729dlvsa24_1Plasma Adenocarcinoma Former F
SA016742130729dlvsa21_1Plasma Adenocarcinoma Former F
SA016743130730dlvsa03_2Plasma Adenocarcinoma Former F
SA016744130729dlvsa42_3Plasma Adenocarcinoma Former F
SA016745130730dlvsa07_2Plasma Adenocarcinoma Former F
SA016746130731dlvsa24_1Plasma Adenocarcinoma Former M
SA016747130730dlvsa20_1Plasma Adenocarcinoma Former M
SA016748130731dlvsa10_2Plasma Adenocarcinoma Former M
SA016749130729dlvsa11_1Plasma Adenocarcinoma Former M
SA016750130730dlvsa44_1Plasma Adenocarcinoma Former M
SA016751130730dlvsa05_2Plasma Adenocarcinoma Former M
SA016752130729dlvsa15_1Plasma Adenocarcinoma Former M
SA016753130731dlvsa18_1Plasma Adenocarcinoma Former M
SA016754130731dlvsa38_1Plasma Adenocarcinoma Former M
SA016755130730dlvsa33_1Plasma Adenocarcinoma Former M
SA016756130730dlvsa35_1Plasma Adenocarcinoma Former M
SA016757130731dlvsa22_1Plasma Adenocarcinoma Former M
SA016758130801dlvsa08_1Plasma Adenocarcnoma Former F
SA016759130801dlvsa04_1Plasma Adenocarcnoma Former F
SA016760130801dlvsa10_1Plasma Adenocarcnoma Former F
SA016761130729dlvsa38_2Plasma Adenosquamous Current F
SA016762130731dlvsa16_1Plasma Adenosquamous Current M
SA016763130729dlvsa22_1Plasma Adenosquamous Former M
SA016764130729dlvsa32_2Plasma Healthy Current F
SA016765130729dlvsa44_3Plasma Healthy Current F
SA016766130729dlvsa40_2Plasma Healthy Current F
SA016767130731dlvsa08_1Plasma Healthy Current F
SA016768130731dlvsa30_1Plasma Healthy Current F
SA016769130730dlvsa38_3Plasma Healthy Current F
SA016770130731dlvsa50_1Plasma Healthy Current M
SA016771130729dlvsa19_1Plasma Healthy Current M
SA016772130730dlvsa50_1Plasma Healthy Current M
SA016773130731dlvsa12_1Plasma Healthy Current M
SA016774130731dlvsa42_1Plasma Healthy Former F
SA016775130731dlvsa40_1Plasma Healthy Former F
SA016776130729dlvsa26_1Plasma Healthy Former F
SA016777130731dlvsa46_1Plasma Healthy Former F
SA016778130801dlvsa12_1Plasma Healthy Former F
SA016779130729dlvsa34_2Plasma Healthy Former F
SA016780130730dlvsa30_1Plasma Healthy Former F
SA016781130731dlvsa04_1Plasma Healthy Former F
SA016782130731dlvsa06_1Plasma Healthy Former F
SA016783130801dlvsa06_1Plasma Healthy Former F
SA016784130731dlvsa20_1Plasma Healthy Former F
SA016785130729dlvsa01_1Plasma Healthy Former F
SA016786130730dlvsa40_1Plasma Healthy Former F
SA016787130729dlvsa03_1Plasma Healthy Former M
SA016788130729dlvsa05_1Plasma Healthy Former M
SA016789130730dlvsa42_1Plasma Healthy Former M
SA016790130730dlvsa24_1Plasma Healthy Former M
SA016791130731dlvsa48_1Plasma Healthy Former M
SA016792130730dlvsa22_1Plasma Healthy Former M
SA016793130730dlvsa48_2Plasma Healthy Former M
SA016794130730dlvsa09_1Plasma Healthy Former M
SA016795130730dlvsa11_1Plasma Healthy Former M
SA016796130801dlvsa28_1Plasma NA NA NA
SA016797130801dlvsa20_1Plasma NA NA NA
SA016798130801dlvsa17_2Plasma NA NA NA
SA016799130801dlvsa18_2Plasma NA NA NA
SA016800130801dlvsa19_2Plasma NA NA NA
SA016801130801dlvsa22_1Plasma NA NA NA
SA016802130801dlvsa24_1Plasma NA NA NA
SA016803130801dlvsa25_1Plasma NA NA NA
SA016804130801dlvsa23_1Plasma NA NA NA
SA016805130801dlvsa16_1Plasma NA NA NA
SA016806130801dlvsa21_1Plasma NA NA NA
SA016807130801dlvsa26_1Plasma NA NA NA
SA016808130801dlvsa27_1Plasma NA NA NA
SA016809130801dlvsa29_1Plasma NA NA NA
SA016810130801dlvsa30_2Plasma NA NA NA
SA016811130801dlvsa14_1Plasma NA NA NA
SA016812130801dlvsa15_1Plasma NA NA NA
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Collection:

Collection ID:CO000384
Collection Summary:Blood samples (serum and plasma) were collected from NSCLC adenocarcinoma and control subjects with patient consent using approved IRB protocols (LC001 for cancer cases and LC002 for control cases).
Collection Protocol Filename:Cancer Epidemiol Biomarkers Prev-2015-Fahrmann-1716-23.pdf
Sample Type:Blood

Treatment:

Treatment ID:TR000404
Treatment Summary:Subjects were recruited over a 4-year period (2010–2014) from the UC Davis Medical Center and Cancer Center Clinics. All subjects were diagnosed with NSCLC adenocarcinoma before specimen collection. The control population was heavily recruited from spouses and family members accompanying a lung cancer patient to their clinic to maintain as much of a similar environment and life styles, especially diet and smoking history, as possible. Cases were frequency matched with controls for gender, age, and smoking history. Only cases diagnosed with NSCLC adenocarcinoma were used in these studies. Fasting status was not controlled for as individuals were recruited upon their arrival to the clinic. 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.
Treatment Protocol Filename:Cancer Epidemiol Biomarkers Prev-2015-Fahrmann-1716-23.pdf
Human Fasting:None

Sample Preparation:

Sampleprep ID:SP000397
Sampleprep Summary:1. Switch on bath to pre-cool at –20°C (±2°C validity temperature range) 2. Gently rotate or aspirate the blood samples for about 10s to obtain a homogenised sample. 3. Aliquot 30μl of plasma sample to a 1.0 mL extraction solution. The extraction solution has to be prechilled using the ThermoElectron Neslab RTE 740 cooling bath set to -20°C. 4. Vortex the sample for about 10s and shake for 5 min at 4°C using the Orbital Mixing Chilling/Heating Plate. If you are using more than one sample, keep the rest of the sample on ice (chilled at <0°C with sodium chloride). 5. Centrifuge samples for 2min at 14000 rcf using the centrifuge Eppendorf 5415 D. 6. Aliquot two 450μL portions of the supernatant. One for analysis and one for a backup sample. Store the backup aliquot in -20°C freezer. 7. Evaporate one 450μL aliquots of the sample in the Labconco Centrivap cold trap concentrator to complete dryness. 8. The dried aliquot is then re-suspended with 450 μL 50% acetonitrile (degassed as given above). 9. Centrifuged for 2 min at 14000 rcf using the centrifuge Eppendorf 5415. 10. Remove supernatant to a new Eppendorf tube. 11. Evaporate the supernatant to dryness in the Labconco Centrivap cold trap concentrator. 12. Submit to derivatization.
Sampleprep Protocol Filename:SOP blood-GCTOF-11082012.pdf

Combined analysis:

Analysis ID AN000603
Analysis type MS
Chromatography type GC
Chromatography system Agilent 6890N
Column Restek Corporation Rtx-5Sil MS
MS Type EI
MS instrument type GC-TOF
MS instrument name Leco Pegasus IV TOF
Ion Mode POSITIVE
Units counts

Chromatography:

Chromatography ID:CH000429
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 Temperature:50-330C
Flow Rate:1 ml/min
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

MS:

MS ID:MS000536
Analysis ID:AN000603
Instrument Name:Leco Pegasus IV TOF
Instrument Type:GC-TOF
MS Type:EI
Ion Mode:POSITIVE
Ion Source Temperature:250 C
Ionization Energy:70 eV
Mass Accuracy:Nominal
Source Temperature:250 C
Scan Range Moverz:85-500 Da
Scanning Cycle:17 Hz
Scanning Range:85-500 Da
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