1. Young Belgium Magnetic Resonance Scientist 2014
Validation of 1H-NMR-based metabolomics as a tool to detect lung cancer in human blood plasma
Evelyne Louis
Cluster Oncology
Young Belgium Magnetic Resonance Scientist 2014
24 and 25 November 2014, Spa

2. Content Introduction Research questions and methodology Results
Conclusion and future perspectives
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3. Introduction Lung cancer incidence Lung cancer mortality 18/11/2018

4. Introduction
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5. Introduction Most common cause of cancer death
Often diagnosed in a metastatic stage
Average 5-year survival rate: ±15%
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6. Introduction
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7. Content Introduction Research questions and methodology Results
Conclusion and future perspectives
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8. Metabolomics Congres - Glasgow 2013
Research questions
Does the analysis of the metabolic composition of blood plasma by 1H-NMR spectroscopy allows to detect lung cancer? Can a statistical classifier be constructed by means of multivariate statistics? Is it possible to validate this statistical classifier with an acceptable predictive accuracy?
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9. Research methodology 1H-NMR spectrum Controls Lung cancer patients
Analysis by 1H-NMR spectroscopy
Valine
Reference plasma
(1mg/100µl plasma)
110 integration regions or variables (VAR)
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10. Research methodology 1H-NMR spectrum Controls Lung cancer patients
Analysis by 1H-NMR spectroscopy
Biological interpretation
VAR 1
VAR 2
VAR 3
VAR 4
VAR 5
110 VAR
VAR 6
Multivariate statistics
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11. Subject characteristics
Training cohort
Validation cohort
Lung cancer patients (LC)
Controls (C)
Number
209
199
Gender
M: 70%
F: 30%
M: 52%
F: 48%
Average age
68 ± 10
67 ± 11
Average BMI
25.8 ± 4.6
28.2 ± 5.1
Lung cancer patients (LC)
Controls (C)
Number 50 64
Gender
M: 58%
F: 42%
M: 44%
F: 56%
Average age
67 ± 9
70 ± 10
Average BMI
25.7 ± 4.2
28.3 ± 6.1
Examine predictive accuracy of statistical classifier
Construct statistical classifier
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12. Content Introduction Research questions and methodology Results
Conclusion and future perspectives
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13. Training cohort – 110 VAR ↓ in LC ↑ in LC ●: C ●: LC
183 out of 199 (92%) correctly classified
169 out of 209 (81%)
correctly classified
OPLS-DA plot
Discrimination
↓ in LC
S-plot
Biomarkers
↑ in LC
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14. Validation cohort – 110 VAR
●: LC
Specificity: 72% (46/64)
Sensitivity: 86% (43/50)
ROC-curve
AUC training cohort: 0.86
AUC validation cohort: 0.93
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15. Training cohort – 28 VAR ↓ in LC ↑ in LC ↓ in LC Lactate Lipids
Glucose Threonine
Myo-inositol Citrate
β-hydroxybutyrate
S-plot
Biomarkers
↑ in LC
●: C
●: LC
176 out of 199 (88%)
correctly classified 150 out of 209 (72%) correctly classified
OPLS-DA plot
Discrimination
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16. Validation cohort – 28 VAR
●: LC
Specificity: 83% (53/64)
Sensitivity: 90% (45/50)
AUC training cohort: 0.80
AUC validation cohort: 0.86
ROC-curve
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17. Content Introduction Research questions and methodology Results
Conclusion and future perspectives
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18. Conclusion and future perspectives
Both statistical classifiers show a good predictive accuracy
Future experiments are ongoing to investigate whether the constructed classifiers have potential as a valid screening tool
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19. Acknowledgements Research team University Biobank Limburg
Prof. dr. Peter Adriaensens
Prof. Dr. Michiel Thomeer
Prof. Dr. Liesbet Mesotten
Ma. Gunter Reekmans
Dr. Kirsten Stinkens
Dr. Karolien Vanhove
Mevr. Liene Bervoets
University Biobank Limburg
Limburg Clinical Research Program, sponsored by the foundation Limburg Sterk Merk, province of Limburg and Flemish government