1. 30 November and 1 December 2015, Blankenberge
1H-NMR-based metabolomics to detect and differentiate cancer types and its added value for lung cancer risk models
Evelyne Louis
Hasselt University
YBMRS 2015
30 November and 1 December 2015, Blankenberge

2. Content Introduction General research question:
“Can NMR metabolomics improve the identification of high-risk individuals eligible for low-dose computed tomography (LDCT) screening?”
Can we do more? Differentiate between lung and breast cancer?
Conclusions and future perspectives
Lung cancer vs. control
16/11/2018

3. Introduction Long, 2de na prostaat bij mannen
Long, 3de na borst en colorectaal bij vrouwen
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4. Introduction
Early detection of lung cancer is required to achieve a substantial decline in lung cancer mortality
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5. Introduction Low-dose computed tomography (LDCT) High sensitivity
Reduction in lung cancer mortality
High rate of false positive results
Interest in improving current risk models which only take clinical risk parameters (e.g. age, BMI, smoking, …) into account for a better selection of high-risk individuals eligible for LDCT screening
Radiation dose: 1,5 mSv – Standard CT = 7 mSv
Detects lung cancer as small as 1 mm in diameter
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6. Introduction
Blood-based diagnostic biomarkers represent an appealing option to complement current risk models
Non-invasive
Minimal risk for the patient
The –omics of systems biology have become increasingly popular
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7. Introduction Metabolomics is complementary to other – omics, and:
High-throughput
Relatively cheap on a per sample basis
Reflects changes in the metabolic phenotype
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8. Introduction 1H-NMR spectroscopy Structural information
Highly reproducible
Non-destructive
Minimal sample preparation
Less sensitive than mass spectrometry
1H-NMR-detected plasma metabolites might be useful parameters to be added to current risk models for a better selection of high-risk individuals eligible for LDCT screening
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9. General research question
Does the plasma metabolic phenotype has potential to improve the identification of high-risk individuals eligible for LDCT screening?
Age
BMI
Smoking … +
NMR metabolomics
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10. Metabolomics Congres - Glasgow 2013
Lung cancer vs. control
Does the analysis of the metabolic composition of blood plasma by 1H-NMR spectroscopy allows to detect lung cancer?
NMR metabolomics
16/11/2018

11. Lung cancer vs. control 1H-NMR spectrum Lung cancer patients Controls
1H-NMR spectroscopy
Spiking
Valine
VAR 1
VAR 2
VAR 3
VAR 4
110 integration regions
VAR 5
VAR 6
Reference plasma
Multivariate statistics
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12. Lung cancer vs. control
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13. Training of a classification model Validation in an independent cohort
Lung cancer vs. control
Training of a classification model
182 out of 233 LC (78%) correctly classified
208 out of 226 C (92%)
correctly classified
Validation in an independent cohort
Sensitivity: 71% (70/98)
Specificity: 81% (72/89)
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14.  Lung cancer vs. control 16/11/2018 evelyne.louis@uhasselt.be
NMR metabolomics
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15. + Lung cancer vs. control Age BMI Smoking Misclassification error: 24%…
Misclassification error: 24%
Age
BMI
Smoking … +
Misclassification error: 18%
NMR metabolomics
16/11/2018

16.  + Lung cancer vs. control Age BMI Smoking … 16/11/2018
NMR metabolomics
16/11/2018

17. Content Introduction General research question:
“Can NMR metabolomics improve the identification of high-risk individuals eligible for low-dose computed tomography (LDCT) screening?”
Can we do more? Differentiate between lung and breast cancer?
Conclusions and future perspectives
Lung cancer vs. control
16/11/2018

18. Metabolomics Congres - Glasgow 2013
Lung vs. breast cancer
Does the plasma metabolic phenotype enables to differentiate between lung and breast cancer?
NMR metabolomics
16/11/2018

19. Lung vs. breast cancer 1H-NMR spectrum Lung cancer patients
Breast cancer patients
1H-NMR spectroscopy
VAR 1
VAR 2
VAR 3
VAR 4
VAR 5
VAR 6
110 VAR
Multivariate statistics
16/11/2018

20. Lung vs. breast cancer
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21. Training of a classification model Validation in an independent cohort
Lung vs. breast cancer
Training of a classification model
50 out of 54 LC (93%) correctly classified
79 out of 80 BC (99%)
correctly classified
Validation in an independent cohort
Sensitivity: 89% (72/81)
Specificity: 82% (49/60)
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22.  Lung vs. breast cancer 16/11/2018 evelyne.louis@uhasselt.be
NMR metabolomics
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23. Preliminary data 31 out of 37 LC (84%) correctly classified
35 out of 37 BC (95%)
correctly classified
29 out of 37 CRC (78%)
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24. Conclusions NMR metabolomics allows to detect lung cancer
has added value to improve current risk models for a better selection of high-risk individuals eligible for LDCT screening
enables to differentiate between cancer types
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25. Future perspectives
Validation in large-scale, prospective screening studies with asymptomatic, high-risk individuals who are eligible for LDCT screening
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26. Acknowledgements Research team Prof. dr. Peter Adriaensens
Prof. dr. Michiel Thomeer
Prof. dr. Liesbet Mesotten
Dr. Karolien Vanhove
Ma. Gunter Reekmans
Mevr. Liene Bervoets
Prof. dr. Ziv Shkedy
Mr. Theophile Bigirumurame
University Biobank Limburg
LCRP, sponsored by the foundation Limburg Sterk Merk, province of Limburg and Flemish government