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Published byΣαλώμη Κοσμόπουλος Modified over 6 years ago
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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
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Content Introduction Research questions and methodology Results
Conclusion and future perspectives 18/11/2018
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Introduction Lung cancer incidence Lung cancer mortality 18/11/2018
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Introduction 18/11/2018
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Introduction Most common cause of cancer death
Often diagnosed in a metastatic stage Average 5-year survival rate: ±15% 18/11/2018
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Introduction 18/11/2018
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Content Introduction Research questions and methodology Results
Conclusion and future perspectives 18/11/2018
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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? 18/11/2018
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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) 18/11/2018
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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 18/11/2018
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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 18/11/2018
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Content Introduction Research questions and methodology Results
Conclusion and future perspectives 18/11/2018
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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 18/11/2018
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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 18/11/2018
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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 18/11/2018
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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 18/11/2018
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Content Introduction Research questions and methodology Results
Conclusion and future perspectives 18/11/2018
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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 18/11/2018
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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
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