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

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 evelyne.louis@uhasselt.be

Introduction Long, 2de na prostaat bij mannen Long, 3de na borst en colorectaal bij vrouwen 16/11/2018 evelyne.louis@uhasselt.be

Introduction Early detection of lung cancer is required to achieve a substantial decline in lung cancer mortality 16/11/2018 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

Introduction Metabolomics is complementary to other – omics, and: High-throughput Relatively cheap on a per sample basis Reflects changes in the metabolic phenotype 16/11/2018 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

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 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

Lung cancer vs. control 16/11/2018 evelyne.louis@uhasselt.be

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) 16/11/2018 evelyne.louis@uhasselt.be

 Lung cancer vs. control 16/11/2018 evelyne.louis@uhasselt.be NMR metabolomics 16/11/2018 evelyne.louis@uhasselt.be

+ Lung cancer vs. control Age BMI Smoking Misclassification error: 24% … Misclassification error: 24% Age BMI Smoking … + Misclassification error: 18% NMR metabolomics 16/11/2018 evelyne.louis@uhasselt.be

 + Lung cancer vs. control Age BMI Smoking … 16/11/2018 NMR metabolomics 16/11/2018 evelyne.louis@uhasselt.be

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 evelyne.louis@uhasselt.be

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 evelyne.louis@uhasselt.be

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 evelyne.louis@uhasselt.be

Lung vs. breast cancer 16/11/2018 evelyne.louis@uhasselt.be

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) 16/11/2018 evelyne.louis@uhasselt.be

 Lung vs. breast cancer 16/11/2018 evelyne.louis@uhasselt.be NMR metabolomics 16/11/2018 evelyne.louis@uhasselt.be

Preliminary data 31 out of 37 LC (84%) correctly classified 35 out of 37 BC (95%) correctly classified 29 out of 37 CRC (78%) 16/11/2018 evelyne.louis@uhasselt.be

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 16/11/2018 evelyne.louis@uhasselt.be

Future perspectives Validation in large-scale, prospective screening studies with asymptomatic, high-risk individuals who are eligible for LDCT screening 16/11/2018 evelyne.louis@uhasselt.be

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