E Louis1, P Adriaensens2, W Guedens3, K Vanhove4, K Vandeurzen5, K Darquennes6, J Vansteenkiste7, C Dooms7, E de Jonge8, M Thomeer9, L Mesotten10. 1. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt. 2. Biomolecule Design Group, Institute for Materials Research, Hasselt University, Hasselt Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Hasselt. 3. Biomolecule Design Group, Institute for Materials Research, Hasselt University, Hasselt. 4. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt Department of Respiratory Medicine, Algemeen Ziekenhuis Vesalius, Tongeren. 5. Department of Respiratory Medicine, Mariaziekenhuis Noord-Limburg, Overpelt. 6. Department of Respiratory Medicine, Ziekenhuis Maas en Kempen, Maaseik. 7. Department of Respiratory Medicine, Universitaire Ziekenhuizen KU Leuven, Leuven. 8. Department of Gynaecology, Ziekenhuis Oost-Limburg, Genk, Belgium. 9. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium. 10. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt Department of Nuclear Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium liesbet.mesotten@zol.be.
Abstract
BACKGROUND: Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. However, up to now it is not clear whether different cancer types are characterized by a specific metabolite profile. Therefore, this study aims to evaluate whether the plasma metabolic phenotype allows to discriminate between lung and breast cancer. PATIENTS AND METHODS: The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 80 female breast cancer patients and 54 female lung cancer patients, all with an adenocarcinoma. The validity of the model was examined by permutation testing and by classifying an independent validation cohort of 60 female breast cancer patients and 81 male lung cancer patients, all with an adenocarcinoma. RESULTS: The model allows to classify 99% of the breast cancer patients and 93% of the lung cancer patients correctly with an area under the curve (AUC) of 0.96 and can be validated in the independent cohort with a sensitivity of 89%, a specificity of 82% and an AUC of 0.94. Decreased levels of sphingomyelin and phosphatidylcholine (phospholipids with choline head group) and phospholipids with short, unsaturated fatty acid chains next to increased levels of phospholipids with long, saturated fatty acid chains seem to indicate that cell membranes of lung tumors are more rigid and less sensitive to lipid peroxidation. The other discriminating metabolites are pointing to a more pronounced response of the body to the Warburg effect for lung cancer. CONCLUSION: Metabolic phenotyping of plasma allows to discriminate between lung and breast cancer, indicating that the metabolite profile reflects more than a general cancer marker. CLINICAL TRIAL REGISTRATION NUMBER: NCT02362776.
BACKGROUND: Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. However, up to now it is not clear whether different cancer types are characterized by a specific metabolite profile. Therefore, this study aims to evaluate whether the plasma metabolic phenotype allows to discriminate between lung and breast cancer. PATIENTS AND METHODS: The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 80 female breast cancerpatients and 54 female lung cancer patients, all with an adenocarcinoma. The validity of the model was examined by permutation testing and by classifying an independent validation cohort of 60 female breast cancerpatients and 81 male lung cancerpatients, all with an adenocarcinoma. RESULTS: The model allows to classify 99% of the breast cancerpatients and 93% of the lung cancerpatients correctly with an area under the curve (AUC) of 0.96 and can be validated in the independent cohort with a sensitivity of 89%, a specificity of 82% and an AUC of 0.94. Decreased levels of sphingomyelin and phosphatidylcholine (phospholipids with choline head group) and phospholipids with short, unsaturated fatty acid chains next to increased levels of phospholipids with long, saturated fatty acid chains seem to indicate that cell membranes of lung tumors are more rigid and less sensitive to lipid peroxidation. The other discriminating metabolites are pointing to a more pronounced response of the body to the Warburg effect for lung cancer. CONCLUSION: Metabolic phenotyping of plasma allows to discriminate between lung and breast cancer, indicating that the metabolite profile reflects more than a general cancer marker. CLINICAL TRIAL REGISTRATION NUMBER: NCT02362776.