Evelyne Louis1, Peter Adriaensens2, Wanda Guedens3, Theophile Bigirumurame4, Kurt Baeten5, Karolien Vanhove6, Kurt Vandeurzen7, Karen Darquennes8, Johan Vansteenkiste9, Christophe Dooms9, Ziv Shkedy4, Liesbet Mesotten10, Michiel Thomeer11. 1. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium. 2. Biomolecule Design Group, Institute for Materials Research, Hasselt University, Hasselt, Belgium; Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Hasselt, Belgium. 3. Biomolecule Design Group, Institute for Materials Research, Hasselt University, Hasselt, Belgium. 4. Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium. 5. Janssen Diagnostics BVBA, Beerse, Belgium. 6. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium; Department of Respiratory Medicine, Algemeen Ziekenhuis Vesalius, Tongeren, Belgium. 7. Department of Respiratory Medicine, Mariaziekenhuis Noord-Limburg, Overpelt, Belgium. 8. Department of Respiratory Medicine, Ziekenhuis Maas en Kempen, Maaseik, Belgium. 9. Department of Respiratory Medicine, Universitaire Ziekenhuizen Katholieke Universiteit Leuven, Leuven, Belgium. 10. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium; Department of Nuclear Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium. 11. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium; Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium. Electronic address: michiel.thomeer@uhasselt.be.
Abstract
INTRODUCTION: Low-dose computed tomography, the currently used tool for lung cancer screening, is characterized by a high rate of false-positive results. Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. Therefore, this study aims to evaluate whether the metabolic phenotype of blood plasma allows detection of lung cancer. 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 233 patients with lung cancer and 226 controls. The validity of the model was examined by classifying an independent cohort of 98 patients with lung cancer and 89 controls. RESULTS: The model makes it possible to correctly classify 78% of patients with lung cancer and 92% of controls, with an area under the curve of 0.88. Important moreover is the fact that the model is convincing, which is demonstrated by validation in the independent cohort with a sensitivity of 71%, a specificity of 81%, and an area under the curve of 0.84. Patients with lung cancer have increased glucose and decreased lactate and phospholipid levels. The limited number of patients in the subgroups and their heterogeneous nature do not (yet) enable differentiation between histological subtypes and tumor stages. CONCLUSIONS: Metabolic phenotyping of plasma allows detection of lung cancer, even in an early stage. Increased glucose and decreased lactate levels are pointing to an increased gluconeogenesis and are in accordance with recently published findings. Furthermore, decreased phospholipid levels confirm the enhanced membrane synthesis.
INTRODUCTION: Low-dose computed tomography, the currently used tool for lung cancer screening, is characterized by a high rate of false-positive results. Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. Therefore, this study aims to evaluate whether the metabolic phenotype of blood plasma allows detection of lung cancer. 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 233 patients with lung cancer and 226 controls. The validity of the model was examined by classifying an independent cohort of 98 patients with lung cancer and 89 controls. RESULTS: The model makes it possible to correctly classify 78% of patients with lung cancer and 92% of controls, with an area under the curve of 0.88. Important moreover is the fact that the model is convincing, which is demonstrated by validation in the independent cohort with a sensitivity of 71%, a specificity of 81%, and an area under the curve of 0.84. Patients with lung cancer have increased glucose and decreased lactate and phospholipid levels. The limited number of patients in the subgroups and their heterogeneous nature do not (yet) enable differentiation between histological subtypes and tumor stages. CONCLUSIONS: Metabolic phenotyping of plasma allows detection of lung cancer, even in an early stage. Increased glucose and decreased lactate levels are pointing to an increased gluconeogenesis and are in accordance with recently published findings. Furthermore, decreased phospholipid levels confirm the enhanced membrane synthesis.
Authors: Jacqueline V Aredo; Natasha Purington; Li Su; Sophia J Luo; Nancy Diao; David C Christiani; Heather A Wakelee; Summer S Han Journal: Lung Cancer Date: 2021-03-11 Impact factor: 5.705
Authors: K Vanhove; P Giesen; O E Owokotomo; L Mesotten; E Louis; Z Shkedy; M Thomeer; P Adriaensens Journal: BMC Cancer Date: 2018-09-03 Impact factor: 4.430