NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Citation: Derveaux, E.; Geubbelmans, M.; Criel, M.; Demedts, I.; Himpe, U.; Tournoy, K.; Vercauter, P.; Johansson, E.; Valkenborg, D.; Vanhove, K.; et al. Simple Summary: The initiation of non-small-cell lung cancer (NSCLC) causes significant changes in a patient's blood plasma metabolomic profile. Locally and early-advanced NSCLC patients receive a complete surgical resection of the lung tumor, but the level of metabolic changes after this surgical treatment is currently unknown. By collecting multiple blood plasma samples before and after complete NSCLC removal, metabolic changes can be detected by analyzing the patient's plasma using proton nuclear magnetic resonance (NMR) spectroscopy. Detection of significant changes in the plasma metabolism, a so-called metabolic shift in the patient's blood plasma after surgical tumor resection, indicates the absence of disease recurrence and thus can provide an indication of a good prognosis. Abstract: Background: Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre-and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1 H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre-and ...