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

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 1H-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 postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

“…However, this can even be further improved by reducing patient-to-patient (donor-intrinsic) variabilities and modelling only the metabolic changes that are related to the onset of lung cancer. To overrule these variabilities, Derveaux et al performed another study with a longitudinal setup in which the pre-and postoperative plasma metabolite profiles of the same individuals, all early-stage NSCLC patients that received surgery, were compared [20]. Analysis of the pre-and postoperative plasma samples provides early-stage NSCLC and healthy metabolite profiles, respectively.…”

“…OPLS-DA was used in the studies described here, being common in NMR-metabolomics. The obtained statistical model can then be interpret using different model parameters; R2X (the goodness of fit; variation within the groups explained by the model; indicated on the predictive vertical axis), R2Y (explanation between the groups; indicated on the horizontal axis), and Q2 (the predictive accuracy of the trained model) [20]. The closer the values of R2X and R2Y to 1, the better the model.…”

“…The closer the values of R2X and R2Y to 1, the better the model. A Q2 value > 0.30 is considered as a high predictive model in the context of metabolomics [20]. Training of the classification model will always be performed using data from the training cohort, while additional independent validation of the model will always be performed using data from the independent validation cohort, where no group information will be provided.…”

“…Training of the classification model will always be performed using data from the training cohort, while additional independent validation of the model will always be performed using data from the independent validation cohort, where no group information will be provided. High sensitivity and specificity rates in this validation cohort will therefore indicate the actual differentiation between the two groups [20]. Another important parameter that is taken into account is the area under the curve (AUC) value of the receiver operating curves (ROCs), which indicates the overall performance of both training and validation models.…”

“…Part III highlights a second study of Derveaux et al that focuses on a deeper metabolic interpretation of early-stage lung cancer by comparing pre-and postoperative plasma metabolite profiles of patients diagnosed with early-staged NSCLC [20]. Importantly, pre-and postoperative plasma samples were donated by the same patients at different timepoints before and after complete surgical resection (i.e.…”