Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

Abstract

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

Keywords: biomarkers; early detection; lung cancer; metabolomics; screening study.

Figure 1

General characterization of the serum metabolite profile: (A)—numbers of metabolites in different classes used in quantitative and binary analyses (chart shows the relative contribution of compounds used in either type of analysis). (B)—relative contribution of different classes of lipids to the aggregated concentration of whole detected lipids. (C)—the global structure of the dataset. Spatial visualization was created using the UMAP data transformation from 385-dimensional metabolic space to 2D view, preserving the structure of the high-dimensional data to explore the potential sample clustering. Samples from MOLTEST and SMAC cohorts are marked with circles and triangles, respectively (Ctr—controls, LN—benign lung nodules, LC—lung cancer).

Figure 2

Aggregated concentrations of different classes of metabolites in serum samples of healthy controls (Ctr), individuals with benign lung nodules (LN), and lung cancer patients (LC). Boxplots show the minimum and maximum values, lower and upper quartile, and median (outliers are marked with gray circles); represented are FDR-corrected results of the Kruskal−Wallis test for a general variance and the posthoc test p-values for a significance of differences between pairwise compared groups (n = 154 in each group).

Figure 3

Levels of selected metabolites separately in two cohorts participating in lung cancer screening programs: (A)—aggregated concentration of total lipids, triglycerides, and cholesteryl esters in serum samples of healthy controls (Ctr), individuals with benign lung nodules (LN), and lung cancer patients (LC). (B)—concentration of selected compounds in serum samples from three analyzed groups. Boxplots show the minimum and maximum values, lower and upper quartile, and median (outliers are marked with gray circles); represented are the eta squared effect size values for a general variance and the Pallant r effect size for a significance of differences between pairwise compared groups (n = 123 and 31 in each group for MOLTEST and SMAC cohort, respectively); significance levels of effect size: N—negligible, S—small, M—medium, L—large.