Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

Abstract Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC

Alkylresorcinols as a New Type of Gut Microbiota Regulators Influencing Immune Therapy Efficiency in Lung Cancer Treatment

Background. Alkylresorcinols (ARs) are polyphenolic compounds of microbial origin with a wide spectrum of biological activities and are potentially involved in host immune functioning. The present study is aimed at evaluating alterations in AR content in blood serum and faeces from healthy donors and patients with lung cancer in connection with response to immune checkpoint inhibitor (ICI) therapy to estimate the regulatory potential of AR. Methods. Quantitative analysis of AR levels, as well as other microbial metabolites in blood serum and faeces, was performed using gas chromatography with mass spectrometric detection; estimation of lymphocyte subsets was performed by flow cytometry; faecal microbiota transplantation (FMT) from lung cancer patients after ICI therapy to germ-free mice was performed to explore whether the intestinal microbiota could produce AR molecules. Results. AR concentrations in both faeces and serum differ dramatically between healthy and lung cancer donors. The significant increase in AR concentrations in mouse faeces after FMT points to the microbial origin of ARs. For several ARs, there were strong positive and negative correlations in both faeces and serum with immune cells and these interrelationships differed between the therapy-responsive and nonresponsive groups. Conclusions. The content of ARs may influence the response to ICI therapy in lung cancer patients. ARs may be considered regulatory molecules that determine the functioning of antitumor immunity