Real-time breath metabolomics as catalyst for personalized lung cancer diagnostics: prospective matched case-control...

Exhaled breath analysis offers notable advantages as a non-invasive method for obtaining biological information from lung cancer patients. However, since the 1980s, its successful translation into clinical practice has remained elusive. The primary challenges include the low concentrations of metabolites in exhaled breath, complexities in breath collection methodologies, difficulties in process standardisation, limited molecular coverage across different methods, and challenges in compound identification and in understanding their molecular origin. Comprehensive reviews by Amann et al. [2011], Hanna et al. [2018], Schmidt et al. [2023], and Vadala et al. [2023] provide holistic insights into the dynamic field of lung cancer breath research. This study aimed to evaluate the efficacy of real-time secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) in differentiating lung cancer patients from matched controls based on breath metabolomic profiles.

This prospective matched case-control study analysed 178 patients. The study included treatment-naive lung cancer patients and controls matched (1:1) on age, sex, and smoking status. SESI-HRMS was used for real-time breath analysis. Data processing was conducted through a validated multistep analytical framework. Statistical evaluation incorporated multivariate techniques and machine learning algorithms. High-resolution mass spectral features were assigned following the Schymanski [2014] classification, enabling the identification of distinct metabolic alterations.

SESI-HRMS identified 3,750 exhaled breath features. T-tests revealed 608 features with significant differences in intensity (P≤0.05) between cases and controls, of which 18 features remained significant after multiple testing correction (q≤0.05). Prediction model achieved reasonable performances. Cancer vs. controls was predicted with an accuracy of 0.75, sensitivity and specificity of 0.80 and 0.71, respectively. Functional enrichment analysis highlighted distinct metabolic pathways for different histological cancer types, including de novo fatty acid metabolism in adenocarcinoma and glucose metabolism in squamous cell carcinoma.

Real-time SESI-HRMS breath analysis differentiated lung cancer patients with acceptable accuracy from matched controls and provides valuable metabolic insights in lung cancer. This non-invasive approach could complement existing methods like genome profiling and low-dose computed tomography, potentially enhancing early detection and personalised treatment strategies towards a multi-omics approach. Further research is warranted to validate these preliminary findings and to refine the identification of putative breath biomarkers.