Background SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool. Methods Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92. Results Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs. Conclusion Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

Background Screening decreases mortality among lung cancer patients but is not widely implemented, thus there is an unmet need for an easily accessible non-invasive method to enable early diagnosis. Particles in exhaled air offer a promising such diagnostic tool. We investigated the validity of a particles in exhaled air device (PExA) to measure the particle flow rate (PFR) and collect exhaled breath particles (EBP) to diagnose primary lung adenocarcinoma (LUAD). Methods Seventeen patients listed for resection of LUAD stages IA–IIIA and 18 non-cancer surgical control patients were enrolled. EBP were collected before and after surgery for LUAD, and once for controls. Proteomic analysis was carried out using a proximity extension assay technology. Results were validated in both plasma from the same cohort and with microarray data from healthy lung tissue and LUAD tissue in the GSE10072 dataset. Results Of the 92 proteins analyzed, levels of five proteins in EBP were significantly higher in the LUAD patients compared to controls. Levels of phospholipid transfer protein (PLTP) and hepatocyte growth factor receptor (MET) decreased in LUAD patients after surgery compared to control patients. PFR was significantly higher in the LUAD cohort at all timepoints compared to the control group. MET in plasma correlated significantly with MET in EBP. Conclusion Collection of EBP and measuring of PFR has never been performed in patients with LUAD. In the present study PFR alone could distinguish between LUAD and patients without LUAD. PLTP and MET were identified as potential biomarkers to evaluate successful tumor excision.

Non-small cell lung cancer (NSCLC) is associated with low survival rates, often due to late diagnosis and lack of personalized medicine. Diagnosing and monitoring NSCLC using blood samples has lately gained interest due to its less invasive nature. In the present study, plasma was collected at three timepoints and analyzed using proximity extension assay technology and quantitative real-time polymerase chain reaction in patients with primary NSCLC stages IA–IIIA undergoing surgery. Results were adjusted for patient demographics, tumor, node, metastasis (TNM) stage, and multiple testing. Major histocompatibility (MHC) class 1 polypeptide-related sequence A/B (MIC-A/B) and tumor necrosis factor ligand superfamily member 6 (FASLG) were significantly increased post-surgery, suggesting radical removal of cancerous cells. Levels of hepatocyte growth factor (HGF) initially increased postoperatively but were later lowered, potentially indicating radical removal of malignant cells. The levels of FASLG in patients who later died or had a relapse of NSCLC were lower at all three timepoints compared to surviving patients without relapse, indicating that FASLG may be used as a prognostic biomarker. The biomarkers were confirmed using microarray data. In conclusion, quantitative proteomics could be used for NSCLC identification but may also provide information on radical surgical removal of NSCLC and post-surgical prognosis.

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