Non-invasive prediction of lung cancer histological types through exhaled breath analysis by UV-irradiated electronic nose and GC/QTOF/MS

Abstract

Lung cancer (LC) is one of the most lethal diseases from the last decades. Accurate diagnosis of LC histology could lead to the prescription of personalized medical treatment to the affected subjects, which could reduce the mortality rate. We present here an experimental study performed in the pulmonology units of three hospitals from Morocco to non-invasively detect LC and predict LC histology via the analysis of the volatile organic compounds (VOCs) emitted through breathing. Gas chromatography coupled to a quadrupole time-of-flight mass spectrometer (GC/QTOF/MS) employed to detect the breath VOCs, revealed 30 discriminative VOCs in the breath of healthy subjects and LC patients; among them, 4 unique breath VOCs were found for the first time in the breath of LC patients, and could be used as new biomarkers for future LC diagnosis. Besides, an electronic nose (e-nose) system using a novel sensing technique in breath analysis, based on UV-irradiation of the gas sensors, was employed to characterize the overall composition of the collected breath samples, providing a satisfactory discrimination between the breath patterns of LC patients and healthy subjects. Importantly, the e-nose could further discriminate with high accuracy between the two types of LC (non-small cell LC and small cell LC), as well as between two of the major subtypes of non-small cell LC, namely squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The reported results prove that breath analysis with chemical gas sensors and analytical techniques can provided an accurate mean for the non-invasive diagnosis of LC and LC histology.