Background: The pattern of volatile organic compounds (VOCs) in the exhaled breath of patients with lung cancer may be unique. New sensor systems that detect patterns of VOCs have been developed. One of these sensor systems, a colorimetric sensor array, has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge. The colours of these spots change based on the chemicals with which they come into contact. In this proof of principle study, the ability of this sensor system to detect a pattern of VOCs unique to lung cancer is assessed. Methods: Individuals with lung cancer, those with other lung diseases and healthy controls performed tidal breathing of room air for 12 min while exhaling into a device designed to draw their breath across a colorimetric sensor array. The colour changes that occurred for each individual were converted into a numerical vector. The vectors were analysed statistically, using a random forests technique, to determine whether lung cancer could be predicted from the responses of the sensor. Results: 143 individuals participated in the study: 49 with non-small cell lung cancer, 18 with chronic obstructive pulmonary disease 15 with idiopathic pulmonary fibrosis 20 with pulmonary arterial hypertension 20 with sarcoidosis and 21 controls. A prediction model was developed using observations from 70% of the subjects. This model was able to predict the presence of lung cancer in the remaining 30% of subjects with a sensitivity of 73.3% and a specificity of 72.4% (p = 0.01). Conclusions: The unique chemical signature of the breath of patients with lung cancer can be detected with moderate accuracy by a colorimetric sensor array.

Introduction The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. Objectives To develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. Methods The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models. Results The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825–0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively). Conclusions A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.

Volatile organic compounds are able to be detected in the exhaled breath by a variety of sensing techniques. These volatiles may be produced by cellular metabolic processes, or inhaled/absorbed from exogenous sources. Lung cancer cells may produce and process these compounds different than normal cells. The differences may be detectable in the breath. The following manuscript will review the evidence supporting the premise that a unique chemical signature can be detected in the breath of patients with lung cancer, discuss the results of studies using mass spectrometry and nonspecific chemical sensing techniques to detect the unique lung cancer signature, and speculate on the advancements that must occur to develop a breath test accurate enough to be clinically useful.