Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath.

BACKGROUND: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. RESEARCH QUESTION: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? STUDY DESIGN AND METHODS: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. RESULTS: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). INTERPRETATION: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer.

OBJECTIVES: Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. MATERIALS AND METHODS: This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. RESULTS: In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89-1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91-1.00). CONCLUSION: Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy.

Nose in malignant mesothelioma-Prediction of response to immune checkpoint inhibitor treatment.

INTRODUCTION: Recent clinical trials with immune checkpoint inhibitors (ICIs) have shown that a subgroup of patients with malignant pleural mesothelioma (MPM) could benefit from these agents. However, there are no accurate biomarkers to predict who will respond. The aim of this study was to assess the accuracy of exhaled breath analysis using electronic technology (eNose) for discriminating between responders to ICI and non-responders. METHODS: This proof-of-concept prospective observational study was part of an intervention study (INITIATE) in patients with recurrent MPM who were treated with nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA-4). At baseline and after six weeks of treatment, breath profiles were collected by an eNose. Modified Response Evaluation Criteria in Solid Tumors were used to assess efficacy at 6-month follow-up. For data processing and statistics, we used independent t-test analyses followed by linear discriminant and receiver-operating characteristic (ROC) analysis. RESULTS: Exhaled breath data of 31 MPM patients who received nivolumab plus ipilimumab were available at baseline. There were 16 with and 15 without a response after 6 months of treatment. At baseline, breath profiles significantly differed between responders and non-responders, with a cross validation value of 71%. The ROC-AUC after internal cross-validation was 0.90 (confidence interval: 0.80-1.00). CONCLUSION: An eNose is able to discriminate at baseline between responders and non-responders to nivolumab plus ipilimumab in MPM, thereby potentially identifying a subgroup of patients that will benefit from ICI treatment.