Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis

BACKGROUND

Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown.

METHODS

In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits.

RESULTS

Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits.

CONCLUSIONS

Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.

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.

Exhaled breath profiles to detect lung infection with Staphylococcus aureus in children with cystic fibrosis

BACKGROUND

An electronic nose (eNose) can be used to detect volatile organic compounds (VOCs). Exhaled breath contains numerous VOCs and individuals' VOCs mixtures create distinct breath profiles. Previous reports have shown that eNose can detect lung infections. Whether eNose can detect Staphylococcus aureus airway infections in breath of children with cystic fibrosis (CF) is currently unclear.

METHODS

In this cross-sectional observational study, a cloud-connected eNose was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant and receiver operating characteristics (ROC) analyses.

RESULTS

Breath profiles from 100 children with CF (median predicted FEV1 91%) were obtained and analysed. CF patients with positive airway cultures for any CF pathogen were distinguishable from no CF pathogens (no growth or usual respiratory flora) with accuracy of 79.0% (AUC-ROC 0.791; 95% CI: 0.669-0.913) and between patients positive for Staphylococcus aureus (SA) only and no CF pathogen with accuracy of 74.0% (AUC-ROC 0.797; 95% CI: 0.698-0.896). Similar differences were seen for Pseudomonas aeruginosa (PA) infection vs no CF pathogens (78.0% accuracy, AUC-ROC 0.876, 95% CI: 0.794-0.958). SA- and PA-specific signatures were driven by different sensors in the SpiroNose suggesting pathogen-specific breath signatures.

CONCLUSIONS

Breath profiles of CF patients with SA in airway cultures are distinct from those with no infection or PA infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF.

Precision Medicine for More Oxygen (P4O2)-Study Design and First Results of the Long COVID-19 Extension

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40-65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3-6 and 12-18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient's home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3-6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

Abstract 666: A phase 1 study to detect adverse events after SBRT and immunotherapy by electronic nose in advanced NSCLC

It is increasingly understood that cancers can be recognized by the immune system and that inflammation relates to response. Combining stereotactic radiotherapy (SBRT) to increase release of cancer cell antigens with an anti-CTLA-4 and PD-L1 inhibitor may lead to increased response rates. Due to tumor heterogeneity it may be important to irradiate the primary tumor (addressing trunk mutations), instead of its metastasis (branch mutations). No solid predictive biomarker for response and/or immunotherapy related adverse events (TRAEs) is available. Electronic nose (eNose) technology measures the complete mixture of volatile organic compounds in exhaled breath and can detect lung cancer based on pattern recognition of the breath profile. We hypothesize that eNose is able to predict TRAEs and response in patients with NSCLC.

Methods: In 3 sequential cohorts, immunotherapy regimes combined with SBRT were studied in stage IIIB/IV NSCLC patients progressing on chemotherapy. All patients were irradiated on the primary tumor (1x20 Gy on 9cc) 1 week after the 1st dose of immunotherapy. The 1st cohort (n=3) received durvalumab. The 2nd and 3rd cohort (both n=6) received a combination of durvalumab and tremelimumab followed by durvalumab monotherapy. Duplicate eNose measurements were performed by using the SpiroNose that contains 7 metal oxide semiconductor sensors. TRAEs were categorized using NCI CTCAE version 4.3. Descriptive statistics were used to summarize baseline characteristics and TRAEs. The relationship between breath profiles and response and TRAEs was analyzed with advanced signal processing, ambient correction and Mann-Whitney U test. Linear discriminant and receiver operating characteristics analysis followed. Pearson correlation and regression assessed duration of response.

Findings: Fifteen patients were included as described above. Baseline characteristics of the groups were comparable. Median progression free survival was 2 months, overall survival 10 months (immature). No statistical difference was found in (duration of) response. There was 1 low grade TRAE (CTC 1-2) in cohort 1 and 10 in cohort 2/3. High grade TRAEs (CTC 3) were only present in cohort 2/3 (n=3) and 1 patient discontinued treatment. There was one dose limiting toxicity. At baseline, 12/15 patients performed eNose measurements; 7 with TRAEs and 5 without. The TRAE group had a significant higher sensor 7 signal compared to those without TRAE (p=.042). The cross-validated accuracy for detecting TRAE was 67%. The ROC-AUC was .857 [.638-1].

Interpretation: eNose breath profiles at baseline may predict which patients will develop TRAEs. The small sample size increases the risk of overfitting, therefore another cohort of 34 immuno-monotherapy patients is being analyzed (15 with and 19 without TRAEs, pending). We demonstrated no new safety data.

Disclosure: This study was sponsored by a research grant from AstraZeneca.

Citation Format: M Benthe Muntinghe-Wagenaar, Hanneke Kievit, Lucie B. Hijmering-Kappelle, Birgitta I. Hiddinga, J Fred Ubbels, Robin Wijsman, Mechteld F. Brasz, Gitte Slingers, Rianne de Vries, Milou M. Schuurbiers, Harry J. Groen, Huib A. Kerstjens, Anthonie J. van der Wekken, Michel M. van den Heuvel, T Jeroen Hiltermann. A phase 1 study to detect adverse events after SBRT and immunotherapy by electronic nose in advanced NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 666.

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.

Identification of recent exacerbations in COPD patients by electronic nose

Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months. Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA). Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1-4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)-area under the curve (AUC)=0.98, 95% CI 0.97-1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94-1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results. Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.

eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy

BACKGROUND

Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma.

OBJECTIVE

We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma.

METHODS

Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics.

RESULTS

Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics.

CONCLUSION

eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.

Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography-Mass Spectrometry Data

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label

Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath.Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set.This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression.Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R²=0.581) and neutrophilic (R²=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set.Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner.

Breathomics from exhaled volatile organic compounds in pediatric asthma

Asthma is the most common chronic disease in children, and is characterized by airway inflammation, bronchial hyperresponsiveness, and airflow obstruction. Asthma diagnosis, phenotyping, and monitoring are still challenging with currently available methods, such as spirometry, F_E NO or sputum analysis. The analysis of volatile organic compounds (VOCs) in exhaled breath could be an interesting non-invasive approach, but has not yet reached clinical practice. This review describes the current status of breath analysis in the diagnosis and monitoring of pediatric asthma. Furthermore, features of an ideal breath test, different breath analysis techniques, and important methodological issues are discussed. Although only a (small) number of studies have been performed in pediatric asthma, of which the majority is focusing on asthma diagnosis, these studies show moderate to good prediction accuracy (80-100%, with models including 6-28 VOCs), thereby qualifying breathomics for future application. However, standardization of procedures, longitudinal studies, as well as external validation are needed in order to further develop breathomics into clinical tools. Such a non-invasive tool may be the next step toward stratified and personalized medicine in pediatric respiratory disease.

Skin autofluorescence for the risk assessment of chronic complications in diabetes: a broad excitation range is sufficient

Skin autofluorescence (AF) is becoming an accepted clinical method for assessing the risk of chronic complications in diabetes mellitus (DM). In this study, the role of the excitation wavelength in the recognition of increased risk of diabetes-related chronic complications was investigated. An Excitation Emission Matrix Scanner (EEMS) was used to perform noninvasive measurements in four age-matched groups of patients with type 1 and type 2 DM, with and without chronic complications, as well as in a control group (N=97 in total). AF was calculated for excitation wavelengths in the range 355 - 405 nm. Mean spectra were assessed per group. AF values in both type 1 and type 2 DM patients with complications were increased compared to the control subjects (p < 0:01); this ratio remained practically constant, independent of the excitation wavelength. No emission peaks were distinctive for specific patient groups. We conclude that in these groups, no characteristic fluorophores dictate the use of a specific wavelength or set of wavelengths. The results show the validity of applying a broad excitation wavelength range for risk assessment of chronic complications in diabetes.