Screening for gastric cancer using exhaled breath samples.

BACKGROUND: The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population. METHODS: A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population. RESULTS: The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier. CONCLUSION: This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.

Detection of halitosis in breath: Between the past, present, and future.

To develop a new generation of diagnostics for halitosis, replacing the subjective organoleptic assessment, a series of exhaled breath analyzers has been developed and assessed. All three devices rely on the assessment of exhaled volatile sulfuric compounds (VSCs), which are mainly generated in and emitted from the oral cavity, contributing to the malodor. Portable, on-site and easy to use, these devices have potential for non-invasive diagnosis of halitosis. However, global assessment of exhaled VSCs alone has two main drawbacks: (i) the absence of VSCs does not rule out halitosis; (ii) non-sulfuric volatile compounds that could be biomarkers of systemic diseases, found in up to 15% of halitosis cases, are neglected. In this article, we review and discuss progress to date in the field of oral/exhaled volatile compounds as potential non-invasive diagnostics for halitosis. We will briefly describe the generation of these compounds both from local (oral) and distal (extra-oral) sources. In addition, we debate the different analytical approaches in use and discuss the potential value of bio-inspired artificially intelligent olfaction in diagnosing and classifying oral and systemic diseases by analyzing exhaled breath.

Distinguishing idiopathic Parkinson's disease from other parkinsonian syndromes by breath test.

INTRODUCTION: Diagnosis of different parkinsonian syndromes is linked with high misdiagnosis rates and various confounding factors. This is particularly problematic in its early stages. With this in mind, the current pilot study aimed to distinguish between Idiopathic Parkinson's Disease (iPD), other Parkinsonian syndromes (non-iPD) and healthy subjects, by a breath test that analyzes the exhaled volatile organic compounds using a highly sensitive nanoarray. METHODS: Breath samples of 44 iPD, 16 non-iPD patients and 37 healthy controls were collected. The samples were passed over a nanoarray and the resulting electrical signals were analyzed with discriminant factor analysis as well as by a K-fold cross-validation method, to test the accuracy of the model. RESULTS: Comparison of non-iPD with iPD states yielded 88% sensitivity, 88% accuracy, and 88% Receiver Operating Characteristic area under the curve in the training set samples with known identity. The validation set of this comparison scored 81% sensitivity and accuracy and 92% negative predictive value. Comparison between atypical parkinsonism states and healthy subjects scored 94% sensitivity and 85% accuracy in the training set samples with known identity. The validation set of this comparison scored 81% sensitivity and 78% accuracy. The obtained results were not affected by l-Dopa or MAO-B inhibitor treatment. CONCLUSIONS: Exhaled breath analysis with nanoarray is a promising approach for a non-invasive, inexpensive, and portable technique for differentiation between different Parkinsonian states. A larger cohort is required in order to establish the clinical usefulness of the method.

Analysis of volatile organic compounds in rats with dopaminergic lesion: Possible application for early detection of Parkinson's disease.

Parkinson's disease (PD) is characterized by dopaminergic (DA) neuron depletion. Early detection of PD may help in selecting the appropriate treatment. Biomarkers of PD have been suggested, however none of these is currently in clinical use. The aim of this study was to identify volatile organic compounds (VOCs) as early biomarkers of PD. Our hypothesis was that during PD progression, specific VOCs are generated that are linked to the biochemical pathways characterizing PD. These VOCs can be detected by GC-MS combined with solid-phase microextraction (SPME) technique. Three groups of rats were studied: DA-lesioned rats injected with 6-hydroxydopamine (HDA; 250µg/rat n=11); control rats injected with saline (n=9), and control rats injected with DSP-4 (n=8), a specific noradrenergic neuron toxin. Blood and striatal tissue homogenate were analyzed. In the blood, 1-octen-3-ol and 2-ethylhexanol were found at significantly higher concentrations in HDA versus sham rats. In the striatal homogenate 1-octen-3-ol and other four compounds were found at significantly lower concentrations in HDA versus sham rats. 1-Octen-3-ol is a cytotoxic compound. These results may lead to the development of an early diagnostic test for PD based on profiling of VOCs in body fluids.

Unique volatolomic signatures of TP53 and KRAS in lung cells.

BACKGROUND: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRAS(V12) mutation, knockdown of TP53 or both with parental HBEC cells. METHODS: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC-MS) or sensor array with discriminant factor analysis (DFA). RESULTS: In TD-GC-MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80-100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. CONCLUSIONS: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC-MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers.

Analysis of exhaled breath for diagnosing head and neck squamous cell carcinoma: a feasibility study.

BACKGROUND: Squamous cell carcinoma of the head and neck (HNSCC) are wide-spread cancers that often lead to disfigurement and loss of important functions such as speech and ingestion. To date, HNSCC has no adequate method for early detection and screening. METHODS: Exhaled breath samples were collected from 87 volunteers; 62 well-defined breath samples from 22 HNSCC patients (larynx and pharynx), 21 patients with benign tumours (larynx and pharynx) and 19 healthy controls were analysed in a dual approach: (i) chemical analysis using gas chromatography/mass spectrometry (GC-MS) and (ii) breath-print analysis using an array of nanomaterial-based sensors, combined with a statistical algorithm. RESULTS: Gas chromatography/mass spectrometry identified ethanol, 2-propenenitrile and undecane as potential markers for HNSCC and/or benign tumours of the head and neck. The sensor-array-based breath-prints could clearly distinguish HNSCC both from benign tumours and from healthy states. Within the HNSCC group, patients could be classified according to tumour site and stage. CONCLUSIONS: We have demonstrated the feasibility of a breath test for a specific, clinically interesting application: distinguishing HNSCC from tumour-free or benign tumour states, as well as for staging and locating HNSCC. The sensor array used here could form the basis for the development of an urgently needed non-invasive, cost-effective, fast and reliable point-of-care diagnostic/screening tool for HNSCC.

A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions.

BACKGROUND: Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the standard method for diagnosing gastric cancer (GC). However, this procedure may not be widely available for screening in the developing world, whereas in developed countries endoscopy is frequently used without major clinical gain. There is a high demand for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination. Here, we studied the feasibility of a nanomaterial-based breath test for identifying GC among patients with gastric complaints. METHODS: Alveolar exhaled breath samples from 130 patients with gastric complaints (37 GC/32 ulcers / 61 less severe conditions) that underwent endoscopy/biopsy were analyzed using nanomaterial-based sensors. Predictive models were built employing discriminant factor analysis (DFA) pattern recognition, and their stability against possible confounding factors (alcohol/tobacco consumption; Helicobacter pylori) was tested. Classification success was determined (i) using leave-one-out cross-validation and (ii) by randomly blinding 25% of the samples as a validation set. Complementary chemical analysis of the breath samples was performed using gas chromatography coupled with mass spectrometry. RESULTS: Three DFA models were developed that achieved excellent discrimination between the subpopulations: (i) GC vs benign gastric conditions, among all the patients (89% sensitivity; 90% specificity); (ii) early stage GC (I and II) vs late stage (III and IV), among GC patients (89% sensitivity; 94% specificity); and (iii) ulcer vs less severe, among benign conditions (84% sensitivity; 87% specificity). The models were insensitive against the tested confounding factors. Chemical analysis found that five volatile organic compounds (2-propenenitrile, 2-butoxy-ethanol, furfural, 6-methyl-5-hepten-2-one and isoprene) were significantly elevated in patients with GC and/or peptic ulcer, as compared with less severe gastric conditions. The concentrations both in the room air and in the breath samples were in the single p.p.b.v range, except in the case of isoprene. CONCLUSION: The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases. It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses. Although this pilot study does not allow drawing far-reaching conclusions, the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions.

Diagnosis of head-and-neck cancer from exhaled breath.

BACKGROUND: Head-and-neck cancer (HNC) is the eighth most common malignancy worldwide. It is often diagnosed late due to a lack of screening methods and overall cure is achieved in <50% of patients. Head-and-neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract, mostly HNC or lung cancer (LC), making lifelong follow-up necessary. METHODS: Alveolar breath was collected from 87 volunteers (HNC and LC patients and healthy controls) in a cross-sectional clinical trial. The discriminative power of a tailor-made Nanoscale Artificial Nose (NA-NOSE) based on an array of five gold nanoparticle sensors was tested, using 62 breath samples. The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation. The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry (GC-MS), using 40 breath samples. RESULTS: The NA-NOSE could clearly distinguish between (i) HNC patients and healthy controls, (ii) LC patients and healthy controls, and (iii) HNC and LC patients. The GC-MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups. CONCLUSION: The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors.

BACKGROUND: Tumour growth is accompanied by gene and/or protein changes that may lead to peroxidation of the cell membrane species and, hence, to the emission of volatile organic compounds (VOCs). In this study, we investigated the ability of a nanosensor array to discriminate between breath VOCs that characterise healthy states and the most widespread cancer states in the developed world: lung, breast, colorectal, and prostate cancers. METHODS: Exhaled alveolar breath was collected from 177 volunteers aged 20-75 years (patients with lung, colon, breast, and prostate cancers and healthy controls). Breath from cancerous subjects was collected before any treatment. The healthy population was healthy according to subjective patient's data. The breath of volunteers was examined by a tailor-made array of cross-reactive nanosensors based on organically functionalised gold nanoparticles and gas chromatography linked to the mass spectrometry technique (GC-MS). RESULTS: The results showed that the nanosensor array could differentiate between 'healthy' and 'cancerous' breath, and, furthermore, between the breath of patients having different cancer types. Moreover, the nanosensor array could distinguish between the breath patterns of different cancers in the same statistical analysis, irrespective of age, gender, lifestyle, and other confounding factors. The GC-MS results showed that each cancer could have a unique pattern of VOCs, when compared with healthy states, but not when compared with other cancer types. CONCLUSIONS: The reported results could lead to the development of an inexpensive, easy-to-use, portable, non-invasive tool that overcomes many of the deficiencies associated with the currently available diagnostic methods for cancer.