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.

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.

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.