Yoav Y. Broza

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Conventional diagnostic methods for lung cancer are unsuitable for widespread screening because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1-20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solid-phase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer.

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 patients 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.

Volatile Organic Compounds of Lung Cancer and Possible Biochemical Pathways

Biochemical Pathways Meggie Hakim,† Yoav Y. Broza,† Orna Barash,† Nir Peled,‡ Michael Phillips, Anton Amann, and Hossam Haick*,† †The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, TechnionIsrael Institute of Technology, Haifa 32000, Israel ‡The Thoracic Cancer Research and Detection Center, Sheba Medical Center, Tel-Aviv University, Tel-Aviv 52621, Israel Menssana Research, Inc., Fort Lee, New Jersey 07024, United States Breath Research Institute, Austrian Academy of Sciences, 6850 Dornbirn, Austria University-Clinic for Anesthesia, Innsbruck Medical University, 6020 Innsbruck, Austria

Nanomaterial-based sensors for detection of disease by volatile organic compounds

The importance of developing new diagnostic and detection technologies for the growing number of clinical challenges is rising each year. Here, we present a concise, yet didactic review on a new diagnostics frontier based on the detection of disease-related volatile organic compounds (VOCs) by means of nanomaterial-based sensors. Nanomaterials are ideal for such sensor arrays because they are easily fabricated, chemically versatile and can be integrated into currently available sensing platforms. Following a general introduction, we provide a brief description of the VOC-related diseases concept. Then, we focus on detection of VOC-related diseases by selective and crossreactive sensing approaches, through chemical, optical and mechanical transducers incorporating the most important classes of nanomaterials. Selected examples of the integration of nanomaterials into selective sensors and crossreactive sensor arrays are given. We conclude with a brief discussion on the integration possibilities of different types of nanomaterials into sensor arrays, and the expected outcomes and limitations.

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 Multiple Sclerosis from Exhaled Breath Using Bilayers of Polycyclic Aromatic Hydrocarbons and Single-Wall Carbon Nanotubes

A cross-reactive array of polycyclic aromatic hydrocarbons and single wall carbon nanotube bilayers was designed for the detection of volatile organic compounds (tentatively, hexanal and 5-methyl-undecane) that identify the presence of disease in the exhaled breath of patients with multiple sclerosis. The sensors showed excellent discrimination between hexanal, 5-methyl-undecane, and other confounding volatile organic compounds. Results obtained from a clinical study consisting of 51 volunteers showed that the sensors could discriminate between multiple sclerosis and healthy states from exhaled breath samples with 85.3% sensitivity, 70.6% specificity, and 80.4% accuracy. These results open new frontiers in the development of a fast, noninvasive, and inexpensive medical diagnostic tool for the detection and identification of multiple sclerosis. The results could serve also as a launching pad for the discrimination between different subphases or stages of multiple sclerosis as well as for the identification of multiple sclerosis patients who would respond well to immunotherapy.

Vibrio cholerae Strain Typing and Phylogeny Study Based on Simple Sequence Repeats

ABSTRACT Vibrio cholerae is the etiological agent of cholera. Its natural reservoir is the aquatic environment. To date, practical typing of V. cholerae is mainly serological and requires about 200 antisera. Simple sequence repeats (SSR), also termed VNTR (for variable number of tandem repeats), provide a source of high genomic polymorphism used in bacterial typing. Here we describe an SSR-based typing method that combines the variation in highly mutable SSR loci, with that of shorter, relatively more stable mononucleotide repeat (MNR) loci, for accurate and rapid typing of V. cholerae. In silico screening of the V. cholerae genome revealed thousands of perfect SSR tracts with an average frequency of one SSR every 152 bp. A panel of 32 V. cholerae strains, representing both clinical and environmental isolates, was tested for polymorphism in SSR loci. Two strategies were applied to identify SSR variation: polymorphism of SSR tracts longer than 12 bp (L-SSR) assessed by capillary fragment-size analysis and MNR polymorphism assessed by sequencing. The nine L-SSR loci tested were all polymorphic, displaying 2 to 13 alleles per locus. Sequence analysis of eight MNR-containing loci (MNR-multilocus sequence typing [MLST]) provided information on both variations in the MNR tract itself, and single nucleotide polymorphism (SNP) in their flanking sequences. Phylogenetic analysis of the combined SSR data showed a clear discrimination between the clinical strains belonging to O1 and O139 serogroups, and the environmental isolates. Furthermore, discrimination between 27 strains of the 32 strains was achieved. SSR-based typing methods combining L-SSR and MNR-MLST were found to be efficient for V. cholerae typing.

A nanomaterial-based breath test for short-term follow-up after lung tumor resection

UNLABELLED In this case study, we demonstrate the feasibility of nanomaterial-based sensors for identifying the breath-print of early-stage lung cancer (LC) and for short-term follow-up after LC-resection. Breath samples were collected from a small patient cohort prior to and after lung resection. Gas-chromatography/mass-spectrometry showed that five volatile organic compounds were significantly reduced after LC surgery. A nanomaterial-based sensor-array distinguished between pre-surgery and post-surgery LC states, as well as between pre-surgery LC and benign states. In contrast, the same sensor-array could neither distinguish between pre-surgery and post-surgery benign states, nor between LC and benign states after surgery. This indicates that the observed pattern is associated with the presence of malignant lung tumors. The proof-of-concept presented here has initiated a large-scale clinical study for post-surgery follow-up of LC patients. FROM THE CLINICAL EDITOR Monitoring for tumor recurrence remains very challenging due to post-surgical and radiation therapy induced changes in target organs, which often renders standard radiological identification of recurrent malignancies inaccurate. In this paper a novel nanotechnology-based sensor array is used for identification of volatile organic compounds in exhaled air that enable identification of benign vs. malignant states.

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Volatile fingerprints of cancer specific genetic mutations

UNLABELLED We report on a new concept for profiling genetic mutations of (lung) cancer cells, based on the detection of patterns of volatile organic compounds (VOCs) emitted from cell membranes, using an array of nanomaterial-based sensors. In this in-vitro pilot study we have derived a volatile fingerprint assay for representative genetic mutations in cancer cells that are known to be associated with targeted cancer therapy. Five VOCs were associated with the studied oncogenes, using complementary chemical analysis, and were discussed in terms of possible metabolic pathways. The reported approach could lead to the development of novel methods for guiding treatments, so that patients could benefit from safer, more timely and effective interventions that improve survival and quality of life while avoiding unnecessary invasive procedures. Studying clinical samples (tissue/blood/breath) will be required as next step in order to determine whether this cell-line study can be translated into a clinically useful tool. FROM THE CLINICAL EDITOR In this novel study, a new concept for profiling genetic mutations of (lung) cancer cells is described, based on the detection of patterns of volatile organic compounds emitted from cell membranes, using an array of nano-gold based sensors.