Unique volatolomic signatures of TP53 and KRAS in lung cells

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells

Characteristic volatile organic compounds (VOCs) are anticipated to be used for the identification of lung cancer cells. However, to date, consistent biomarkers of VOCs in lung cancer cells have not been obtained through direct comparison between cancer and healthy groups. In this study, we regulated the glycolysis, a common metabolic process in cancer cells, and employed solid phase microextraction gas chromatography mass spectrometry (SPME-GC-MS) combined with untargeted analysis to identify the characteristic VOCs shared by cancer cells. The VOCs released by three types of lung cancer cells (A549, PC-9, NCI-H460) and one normal lung epithelial cell (BEAS-2B) were detected using SPME-GC-MS, both in their resting state and after treatment with glycolysis inhibitors (2-Deoxy-D-glucose, 2-DG/3-Bromopyruvic acid, 3-BrPA). Untargeted analysis methods were employed to compare the VOC profiles between each type of cancer cell and normal cells before and after glycolysis regulation. Our findings revealed that compared to normal cells, the three types of lung cancer cells exhibited three common differential VOCs in their resting state: ethyl propionate, acetoin, and 3-decen-5-one. Furthermore, under glycolysis control, a single common differential VOC-acetoin was identified. Notably, acetoin levels increased by 2.60-3.29-fold in all three lung cancer cell lines upon the application of glycolysis inhibitors while remaining relatively stable in normal cells. To further elucidate the formation mechanism of acetoin, we investigated its production by blocking glutaminolysis. This interdisciplinary approach combining metabolic biochemistry with MS analysis through interventional synthetic VOCs holds great potential for revolutionizing the identification of lung cancer cells and paving the way for novel cytological examination techniques.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Nematode-Applied Technology for Human Tumor Microenvironment Research and Development

Nematodes, such as Caenorhabditis elegans, have been instrumental to the study of cancer. Recently, their significance as powerful cancer biodiagnostic tools has emerged, but also for mechanism analysis and drug discovery. It is expected that nematode-applied technology will facilitate research and development on the human tumor microenvironment. In the history of cancer research, which has been spurred by numerous discoveries since the last century, nematodes have been important model organisms for the discovery of cancer microenvironment. First, microRNAs (miRNAs), which are noncoding small RNAs that exert various functions to control cell differentiation, were first discovered in C. elegans and have been actively incorporated into cancer research, especially in the study of cancer genome defects. Second, the excellent sense of smell of nematodes has been applied to the diagnosis of diseases, especially refractory tumors, such as human pancreatic cancer, by sensing complex volatile compounds derived from heterogeneous cancer microenvironment, which are difficult to analyze using ordinary analytical methods. Third, a nematode model system can help evaluate invadosomes, the phenomenon of cell invasion by direct observation, which has provided a new direction for cancer research by contributing to the elucidation of complex cell–cell communications. In this cutting-edge review, we highlight milestones in cancer research history and, from a unique viewpoint, focus on recent information on the contributions of nematodes in cancer research towards precision medicine in humans.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

Volatile Organic Compounds in Human Breath

A comprehensive analysis of volatile organic compounds (VOCs) from the exhaled breath sample is termed as breathomics. Breath samples are a complex mixture composed of a multitude of VOCs and other molecules. The analysis of total VOCs in exhaled breath provides a promising tool for the diagnosis of many diseases because it enables the observation of biochemical processes in the body in a non-invasive way. VOCs are produced in various physiological and pathophysiological conditions thus making it a potential biomarker for several diseases.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Headspace analysis of mesothelioma cell lines differentiates biphasic and epithelioid sub-types

Malignant mesothelioma (MM) is an incurable cancer. MM is often misdiagnosed, with a poor 5-year survival and limited treatment options. The discovery of endogenous volatile organic compounds (VOCs) is required in order to accelerate the development of a breath test as an alternative to conventional MM diagnosis. For the first time, this study used solid-phase microextraction and gas chromatography-mass spectrometry to identify VOCs released directly from the biphasic MM cell line MSTO-211H and the epithelioid MM cell line NCI-H28 as well as the non-malignant mesothelial cell line MET-5A. Multivariate statistical analysis showed separation between MSTO-211H, NCI-H28 and MET-5A results. 2-ethyl-1-hexanol was significantly increased in both MSTO-211H and NCI-H28 cells compared to MET-5A controls. In addition, ethyl propionate and cyclohexanol were significantly increased in MSTO-211H cells and dodecane was significantly increased in NCI-H28 cells. This is the first study reporting headspace analysis of these MM cell lines and the first to consider the effects of mesothelioma sub-type on VOC profile. Current results further highlight the potential for a diagnostic mesothelioma breath test as well as providing proof of concept for the differentiation between biphasic and epithelioid mesothelioma based on VOC profiles.

Exhaled volatile organic compounds in adult asthma: a systematic review

The search for biomarkers that can guide precision medicine in asthma, particularly those that can be translated to the clinic, has seen recent interest in exhaled volatile organic compounds (VOCs). Given the number of studies reporting "breathomics" findings and its growing integration in clinical trials, we performed a systematic review of the literature to summarise current evidence and understanding of breathomics technology in asthma.A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed (CRD42017084145) of MEDLINE, Embase and the Cochrane databases to search for any reports that assessed exhaled VOCs in adult asthma patients, using the following terms (asthma AND (volatile organic compounds AND exhaled) OR breathomics).Two authors independently determined the eligibility of 2957 unique records, of which 66 underwent full-text review. Data extraction and risk of bias assessment was performed on the 22 studies deemed to fulfil the search criteria. The studies are described in terms of methodology and the evidence narratively summarised under the following clinical headings: diagnostics, phenotyping, treatment stratification, treatment monitoring and exacerbation prediction/assessment.Our review found that most studies were designed to assess diagnostic potential rather than focus on underlying biology or treatable traits. Results are generally limited by a lack of methodological standardisation and external validation and by insufficiently powered studies, but there is consistency across the literature that exhaled VOCs are sensitive to underlying inflammation. Modern studies are applying robust breath analysis workflows to large multi-centre study designs, which should unlock the full potential of measurement of exhaled volatile organic compounds in airways diseases such as asthma.

Metabolomics and Biomarker Discovery

Recently, metabolomics-the study of metabolite profiles within biological samples-has found a wide range of applications. This chapter describes the different techniques available for metabolomic analysis, the various samples that can be utilised for analysis and applications of both global and targeted metabolomic analysis to biomarker discovery in medicine.

Profiling Single Cancer Cells with Volatolomics Approach

Single-cell analysis is a rapidly evolving to characterize molecular information at the individual cell level. Here, we present a new approach with the potential to overcome several key challenges facing the currently available techniques. The approach is based on the identification of volatile organic compounds (VOCs), viz. organic compounds having relatively high vapor pressure, emitted to the cell's headspace. This concept is demonstrated using lung cancer cells with various p53 genetic status and normal lung cells. The VOCs were analyzed by gas chromatography combined with mass spectrometry. Among hundreds of detected compounds, 18 VOCs showed significant changes in their concentration levels in tumor cells versus control. The composition of these VOCs was found to depend, also, on the sub-molecular structure of the p53 genetic status. Analyzing the VOCs offers a complementary way of querying the molecular mechanisms of cancer as well as of developing new generation(s) of biomedical approaches for personalized screening and diagnosis.

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Measurement of VOCs was achieved at the single-cell level

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Genetic changes influence the emitted volatiles of single and bulk cancer cells

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Single-cell VOC analysis measures population heterogeneity in initial stage of tumors

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Volatolomics research can promote non-invasive, simple, and cost-effective diagnostics

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Versatile set-up for non-invasive in vitro analysis of headspace VOCs

Volatile organic compound (VOC) profiles emitted in trace concentrations from bacteria or cells has gained increasing importance over the decades. Analysis of VOCs in the headspace does not interfere with in vitro systems and, therefore, offers new options for non-invasive monitoring of cultures. Currently there is not any available standardized in vitro sampling system which considers effects of dilution and contamination onto ppbV to pptV VOC concentrations during. In this study a new in vitro system for online and offline headspace measurement of biological cultures was designed. The system was built from inert materials, equipped with universal sampling ports and easily adjustable volume options. Standard VOC mixtures in the system were analyzed by means of proton-transfer-reaction time-of-flight mass spectrometry and needle-trap-microextraction coupled with gas chromatography/mass spectrometry with a variance of 5%-14% and 10%-15%, respectively. In a proof of concept setup volatile emissions over cell cultures and pure media were assessed. The newly developed system enabled reliable and reproducible headspace analyses of in vitro cultures. As parallel application of different analytical methods is possible and confounding factors could be minimized, this set-up represents an important step towards standardization of headspace analysis over biological cultures.

Detection of Lung Cancer: Concomitant Volatile Organic Compounds and Metabolomic Profiling of Six Cancer Cell Lines of Different Histological Origins

In recent years, there has been an extensive search for a non-invasive screening technique for early detection of lung cancer. Volatile organic compound (VOC) analysis in exhaled breath is one such promising technique. This approach is based on the fact that tumor growth is accompanied by unique oncogenesis, leading to detectable changes in VOC emitting profile. Here, we conducted a comprehensive profiling of VOCs and metabolites from six different lung cancer cell lines and one normal lung cell line using mass spectrometry. The concomitant VOCs and metabolite profiling allowed significant discrimination between lung cancer and normal cell, nonsmall cell lung cancer (NSCLC) and small cell lung cancer (SCLC), as well as between different subtypes of NSCLC. It was found that a combination of benzaldehyde, 2-ethylhexanol, and 2,4-decadien-1-ol could serve as potential volatile biomarkers for lung cancer. A detailed correlation between nonvolatile metabolites and VOCs can demonstrate possible biochemical pathways for VOC production by the cancer cells, thus enabling further optimization of VOCs as biomarkers. These findings could eventually lead to noninvasive early detection of lung cancer and differential diagnosis of lung cancer subtypes, thus revolutionizing lung cancer treatment.

Discrimination between the human prostate normal and cancer cell exometabolome by GC-MS

Serum prostate-specific antigen (PSA) is currently the most used biomarker in clinical practice for prostate cancer (PCa) detection. However, this biomarker has several drawbacks. In this work, an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling of PCa cells was performed to prove the concept that metabolic alterations might differentiate PCa cell lines from normal prostate cell line. For that, we assessed the differences in volatile organic compounds (VOCs) profile in the extracellular medium (exometabolome) of four PCa cell lines and one normal prostate cell line at two pH values (pH 2 and 7) by GC-MS. Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal prostate cells. The most altered metabolites included ketones, aldehydes and organic acids. Among these, we highlight pentadecane-2-one and decanoic acid, which were significantly increased in PCa compared to normal cells, and cyclohexanone, 4-methylheptan-2-one, 2-methylpentane-1,3-diol, 4-methylbenzaldehyde, 1-(3,5-dimethylfuran-2-yl)ethanone, methyl benzoate and nonanoic acid, which were significantly decreased in PCa cells. The PCa volatilome was markedly influenced by the VOCs extraction pH, though the discriminant capability was similar. Overall, our data suggest that VOCs monitoring has the potential to be used as a PCa screening methodology.

Breathprinting and Early Diagnosis of Lung Cancer

The electronic nose (e-nose) is a promising technology as a useful addition to the currently available modalities to achieve lung cancer diagnosis. The e-nose can assess the volatile organic compounds detected in the breath and derived from the cellular metabolism. Volatile organic compounds can be analyzed to identify the individual chemical elements as well as their pattern of expression to reproduce a sensorial combination similar to a fingerprint (breathprint). The e-nose can be used alone, mimicking the combinatorial selectivity of the human olfactory system, or as part of a multisensorial platform. This review analyzes the progress made by investigators interested in this technology as well as the perspectives for its future utilization.

Volatile fingerprinting of human respiratory viruses from cell culture

Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.

Smell of cells: Volatile profiling of stem- and non-stem cell proliferation

Bacterial and cell cultures are known to emit a large number of volatile organic compounds (VOCs). Conventional biochemical methods are often destructive, time-consuming and expensive. In contrast, VOC analysis of headspace over cultures may offer a non-destructive alternative for the monitoring of cell proliferation and metabolism. VOC profiles from cultures of murine pluripotent stem cells and fibroblasts were assessed every 24 h for 3 days. Pure cell media were measured as parallel controls. VOC analysis was highly standardized with respect to time of measurement and phases of cell growth. Cultures were grown in custom-made inert boxes. In order to determine the effects of fresh media supply on VOC emissions, both cell types were cultured with and without daily media exchange. VOCs from headspace were preconcentrated by means of needle trap micro-extraction and analysed by gas chromatography-mass spectrometry (GC-MS). Murine pluripotent stem cells emitted increasing concentrations of thiirane and methyl-methoxy-hydroxy-methyl-amine (MMHA). Substance concentration correlated with cell numbers. Murine fibroblasts did not emit thiirane or MMHA. Concentrations of aldehydes, especially benzaldehyde, were lower in both cell cultures than in pure media samples. Daily media exchange resulted in higher cell numbers, but had no major effects on VOC concentrations emitted from the cells. Investigation and monitoring of volatile substances such as thiirane and MMHA may enhance the understanding of stem cell properties and lead to a destruction-free characterization of pluripotent stem cells.

The 150 most important questions in cancer research and clinical oncology series: questions 67-75 : Edited by Chinese Journal of Cancer

Since the beginning of 2017, Chinese Journal of Cancer has published a series of important questions in cancer research and clinical oncology, which sparkle diverse thoughts, interesting communications, and potential collaborations among researchers all over the world. In this article, 9 more questions are presented as followed. Question 67. How could we overcome the resistance of hepatocellular carcinoma against chemotherapeutics? Question 68. Is pursuit of non-covalent small-molecule binders of RAS proteins viable as a strategy of cancer drug discovery? Question 69. In what oligomeric structures do RAS proteins signal? Question 70. How can we achieve non-invasive early detection and diagnosis of lung cancer? Question 71. Does genetic information influence the volatolome enabling diagnosis of lung cancer with genetic mutations via cell headspace or breath analysis? Question 72. Is heavy ion beam radiotherapy effective to kill cancer stem cells? Question 73. Is there any diversity among different types of cancer in terms of sensitivity to heavy ion beam radiotherapy? Question 74. Can targeted alpha-particle therapy augment the effect of carbon ion radiotherapy on malignancies? Question 75. How does chromosomal instability drive tumor progression?

Detection of Lung Cancer and EGFR Mutation by Electronic Nose System

INTRODUCTION

Early detection of lung cancer (LC) has been well established as a significant key point in patient survival and prognosis. New highly sensitive nanoarray sensors for exhaled volatile organic compounds that have been developed and coupled with powerful statistical programs may be used when diseases such as LC are suspected. Detection of genetic aberration mutation by nanoarray sensors is the next target.

METHODS

Breath samples were taken from patients who were evaluated for suspicious pulmonary lesions. Patients were classified as those with benign nodules, as patients with LC with or without the EGFR mutation, and according to their smoking status. Breath prints were recognized by nanomaterial-based sensor array, and pattern recognition methods were used.

RESULTS

A total of 119 patients participated in this study, 30 patients with benign nodules and 89 patients with LC (16 with early disease and 73 with advanced disease). Patients with LC who harbored the EGFR mutation (n = 19) could be discriminated from those with wild-type EGFR (n = 34) with an accuracy of 83%, sensitivity of 79%, and specificity of 85%. Discrimination of early LC from benign nodules had 87% accuracy and positive and negative predictive values of 87.7 and 87.5% respectively. Moderate discrimination (accuracy of 76%) was found between LC of heavy smokers and that of never-smokers or distant past light smokers.

CONCLUSIONS

Breath analysis could discriminate patients with LC who harbor the EGFR mutation from those with wild-type EGFR and those with benign pulmonary nodules from those patients with early LC. A positive breath print for the EGFR mutation may be used in treatment decisions if tissue sampling does not provide adequate material for definitive mutation analysis.

Advanced Materials for Health Monitoring with Skin-Based Wearable Devices

Skin-based wearable devices have a great potential that could result in a revolutionary approach to health monitoring and diagnosing disease. With continued innovation and intensive attention to the materials and fabrication technologies, development of these healthcare devices is progressively encouraged. This article gives a concise, although admittedly non-exhaustive, didactic review of some of the main concepts and approaches related to recent advances and developments in the scope of skin-based wearable devices (e.g. temperature, strain, biomarker-analysis werable devices, etc.), with an emphasis on emerging materials and fabrication techniques in the relevant fields. To give a comprehensive statement, part of the review presents and discusses different aspects of these advanced materials, such as the sensitivity, biocompatibility and durability as well as the major approaches proposed for enhancing their chemical and physical properties. A complementary section of the review linking these advanced materials with wearable device technologies is particularly specified. Some of the strong and weak points in development of each wearable material/device are highlighted and criticized. Several ideas regarding further improvement of skin-based wearable devices are also discussed.

Volatile metabolomic signature of human breast cancer cell lines

Breast cancer (BC) remains the most prevalent oncologic pathology in women, causing huge psychological, economic and social impacts on our society. Currently, the available diagnostic tools have limited sensitivity and specificity. Metabolome analysis has emerged as a powerful tool for obtaining information about the biological processes that occur in organisms, and is a useful platform for discovering new biomarkers or make disease diagnosis using different biofluids. Volatile organic compounds (VOCs) from the headspace of cultured BC cells and normal human mammary epithelial cells, were collected by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography combined with mass spectrometry (GC-MS), thus defining a volatile metabolomic signature. 2-Pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate were detected only in cultured BC cell lines. Multivariate statistical methods were used to verify the volatomic differences between BC cell lines and normal cells in order to find a set of specific VOCs that could be associated with BC, providing comprehensive insight into VOCs as potential cancer biomarkers. The establishment of the volatile fingerprint of BC cell lines presents a powerful approach to find endogenous VOCs that could be used to improve the BC diagnostic tools and explore the associated metabolomic pathways.

Origin, Analytical Characterization, and Use of Human Odor in Forensics

Developing a strategy to characterize the odor prints of individuals should be relevant to support identification obtained using dogs in courts of justice. This article proposes an overview of the techniques used for the forensic profiling of human odor. After reviewing the origin of human odor-both genetic and physiological-the different analytical steps from sample collection to statistical data processing are presented. The first challenge is the collection of odor, whether by direct sampling with polymer patches, cotton gauze, etc., or indirect sampling with devices like Scent Transfer Unit. Then, analytical techniques are presented. Analyses are commonly performed with gas chromatography coupled with mass spectrometry. As they yield large amounts of data, advanced statistical tools are needed to provide efficient and reliable data processing, which is essential to give more probative value to information.

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.

Cancer metabolism: the volatile signature of glycolysis-in vitro model in lung cancer cells

Discovering the volatile signature of cancer cells is an emerging approach in cancer research, as it may contribute to a fast and simple diagnosis of tumors in vivo and in vitro. One of the main contributors to such a volatile signature is hyperglycolysis, which characterizes the cancerous cell. The metabolic perturbation in cancer cells is known as the Warburg effect; glycolysis is preferred over oxidative phosphorylation (OXPHOS), even in the presence of oxygen. The precise mitochondrial alterations that underlie the increased dependence of cancer cells on aerobic glycolysis for energy generation have remained a mystery. We aimed to profile the volatile signature of the glycolysis activity in lung cancer cells. For that an in vitro model, using lung cancer cell line cultures (A549, H2030, H358, H322), was developed. The volatile signature was measured by proton transfer reaction mass spectrometry under normal conditions and glycolysis inhibition. Glycolysis inhibition and mitochondrial activity were also assessed by mitochondrial respiration capacity measurements. Cells were divided into two groups upon their glycolytic profile (PET positive and PET negative). Glycolysis blockade had a unique characteristic that was shared by all cells. Furthermore, each group had a characteristic volatile signature that enabled us to discriminate between those sub-groups of cells. In conclusion, lung cancer cells may have different subpopulations of cells upon low and high mitochondrial capacity. In both groups, glycolysis blockade induced a unique volatile signature.

Differentiation between genetic mutations of breast cancer by breath volatolomics

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

The Pursuit of Noninvasive Diagnosis of Lung Cancer

The noninvasive diagnosis of lung cancer remains a formidable challenge. Although tissue diagnosis will remain the gold standard for the foreseeable future, questions pertaining to the risks and costs associated with invasive diagnostic procedures are of prime relevance. This review addresses new modalities for improving the noninvasive evaluation of suspicious lung nodules. Ultimately, the goal is to translate early diagnosis into early treatment. We discuss how biomarkers could assist in distinguishing benign from malignant nodules and aggressive from indolent tumors. The field of biomarkers is rapidly expanding and progressing, and efforts are well underway to apply molecular diagnostics to address the shortcomings of current lung cancer diagnostic tools.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

Exhaled Molecular Fingerprinting in Diagnosis and Monitoring: Validating Volatile Promises

Medical diagnosis and phenotyping increasingly incorporate information from complex biological samples. This has promoted the development and clinical application of non-invasive metabolomics in exhaled air (breathomics). In respiratory medicine, expired volatile organic compounds (VOCs) are associated with inflammatory, oxidative, microbial, and neoplastic processes. After recent proof of concept studies demonstrating moderate to good diagnostic accuracies, the latest efforts in breathomics are focused on optimization of sensor technologies and analytical algorithms, as well as on independent validation of clinical classification and prediction. Current research strategies are revealing the underlying pathophysiological pathways as well as clinically-acceptable levels of diagnostic accuracy. Implementing recent guidelines on validating molecular signatures in medicine will enhance the clinical potential of breathomics and the development of point-of-care technologies.

Cancerous glucose metabolism in lung cancer-evidence from exhaled breath analysis

Cancer cells prefer hyperglycolysis versus oxidative phosphorylation, even in the presence of oxygen. This phenomenon is used through the FDG-PET scans, and may affect the exhaled volatile signature. This study investigates the volatile signature in lung cancer (LC) before and after an oral glucose tolerance test (OGTT) to determine if tumor cells' hyperglycolysis would affect the volatile signature. Blood glucose levels and exhaled breath samples were analyzed before the OGTT, and 90 min after, in both LC patients and controls. The volatile signature was measured by proton transfer reaction mass spectrometry (PTR-MS). Twenty-two LC patients (age 66.6  ±  12.7) with adenocarcinoma (n  =  14), squamous (n  =  6), small cell carcinoma (n  =  2), and twenty-one controls (age 54.4  ±  13.7; 10 non-smokers and 11 smokers) were included. All LC patients showed a hyperglycolytic state in their FDG-PET scans. Both baseline and post OGTT volatile signatures discriminate between the groups. The OGTT has a minimal effect in LC (a decrease in m/z 54 by 39%, p v  =  0.0499); whereas in the control group, five masses (m/z 64, 87,88, 142 and 161) changed by  -13%, -49%, -40% and  -29% and 46% respectively. To conclude, OGTT has a minimal effect on the VOC signature in LC patients, where a hyperglycolytic state already exists. In contrast, in the control group the OGTT has a profound effect in which induced hyperglycolysis significantly changed the VOC pattern. We hypothesized that a ceiling effect in cancerous patients is responsible for this discrepancy.

Nanoscale Sensor Technologies for Disease Detection via Volatolomics

The detection of many diseases is missed because of delayed diagnoses or the low efficacy of some treatments. This emphasizes the urgent need for inexpensive and minimally invasive technologies that would allow efficient early detection, stratifying the population for personalized therapy, and improving the efficacy of rapid bed-side assessment of treatment. An emerging approach that has a high potential to fulfill these needs is based on so-called "volatolomics", namely, chemical processes involving profiles of highly volatile organic compounds (VOCs) emitted from body fluids, including breath, skin, urine and blood. This article presents a didactic review of some of the main advances related to the use of nanomaterial-based solid-state and flexible sensors, and related artificially intelligent sensing arrays for the detection and monitoring of disease with volatolomics. The article attempts to review the technological gaps and confounding factors related to VOC testing. Different ways to choose nanomaterial-based sensors are discussed, while considering the profiles of targeted volatile markers and possible limitations of applying the sensing approach. Perspectives for taking volatolomics to a new level in the field of diagnostics are highlighted.

Investigation of VOCs associated with different characteristics of breast cancer cells

The efficacy of breath volatile organic compounds (VOCs) analysis for the screening of patients bearing breast cancer lesions has been demonstrated by using gas chromatography and artificial olfactory systems. On the other hand, in-vitro studies suggest that VOCs detection could also give important indications regarding molecular and tumorigenic characteristics of tumor cells. Aim of this study was to analyze VOCs in the headspace of breast cancer cell lines in order to ascertain the potentiality of VOCs signatures in giving information about these cells and set-up a new sensor system able to detect breast tumor-associated VOCs. We identified by Gas Chromatography-Mass Spectrometry analysis a VOCs signature that discriminates breast cancer cells for: i) transformed condition; ii) cell doubling time (CDT); iii) Estrogen and Progesterone Receptors (ER, PgR) expression, and HER2 overexpression. Moreover, the signals obtained from a temperature modulated metal oxide semiconductor gas sensor can be classified in order to recognize VOCs signatures associated with breast cancer cells, CDT and ER expression. Our results demonstrate that VOCs analysis could give clinically relevant information about proliferative and molecular features of breast cancer cells and pose the basis for the optimization of a low-cost diagnostic device to be used for tumors characterization.

Breath testing as potential colorectal cancer screening tool

Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC-MS) for identification and quantification of volatile organic compounds (VOCs). The T-test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis with a pattern recognition method for building a breath pattern to identify different groups. Blind analysis or leave-one-out cross validation was conducted for validation. The GC-MS analysis revealed four significant VOCs that identified the tested groups; these were acetone and ethyl acetate (higher in CRC), ethanol and 4-methyl octane (lower in CRC). The sensor-analysis distinguished CRC from the control group with 85% sensitivity, 94% specificity and 91% accuracy. The performance of the sensors in identifying the advanced adenoma group from the non-advanced adenomas was 88% sensitivity, 100% specificity, and 94% accuracy. The performance of the sensors in identifying the advanced adenoma group was distinguished from the control group was 100% sensitivity, 88% specificity, and 94% accuracy. For summary, volatile marker testing by using sensor analysis is a promising noninvasive approach for CRC screening.

Hybrid volatolomics and disease detection

This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to "volatolomics"-an emerging frontier for fast, risk-free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so-called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by "hybrid" approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.