Prediction of lung cancer using volatile biomarkers in breath1

Discovery of novel elongator protein 2 inhibitors by compound library screening using surface plasmon resonance

Tumour necrosis factor-α (TNF-α) is a pleiotropic cytokine that becomes elevated in chronic inflammatory states, including slowing down osteogenic differentiation, which leads to bone dysplasia in long-term inflammatory microenvironments. The elongator complex plays a role in gene regulation and association with various cellular activities, including the downstream signal transduction of TNF-α in osteogenic cells. To find an inhibitor of Elongator Protein 2 (Elp2), we performed a compound library screen and verified the pharmaceutical effects of candidate compounds on the mouse myoblast cell (C2C12) and mouse osteoblastic cells (MC3T3-E1). The commercial FDA-approved drug (FD) library and the bioactive compound (BC) library were used as candidate libraries. After a label-free, high-throughput affinity measurement with surface plasmon resonance (SPRi), seven kinds of compounds showed binding affinity with mouse Elp2 protein. The seven candidates were then used to perform an inhibition test with TNF-α-induced C2C12 and MC3T3-E1 cell lines. One candidate compound reduced the differentiation suppression caused by TNF-α with resuscitated alkaline phosphatase (ALP) activity, mineralization intensity and expression of osteogenic differentiation marker genes. The results of our study provide a competitive candidate to mitigate the TNF-α-induced osteogenic differentia.

Accuracy and Methodologic Challenges of Volatile Organic Compound-Based Exhaled Breath Tests for Cancer Diagnosis: A Systematic Review and Meta-analysis

Importance

The detection and quantification of volatile organic compounds (VOCs) within exhaled breath have evolved gradually for the diagnosis of cancer. The overall diagnostic accuracy of proposed tests remains unknown.

Objectives

To determine the diagnostic accuracy of VOC breath tests for the detection of cancer and to review sources of methodologic variability.

Data Sources

An electronic search (title and abstract) was performed using the Embase and MEDLINE databases (January 1, 2000, to May 28, 2017) through the OVID platform. The search terms cancer, neoplasm, malignancy, volatile organic compound, VOC, breath, and exhaled were used in combination with the Boolean operators AND and OR. A separate MEDLINE search that used the search terms breath AND methodology was also performed for studies that reported factors that influenced the concentration of VOCs within exhaled breath in humans.

Study Selection

The search was limited to human studies published in the English language. Trials that analyzed named endogenous VOCs within exhaled breath to diagnose or assess cancer were included in this review.

Data Extraction and Synthesis

Systematic review and pooled analysis were conducted in accordance with the recommendations of the Cochrane Library and Meta-analysis of Observational Studies in Epidemiology guidelines. Bivariate meta-analyses were performed to generate pooled point estimates of the hierarchal summary receiver operating characteristic curve of breath VOC analysis. Included studies were assessed according to the Standards for Reporting of Diagnostic Accuracy Studies checklist and Quality Assessment of Diagnostic Accuracy Studies 2 tool.

Main Outcomes and Measures

The principal outcome measure was pooled diagnostic accuracy of published VOC breath tests for cancer.

Results

The review identified 63 relevant publications and 3554 patients. All reports constituted phase 1 biomarker studies. Pooled analysis of findings found a mean (SE) area under the receiver operating characteristic analysis curve of 0.94 (0.01), sensitivity of 79% (95% CI, 77%-81%), and specificity of 89% (95% CI, 88%-90%). Factors that may influence variability in test results included breath collection method, patient physiologic condition, test environment, and method of analysis.

Conclusions and Relevance

The findings of our review suggest that standardization of breath collection methods and masked validation of breath test accuracy for cancer diagnosis is needed among the intended population in multicenter clinical trials. We propose a framework to guide the conduct of future breath tests in cancer studies.

Breath analysis in respiratory diseases: state-of-the-art and future perspectives

INTRODUCTION

The vast majority of respiratory diseases are associated with the production of volatile organic compounds (VOCs), the analysis of which might improve our knowledge about these disorders and their clinical management. The aim of this narrative review is to provide a comprehensive summary of current evidence supporting the application of breath analysis in the field of respiratory diseases, as well as suggesting potential applications available in the near future. Areas covered: A computerized literature search was performed to identify relevant articles reporting original data on the clinical use of breath analysis in respiratory diseases. Papers focusing on diseases other than respiratory, technical issues of VOC sampling and analysis, in vitro experiments or exogenous compounds were excluded. Expert commentary: Currently available evidence on the application of breath analysis in respiratory diseases is encouraging; however, it is mostly based on single-center studies without external validation. The standardization of the technique, together with multicenter clinical trials with external validation, will ensure it is ready for clinical use. Current and new applications in respiratory diseases may represent a major breakthrough in the field, so much so as to deserve further efforts in outlining the most effective way to apply VOC analysis for clinical purposes.

Detection of volatile organic compounds in exhaled breath to screen lung cancer: a systematic review

To evaluate the clinical value of volatile organic compounds (VOCs) in exhaled breath for lung cancer (LC) screening, a systematic review was performed. Systematic search for studies about exhaled VOCs for LC screening was conducted according to PRISMA. Thirty eight studies with 4873 participants met the criteria for inclusion in this systematic review. Generally speaking, the results suggest that exhaled VOCs have potential to screen LC and more studies are needed in the future.

A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS

Background

Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.

Method

The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.

Results

This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.

Conclusion

The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

A Prediction Model with a Combination of Variables for Diagnosis of Lung Cancer

Background

Multivariate models with a combination of variables can predict disease more accurately than a single variable employed alone. We developed a logistic regression model with a combination of variables and evaluated its ability to predict lung cancer.

Material/Methods

The exhaled breath from 57 patients with lung cancer and 72 healthy controls without cancer was collected. The VOCs of exhaled breath were examined qualitatively and quantitatively by a novel electronic nose (Z-nose4200 equipment). The VOCs in the 2 groups were compared using the Mann-Whitney U test, and the baseline data were compared between the 2 groups using the chi-square test or ANOVA. Variables from VOCs and baseline data were selected by stepwise logistic regression and subjected to a prediction model for the diagnosis of lung cancer as combined factors. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of this prediction model.

Results

Nine VOCs in exhaled breath of lung cancer patients differed significantly from those of healthy controls. Four variables – age, hexane, 2,2,4,6,6-pentamethylheptane, and 1,2,6-trimethylnaphthalene – were entered into the prediction model, which could effectively separate the lung cancer samples from the control samples with an accuracy of 82.8%, a sensitivity of 76.0%, and a specificity of 94.0%.

Conclusions

The profile of VOCs in exhaled breath contained distinguishable biomarkers in the patients with lung cancers. The prediction model with 4 variables appears to provide a new technique for lung cancer detection.

Association of Smoking with Metabolic Volatile Organic Compounds in Exhaled Breath

Lung cancer (LC) screening will be more efficient if it is applied to a well-defined high-risk population. Characteristics including metabolic byproducts may be taken into account to access LC risk more precisely. Breath examination provides a non-invasive method to monitor metabolic byproducts. However, the association between volatile organic compounds (VOCs) in exhaled breath and LC risk or LC risk factors is not studied. Exhaled breath samples from 122 healthy persons, who were given routine annual exam from December 2015 to December 2016, were analyzed using thermal desorption coupled with gas chromatography mass spectrometry (TD-GC-MS). Smoking characteristics, air quality, and other risk factors for lung cancer were collected. Univariate and multivariate analyses were used to evaluate the relationship between VOCs and LC risk factors. 7, 7, 11, and 27 VOCs were correlated with smoking status, smoking intensity, years of smoking, and depth of inhalation, respectively. Exhaled VOCs are related to smoking and might have a potential to evaluate LC risk more precisely. Both an assessment of temporal stability and testing in a prospective study are needed to establish the performance of VOCs such as 2,5-dimethylfuranm and 4-methyloctane as lung cancer risk biomarkers.

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection.

Volatile compounds in blood headspace and nasal breath

Breath analysis is a form of metabolomics that utilises the identification and quantification of volatile chemicals to provide information about physiological or pathological processes occurring within the body. An inherent assumption of such analyses is that the concentration of the exhaled gases correlates with the concentration of the same gas in the tissue of interest. In this study we have investigated this assumption by quantifying some volatile compounds in peripheral venous blood headspace, and in nasal breath collected in Tedlar bags obtained at the same time from 30 healthy volunteers, prior to analysis by selected ion flow tube mass spectrometry. Some endogenous compounds were significantly correlated between blood headspace and nasal breath, such as isoprene (r _p = 0.63) and acetone (r _p = 0.68), however many, such as propanol (r _p = -0.26) and methanol (r _p = 0.23), were not. Furthermore, the relative concentrations of volatiles in blood and breath varied markedly between compounds, with some, such as isoprene and acetone, having similar concentrations in each, while others, such as acetic acid, ammonia and methanol, being significantly more abundant in breath, and others, such as methanal, being detectable only in breath. We also observed that breath propanol and acetic acid concentrations were higher in male compared to female participants, and that the blood headspace methanol concentration was negatively correlated to body mass index. No relationship between volatile concentrations and age was observed. Our data suggest that breath concentrations of volatiles do not necessarily give information about the same compound in the blood stream. This is likely due to the upper airway contributing compounds over and above that originating in the circulation. An investigation of the relationship between breath volatile concentrations and that in the tissue(s) of interest should therefore become a routine part of the development process of breath-based biomarkers.

Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma

BACKGROUND

Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids (ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control.

OBJECTIVES

To determine whether exhaled volatile organic compounds (VOCs) as measured by gas-chromatography/mass-spectrometry (GC/MS) and electronic nose (eNose) technology discriminate between clinically stable and unstable episodes of asthma.

METHODS

Twenty-three patients with (partly) controlled mild to moderate persistent asthma using ICS were included in this prospective steroid withdrawal study. Exhaled metabolites were measured at baseline, during loss of control and after recovery. Standardized sampling of exhaled air was performed, after which samples were analysed by GC/MS and eNose. Univariate analysis of covariance (ANCOVA), followed by multivariate principal component analysis (PCA) was used to reduce data dimensionality. Next paired t tests were utilized to analyse within-subject breath profile differences at the different time-points. Finally, associations between exhaled metabolites and sputum inflammation markers were examined.

RESULTS

Breath profiles by eNose showed 95% (21/22) correct classification for baseline vs loss of control and 86% (19/22) for loss of control vs recovery. Breath profiles using GC/MS showed accuracies of 68% (14/22) and 77% (17/22) for baseline vs loss of control and loss of control vs recovery, respectively. Significant associations between exhaled metabolites captured by GC/MS and sputum eosinophils were found (Pearson r≥.46, P<.01).

CONCLUSIONS & CLINICAL RELEVANCE

Loss of asthma control can be discriminated from clinically stable episodes by longitudinal monitoring of exhaled metabolites measured by GC/MS and particularly eNose. Part of the uncovered biomarkers was associated with sputum eosinophils. These findings provide proof of principle for monitoring and identification of loss of asthma control by breathomics.

Analytical methodologies for broad metabolite coverage of exhaled breath condensate

Breath analysis has been gaining popularity as a non-invasive technique that is amenable to a broad range of medical uses. One of the persistent problems hampering the wide application of the breath analysis method is measurement variability of metabolite abundances stemming from differences in both sampling and analysis methodologies used in various studies. Mass spectrometry has been a method of choice for comprehensive metabolomic analysis. For the first time in the present study, we juxtapose the most commonly employed mass spectrometry-based analysis methodologies and directly compare the resultant coverages of detected compounds in exhaled breath condensate in order to guide methodology choices for exhaled breath condensate analysis studies. Four methods were explored to broaden the range of measured compounds across both the volatile and non-volatile domain. Liquid phase sampling with polyacrylate Solid-Phase MicroExtraction fiber, liquid phase extraction with a polydimethylsiloxane patch, and headspace sampling using Carboxen/Polydimethylsiloxane Solid-Phase MicroExtraction (SPME) followed by gas chromatography mass spectrometry were tested for the analysis of volatile fraction. Hydrophilic interaction liquid chromatography and reversed-phase chromatography high performance liquid chromatography mass spectrometry were used for analysis of non-volatile fraction. We found that liquid phase breath condensate extraction was notably superior compared to headspace extraction and differences in employed sorbents manifested altered metabolite coverages. The most pronounced effect was substantially enhanced metabolite capture for larger, higher-boiling compounds using polyacrylate SPME liquid phase sampling. The analysis of the non-volatile fraction of breath condensate by hydrophilic and reverse phase high performance liquid chromatography mass spectrometry indicated orthogonal metabolite coverage by these chromatography modes. We found that the metabolite coverage could be enhanced significantly with the use of organic solvent as a device rinse after breath sampling to collect the non-aqueous fraction as opposed to neat breath condensate sample. Here, we show the detected ranges of compounds in each case and provide a practical guide for methodology selection for optimal detection of specific compounds.

Exhaled breath analysis for the early detection of lung cancer: recent developments and future prospects

In lung cancer, the prognosis and treatment options depend directly on tumor size and its spread at the time of diagnosis. There is therefore a constant search for methods that will allow early detection of cancerous lung nodules. With advancing imaging technology and implantation of screening routines in high-risk populations by low-dose computerized tomography, a significant increase in the number of diagnosed small peripheral lesions can be expected. While early detection of small cancerous lesions carries the benefit of wider treatment options and better prognosis, the process of obtaining a biopsy to confirm a cancerous tissue is not free of complications and bears inconveniences and stress to the patient. This review discusses the potential use of exhaled breath analysis as a simple, noninvasive tool for early detection of lung cancer and characterization of suspicious lung nodules.

A Novel Medical E-Nose Signal Analysis System

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.

Non-small-cell lung cancer cell lines A549 and NCI-H460 express hypoxanthine guanine phosphoribosyltransferase on the plasma membrane

In both males and females, lung cancer is one of the most lethal cancers worldwide and accounts for >30% of cancer-related deaths. Despite advances in biomarker analysis and tumor characterization, there remains a need to find suitable biomarker antigen targets for treatment in late-stage lung cancer. Previous research on the salvage pathway enzyme TK1 shows a unique relationship with cancer patients as serum levels are raised according to cancer grade. To expand this analysis, the other salvage pathway enzymes were evaluated for possible upregulation within lung cancer. Adenine phosphoribosyltransferase, deoxycytidine kinase, and hypoxanthine guanine phosphoribosyltransferase (HPRT) were assessed for their presentation on two non-small-cell lung cancer cell lines NCI-H460 and A549. In the present study, we show that deoxycytidine kinase and adenine phosphoribosyltransferase have no significant relationship with the membrane of NCI-H460 cells. However, we found significant localization of HPRT to the membrane of NCI-H460 and A549 cells. When treated with anti-HPRT antibodies, the average fluorescence of the cell population increased by 24.3% and 12.9% in NCI-H460 and A549 cells, respectively, in comparison with controls. To ensure that expression was not attributed to cytoplasmic HPRT, confocal microscopy was performed to visualize HPRT binding on the plasma membrane. After staining NCI-H460 cells treated with both fluorescent antibodies and a membrane-specific dye, we observed direct overlap between HPRT and the membrane of the cancer cells. Additionally, gold-conjugated antibodies were used to label and quantify the amount of HPRT on the cell surface using scanning electron microscopy and energy-dispersive analysis X-ray. Further confirming HPRT presence, the gold weight percentage of the sample increased significantly when NCI-H460 cells were exposed to HPRT antibody (P=0.012) in comparison with isotype controls. Our results show that HPRT is localized on the surface of these non-small-cell lung cancer cell lines.

Electronic Nose Technology in Respiratory Diseases

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH₃CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer.

Risk analysis of lung cancer and effects of stress level on cancer risk through neuro-fuzzy model

A significant number of people pass away due to limited medical resources for the battle with cancer. Fatal cases can be reduced by using the computational techniques in the medical and health system. If the cancer is diagnosed early, the chance of successful treatment increases. In this study, the risk of getting lung cancer will be obtained and patients will be provided with directions to exterminate the risk. After calculating the risk value for lung cancer, status of the patient's susceptibility and resistance to stress is used in determining the effects of stress to disease. In order to resolve the problem, the neuro-fuzzy logic model has been presented. When encouraging results are obtained from the study; the system will form a pre-diagnosis for the people who possibly can have risk of getting cancer due to working conditions or living standards. Therefore, this study will enable these people to take precautions to prevent the risk of cancer. In this study a new t-norm operator has been utilized in the problem. Finally, the performance of the proposed method has been compared to other methods. Beside this, the contribution of neuro-fuzzy logic model in the field of health and topics of artificial intelligence will also be examined in this study.

Detection of cancer through exhaled breath: a systematic review

Background

Timely diagnosis of cancer represents a challenging task; in particular, there is a need for reliable non-invasive screening tools that could achieve high levels of adherence at virtually no risk in population-based screening. In this review, we summarize the current evidence of exhaled breath analysis for cancer detection using standard analysis techniques and electronic nose.

Methods

Relevant studies were identified searching Pubmed and Web of Science databases until April 30, 2015. Information on breath test performance, such as sensitivity and specificity, was extracted together with volatile compounds that were used to discriminate cancer patients from controls. Performance of different breath analysis techniques is provided for various cancers together with information on methodological issues, such as breath sampling protocol and validation of the results.

Results

Overall, 73 studies were included, where two-thirds of the studies were conducted on lung cancer. Good discrimination usually required a combination of multiple biomarkers, and area under the receiver operating characteristic curve or accuracy reached levels of 0.9 or higher in multiple studies. In 25% of the reported studies, classification models were built and validated on the same datasets. Huge variability was seen in different aspects among the studies.

Conclusions

Analyses of exhaled breath yielded promising results, although standardization of breath collection, sample storage and data handling remain critical issues. In order to foster breath analysis implementation into practice, larger studies should be implemented in true screening settings, paying particular attention to standardization in breath collection, consideration of covariates, and validation in independent population samples.

Piezoresistive Membrane Surface Stress Sensors for Characterization of Breath Samples of Head and Neck Cancer Patients

For many diseases, where a particular organ is affected, chemical by-products can be found in the patient’s exhaled breath. Breath analysis is often done using gas chromatography and mass spectrometry, but interpretation of results is difficult and time-consuming. We performed characterization of patients’ exhaled breath samples by an electronic nose technique based on an array of nanomechanical membrane sensors. Each membrane is coated with a different thin polymer layer. By pumping the exhaled breath into a measurement chamber, volatile organic compounds present in patients’ breath diffuse into the polymer layers and deform the membranes by changes in surface stress. The bending of the membranes is measured piezoresistively and the signals are converted into voltages. The sensor deflection pattern allows one to characterize the condition of the patient. In a clinical pilot study, we investigated breath samples from head and neck cancer patients and healthy control persons. Evaluation using principal component analysis (PCA) allowed a clear distinction between the two groups. As head and neck cancer can be completely removed by surgery, the breath of cured patients was investigated after surgery again and the results were similar to those of the healthy control group, indicating that surgery was successful.

Rapid Growth of Zinc Oxide Nanotube-Nanowire Hybrid Architectures and Their Use in Breast Cancer-Related Volatile Organics Detection

A simple direct method for the rapid fabrication of zinc oxide nanotube-nanowire hybrid structure in an environmentally friendly way is described here. Zinc foils were anodized in an aqueous solution of washing soda and baking soda at room temperature in order to obtain the hybrid architecture. At the beginning of the process nanowires were formed on the substrate. The wider nanowires transformed into nanotubes in about a minute and grew in length with time. The morphological integrity was maintained upon heat treatment at temperatures up to the melting point of the substrate (∼400 °C) except that the nanotube wall became porous. The chemiresistor devices fabricated using the heat-treated structure exhibited high response to low-concentration volatile organic compounds that are considered markers for breast cancer. The response was not significantly affected by high humidity or presence of hydrogen, methane, or carbon dioxide. The devices are expected to find use as breath sensors for noninvasive early detection of breast cancer.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate

Exhaled breath condensate (EBC) is one of the less employed biofluids when searching for clinical markers, despite its non-invasive sampling and the potential relationship between its composition and respiratory disease phenotypes such as lung cancer. The advanced stage at which lung cancer is usually detected is the main reason for the high mortality rate of this carcinogenic disease. In this preliminary research, EBC was used as clinical sample to develop a screening tool for lung cancer discrimination from two control groups (with and without risk factor). Three panels of metabolites were configured using the PanelomiX tool to minimize false negatives (specificity) and false positives (sensitivity). The combination of five metabolites led to three panels providing a sensitivity above 77.9%, specificity above 67.5% and the area under the curve (AUC) above 77.5% for the three panels. An additional study was developed as a first approach to study the statistical significance of metabolites at different stages of lung cancer.

Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

In this article, a database of blood:air and fat:air partition coefficients (λ b:a and λ f:a) is reported for estimating 1678 volatile organic compounds recently reported to appear in the volatilome of the healthy human. For this purpose, a quantitative structure-property relationship (QSPR) approach was applied and a novel method for Henry's law constants prediction developed. A random forest model based on Molecular Operating Environment 2D (MOE2D) descriptors based on 2619 literature-reported Henry's constant values was built. The calculated Henry's law constants correlate very well (R(2) test  =  0.967) with the available experimental data. Blood:air and fat:air partition coefficients were calculated according to the method proposed by Poulin and Krishnan using the estimated Henry's constant values. The obtained values correlate reasonably well with the experimentally determined ones for a test set of 90 VOCs (R(2)  =  0.95). The provided data aim to fill in the literature data gap and further assist the interpretation of results in studies of the human volatilome.

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening

Background

Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening.

Methods

Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.

Results

Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel.

Conclusions

Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening.

Trial Registration

ClinicalTrials.gov NCT00639067

Detection of volatile organic compounds (VOCs) from exhaled breath as noninvasive methods for cancer diagnosis

The detection of cancer at an early stage is often significant in the successful treatment of the disease. Tumor cells have been reported to generate unique cancer volatile organic compound (VOC) profiles which can reflect the disease conditions. The detection and analysis of VOC biomarkers from exhaled breath has been recognized as a new frontier in cancer diagnostics and health inspections owing to its potential in developing rapid, noninvasive, and inexpensive cancer screening tools. To detect specific VOCs of low concentrations from exhaled breath, and to enhance the accuracy of early diagnosis, many breath collection and analysis approaches have been developed. This paper will summarize and critically review the exhaled-breath VOC-related sampling, collection, detection, and analytical methods, especially the recent development in VOC sensors. VOC sensors are commonly inexpensive, portable, programmable, easy to use, and can obtain data in real time with high sensitivities. Therefore, many sensor-based VOC detection techniques have huge potential in clinical point-of-care use.

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.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Canine olfaction and electronic nose detection of volatile organic compounds in the detection of cancer: a review

Olfactory cancer detection shows promise as an affordable, precise, and noninvasive way to screen for cancer. This review focuses on two methods of olfactory cancer detection: first, the ability of canines to differentiate between cancerous and healthy individuals through the use of biological samples and second, electronic nose technology that uses chemical sensors to detect known biomarkers in exhaled breath. This review summarizes and critiques past research and outlines future directions to improve understanding of both canine olfaction and electronic nose technology.

Study of the art: canine olfaction used for cancer detection on the basis of breath odour. Perspectives and limitations

Experimental studies using trained dogs to identify breath odour markers of human cancer, published in the recent decade, have been analyzed and compared with the authors' own results. Particular published studies differ as regards the experimental setup, kind of odour samples (breath, urine, tumor tissue, serum), sample collection methods, dogs' characteristics and dog training methods as well as in results presented in terms of detection sensitivity and specificity. Generally it can be stated that trained dogs are able to distinguish breath odour samples typical for patients with lung cancer and other cancers from samples typical for healthy humans at a 'better than by chance' rate. Dogs' indications were positively correlated with content of 2-pentanone and ethyl acetate (r = 0.97 and r = 0.85 respectively) and negatively correlated with 1-propanol and propanal in breath samples (r = -0.98 and -0.87 respectively). The canine method has some advantages as a potential cancer-screening method, due to its non-invasiveness, simplicity of odour sampling and storage, ease of testing and interpretation of results and relatively low costs. Disadvantages and limitations of this method are related to the fact that it is still not known exactly to which chemical compounds and/or their combinations the dogs react. So far it could not be confirmed that dogs are able to sniff out early preclinical cancer stages with approximately the same accuracy as already diagnosed cases. The detection accuracy may vary due to failure in conditioning of dogs, decreasing motivation or confounding factors. The dogs' performance should be systematically checked in rigorous double-blind procedures. Recommendations for methodological standardization have been proposed.

Analysis of breath volatile organic compounds in children with chronic liver disease compared to healthy controls

Breath testing is increasingly being used as a non-invasive diagnostic tool for disease states across medicine. The purpose of this study was to compare the levels of volatile organic compounds (VOCs) as measured by mass spectrometry in healthy children and children with chronic liver disease (CLD). Patients between the ages of 6 and 21 were recruited for the study. Control subjects were recruited from a general pediatric population during well-child visits, while patients with CLD were recruited from pediatric gastroenterology clinic visits. The diagnosis of CLD was confirmed by clinical, laboratory, and/or histologic data. A single exhaled breath was collected and analyzed by means of selected-ion flow-tube mass spectrometry per protocol. A total of 104 patients were included in the study (49 with CLD and 55 healthy controls). Of the patients with CLD, 20 had advanced liver fibrosis (F3-F4). In the CLD cohort, levels of exhaled 1-decene, 1-heptene, 1-octene and 3 methylhexane were found to be significantly higher when compared to the control population (p < 0.001, p = 0.035, p < 0.001 and p = 0.004, respectively). Exhaled 1-nonene, (E)-2-nonene, and dimethyl sulfide levels were found to be significantly lower in patients with CLD patients when compared to controls (p < 0.001, p < 0.001 and p = 0.007, respectively). By utilizing a combination of five of the VOCs, the accuracy for predicting the presence of CLD was excellent (AUROC = 0.97). Our study demonstrates that children with CLD have a unique pattern of exhaled VOCs. Utilization of a combination of these VOCs represents a promising non-invasive diagnostic tool and may provide further insight into the pathophysiologic processes and pathways leading to pediatric liver disease. Further analysis of these compounds in external cohorts are needed to validate our findings.

Advances in the early detection of lung cancer using analysis of volatile organic compounds: from imaging to sensors

According to the World Health Organization (WHO), 1.37 million people died of lung cancer all around the world in 2008, occupying the first place in all cancer-related deaths. However, this number might be decreased if patients were detected earlier and treated appropriately. Unfortunately, traditional imaging techniques are not sufficiently satisfactory for early detection of lung cancer because of limitations. As one alternative, breath volatile organic compounds (VOCs) may reflect the biochemical status of the body and provide clues to some diseases including lung cancer at early stage. Early detection of lung cancer based on breath analysis is becoming more and more valued because it is non-invasive, sensitive, inexpensive and simple. In this review article, we analyze the limitations of traditional imaging techniques in the early detection of lung cancer, illustrate possible mechanisms of the production of VOCs in cancerous cells, present evidence that supports the detection of such disease using breath analysis, and summarize the advances in the study of E-noses based on gas sensitive sensors. In conclusion, the analysis of breath VOCs is a better choice for the early detection of lung cancer compared to imaging techniques. We recommend a more comprehensive technique that integrates the analysis of VOCs and non-VOCs in breath. In addition, VOCs in urine may also be a trend in research on the early detection of lung cancer.

Current Challenges in Volatile Organic Compounds Analysis as Potential Biomarkers of Cancer

An early diagnosis and appropriate treatment are crucial in reducing mortality among people suffering from cancer. There is a lack of characteristic early clinical symptoms in most forms of cancer, which highlights the importance of investigating new methods for its early detection. One of the most promising methods is the analysis of volatile organic compounds (VOCs). VOCs are a diverse group of carbon-based chemicals that are present in exhaled breath and biofluids and may be collected from the headspace of these matrices. Different patterns of VOCs have been correlated with various diseases, cancer among them. Studies have also shown that cancer cells in vitro produce or consume specific VOCs that can serve as potential biomarkers that differentiate them from noncancerous cells. This review identifies the current challenges in the investigation of VOCs as potential cancer biomarkers, by the critical evaluation of available matrices for the in vivo and in vitro approaches in this field and by comparison of the main extraction and detection techniques that have been applied to date in this area of study. It also summarises complementary in vivo, ex vivo, and in vitro studies conducted to date in order to try to identify volatile biomarkers of cancer.

Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose

OBJECTIVE

Volatile organic compounds (VOCs) in exhaled breath as measured by electronic nose (e-nose) have utility as biomarkers to detect subjects at risk of having lung cancer in a screening setting. We hypothesize that breath analysis using an e-nose chemo-resistive sensor array could be used as a screening tool to discriminate patients diagnosed with lung cancer from high-risk smokers.

METHODS

Breath samples from 191 subjects-25 lung cancer patients and 166 high-risk smoker control subjects without cancer-were analyzed. For clinical relevancy, subjects in both groups were matched for age, sex, and smoking histories. Classification and regression trees and discriminant functions classifiers were used to recognize VOC patterns in e-nose data. Cross-validated results were used to assess classification accuracy. Repeatability and reproducibility of e-nose data were assessed by measuring subject-exhaled breath in parallel across two e-nose devices.

RESULTS

e-Nose measurements could distinguish lung cancer patients from high-risk control subjects, with a better than 80% classification accuracy. Subject sex and smoking status impacted classification as area under the curve results (ex-smoker males 0.846, ex-smoker female 0.816, current smoker male 0.745, and current smoker female 0.725) demonstrated. Two e-nose systems could be calibrated to give equivalent readings across subject-exhaled breath measured in parallel.

CONCLUSIONS

e-Nose technology may have significant utility as a noninvasive screening tool for detecting individuals at increased risk for lung cancer.

SIGNIFICANCE

The results presented further the case that VOC patterns could have real clinical utility to screen for lung cancer in the important growing ex-smoker population.

A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds

In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.

Screening and early detection efforts in lung cancer

Lung cancer is the leading cause of cancer-related death in the United States. Since publication of results from the National Lung Screening Trial, several professional organizations, including the US Preventive Services Task Force, have published guidelines recommending low-dose computed tomography for screening in asymptomatic, high-risk individuals. The benefits of screening include detection of cancer at an early stage when a definitive cure is possible, but the risks include overdiagnosis, false-positive results, psychological distress, and radiation exposure. The current review covers the scope of low-dose computed tomography screening, potential risks, costs, and future directions in the efforts for early detection of lung cancer.

Breath sensors for lung cancer diagnosis

The scope of the applications of breath sensors is abundant in disease diagnosis. Lung cancer diagnosis is a well-fitting health-related application of this technology, which is of utmost importance in the health sector, because lung cancer has the highest death rate among all cancer types, and it brings a high yearly global burden. The aim of this review is first to provide a rational basis for the development of breath sensors for lung cancer diagnostics from a historical perspective, which will facilitate the transfer of the idea into the rapidly evolving sensors field. Following examples with diagnostic applications include colorimetric, composite, carbon nanotube, gold nanoparticle-based, and surface acoustic wave sensor arrays. These select sensor applications are widened by the state-of-the-art developments in the sensors field. Coping with sampling sourced artifacts and cancer staging are among the debated topics, along with the other concerns like proteomics approaches and biomimetic media utilization, feature selection for data classification, and commercialization.

Exhaled breath analysis for lung cancer

Early diagnosis of lung cancer results in improved survival compared to diagnosis with more advanced disease. Early disease is not reliably indicated by symptoms. Because investigations such as bronchoscopy and needle biopsy have associated risks and substantial costs, they are not suitable for population screening. Hence new easily applicable tests, which can be used to screen individuals at risk, are required. Biomarker testing in exhaled breath samples is a simple, relatively inexpensive, non-invasive approach. Exhaled breath contains volatile and non-volatile organic compounds produced as end-products of metabolic processes and the composition of such compounds varies between healthy subjects and subjects with lung cancer. Many studies have analysed the patterns of these compounds in exhaled breath. In addition studies have also reported that the exhaled breath condensate (EBC) can reveal gene mutations or DNA abnormalities in patients with lung cancer. This review has summarised the scientific evidence demonstrating that lung cancer has distinct chemical profiles in exhaled breath and characteristic genetic changes in EBC. It is not yet possible to accurately identify individuals with lung cancer in at risk populations by any of these techniques. However, analysis of both volatile organic compounds in exhaled breath and of EBC have great potential to become clinically useful diagnostic and screening tools for early stage lung cancer detection.

The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva

Breath analysis is a young field of research with its roots in antiquity. Antoine Lavoisier discovered carbon dioxide in exhaled breath during the period 1777-1783, Wilhelm (Vilém) Petters discovered acetone in breath in 1857 and Johannes Müller reported the first quantitative measurements of acetone in 1898. A recent review reported 1765 volatile compounds appearing in exhaled breath, skin emanations, urine, saliva, human breast milk, blood and feces. For a large number of compounds, real-time analysis of exhaled breath or skin emanations has been performed, e.g., during exertion of effort on a stationary bicycle or during sleep. Volatile compounds in exhaled breath, which record historical exposure, are called the 'exposome'. Changes in biogenic volatile organic compound concentrations can be used to mirror metabolic or (patho)physiological processes in the whole body or blood concentrations of drugs (e.g. propofol) in clinical settings-even during artificial ventilation or during surgery. Also compounds released by bacterial strains like Pseudomonas aeruginosa or Streptococcus pneumonia could be very interesting. Methyl methacrylate (CAS 80-62-6), for example, was observed in the headspace of Streptococcus pneumonia in concentrations up to 1420 ppb. Fecal volatiles have been implicated in differentiating certain infectious bowel diseases such as Clostridium difficile, Campylobacter, Salmonella and Cholera. They have also been used to differentiate other non-infectious conditions such as irritable bowel syndrome and inflammatory bowel disease. In addition, alterations in urine volatiles have been used to detect urinary tract infections, bladder, prostate and other cancers. Peroxidation of lipids and other biomolecules by reactive oxygen species produce volatile compounds like ethane and 1-pentane. Noninvasive detection and therapeutic monitoring of oxidative stress would be highly desirable in autoimmunological, neurological, inflammatory diseases and cancer, but also during surgery and in intensive care units. The investigation of cell cultures opens up new possibilities for elucidation of the biochemical background of volatile compounds. In future studies, combined investigations of a particular compound with regard to human matrices such as breath, urine, saliva and cell culture investigations will lead to novel scientific progress in the field.