The Use of Selected Ion Flow Tube-Mass Spectrometry Technology to Identify Breath Volatile Organic Compounds for the Detection of Head and Neck Squamous Cell Carcinoma: A Pilot Study

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

Determination of the two-compartment model parameters of exhaled HCN by fast negative photoionization mass spectrometry

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5-100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis

BACKGROUND

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors.

MATERIALS AND METHODS

An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota.

RESULTS

The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types.

CONCLUSIONS

The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

Detection technologies of volatile organic compounds in the breath for cancer diagnoses

Although there are new approaches in both cancer treatment and diagnosis, overall mortality is a major concern. New technologies have attempted to look at breath volatile organic compounds (VOCs) detection to diagnose cancer. Gas Chromatography and Mass Spectrometry (GC - MS) have remained the gold standard of VOC analysis for decades, but it has limitations in differentiating VOCs between cancer subtypes. To increase efficacy and accuracy, new methods to analyze these breath VOCs have been introduced, such as Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry (SPME/GC-MS), Selected Ion Flow Tube - Mass Spectrometry (SIFT-MS), Proton Transfer Reaction - Mass Spectrometry (PRT-MS), Ion Mobility Spectrometry (IMS), and Colorimetric Sensors. This article highlights new technologies that have been studied and applied in the detection and quantification of breath VOCs for possible cancer diagnoses.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Breath biopsy, a novel technology to identify head and neck squamous cell carcinoma: A systematic review

Head and neck cancers are a heterogeneous group of neoplasms, which together comprise the sixth most common cancer globally. Breath biopsies are a non-invasive clinical investigation that detect volatile organic compounds (VOCs) in exhaled breath. This systematic review examines current applications of breath biopsy for the diagnosis of head and neck squamous cell carcinoma (HNSCC), including data on efficacy and utility, and speculates on the future uses of this non-invasive detection method. Medline, PubMed, Web of Science, Cochrane and Scopus, as well as the grey literature were searched using a search strategy developed to identify relevant studies on the role of breath biopsy in the diagnosis of HNSCC. All included studies were subject to a thorough methodological quality assessment. The initial search generated a total of 1443 articles, 20 of which were eligible for review. A total of 660 HNSCC samples were investigated across the included studies. 3,7-dimethylundecane and benzaldehyde were among several VOCs to be significantly correlated with the presence of HNSCC compared to healthy controls. We show that current breath biopsy methods have high accuracy, specificity and sensitivity for identifying HNSCC. However, further studies are needed given the reported poor quality of the included studies.

Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations

Lung cancer is a leading cause of death worldwide, mostly due to diagnostics in the advanced stage. Therefore, the development of a quick, simple, and non-invasive diagnostic tool to identify cancer is essential. However, the creation of a reliable diagnostic tool is possible only in case of selectivity to other diseases, particularly, cancer of other localizations. This paper is devoted to the study of the variability of exhaled breath samples among patients with lung cancer and cancer of other localizations, such as esophageal, breast, colorectal, kidney, stomach, prostate, cervix, and skin. For this, gas chromatography-mass spectrometry (GC-MS) was used. Two classification models were built. The first model separated patients with lung cancer and cancer of other localizations. The second model classified patients with lung, esophageal, breast, colorectal, and kidney cancer. Mann-Whitney U tests and Kruskal-Wallis H tests were applied to identify differences in investigated groups. Discriminant analysis (DA), gradient-boosted decision trees (GBDT), and artificial neural networks (ANN) were applied to create the models. In the case of classifying lung cancer and cancer of other localizations, average sensitivity and specificity were 68% and 69%, respectively. However, the accuracy of classifying groups of patients with lung, esophageal, breast, colorectal, and kidney cancer was poor.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background

The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC).

Methods

Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning.

Results

Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86–90%.

Conclusions

Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01862-z.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Development of a non-invasive exhaled breath test for the diagnosis of head and neck cancer

BACKGROUND

Improving the ability to identify early-stage head and neck squamous cell carcinoma (HNSCC) can improve treatment outcomes and patient morbidity. We sought to determine the diagnostic accuracy of breath analysis as a non-invasive test for detecting HNSCC.

METHODS

Standardised breath samples were collected from 181 patients suspected of HNSCC prior to any treatment. A selected ion flow-tube mass spectrometer was used to analyse breath for volatile organic compounds. Diagnosis was confirmed by histopathology. A binomial logistic regression model was used to differentiate breath profiles between cancer and control (benign disease) patients based on mass spectrometry derived variables.

RESULTS

In all, 66% of participants had early-stage primary tumours (T1 and T2) and 58% had regional node metastasis. The optimised logistic regression model using three variables had a sensitivity and specificity of 80% and 86%, respectively, with an AUC for ROC curve of 0.821 (95%CI 0.625-1.0) in the testing cohort.

CONCLUSIONS

Breath analysis for non-invasive diagnosis of HNSCC appears to be practical and accurate. Future studies should be conducted in a primary care setting to determine the applicability of breath analysis for early identification of HNSCC.

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H₂SO₄, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H₂SO₄ is more effective at optimising detection of VOCs than HCl or NaOH. H₂SO₄ resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H₂SO₄ to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.

Breath methane to hydrogen ratio as a surrogate marker of intestinal dysbiosis in head and neck cancer

Exhaled breath compounds can non-invasively detect head and neck squamous cell carcinoma (HNSCC). Here we investigated exhaled compounds related to intestinal bacterial carbohydrate fermentation. Fasting breath samples were collected into 3 litre FlexFoil PLUS bags from patients awaiting a biopsy procedure for suspected HNSCC. Samples were analysed using a Syft selected ion flow-tube mass spectrometer and a Quintron BreathTracker. Two tailed non-parametric significance testing was conducted with corrections for multiple imputations. 74 patients were diagnosed (histological) with HNSCC and 61 patients were benign (controls). The methane to hydrogen ratio was significantly different between cancer and non-cancer controls (p = 0.0440). This ratio increased with tumour stage with a significant difference between T1 and T4 tumours (p = 0.0259). Hydrogen levels were significantly higher in controls who were smokers (p = 0.0129), with no smoking dependent methane changes. There were no differences in short chain fatty acids between groups. Exhaled compounds of intestinal carbohydrate fermentation can detect HNSCC patients. These findings suggest a modified carbohydrate fermentation profile in HNSCC patients that is tumour stage and smoking status dependent.

A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms

Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different -omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed.