Breath volatile organic compound analysis: an emerging method for gastric cancer detection

Exhaled volatolomics profiling facilitates personalized screening for gastric cancer

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics

INTRODUCTION

Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.

OBJECTIVES

This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).

METHODS

We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.

RESULTS

The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Volatilomes of human infection

The human volatilome comprises a vast mixture of volatile emissions produced by the human body and its microbiomes. Following infection, the human volatilome undergoes significant shifts, and presents a unique medium for non-invasive biomarker discovery. In this review, we examine how the onset of infection impacts the production of volatile metabolites that reflects dysbiosis by pathogenic microbes. We describe key analytical workflows applied across both microbial and clinical volatilomics and emphasize the value in linking microbial studies to clinical investigations to robustly elucidate the metabolic species and pathways leading to the observed volatile signatures. We review the current state of the art across microbial and clinical volatilomics, outlining common objectives and successes of microbial-clinical volatilomic workflows. Finally, we propose key challenges, as well as our perspectives on emerging opportunities for developing clinically useful and targeted workflows that could significantly enhance and expedite current practices in infection diagnosis and monitoring.

Toward the development of sensors for lung cancer: The adsorption of 1-propanol on hydrophobic zeolites

A healthy breath is mainly composed of water, carbon dioxide, molecular nitrogen, and oxygen and it contains many species, in small quantities, which are related to the ambient atmosphere and the metabolism. The breath of a person affected by lung cancer presents a concentration of 1-propanol higher than usual. In this context, the development of specific sensors to detect 1-propanol from breath is of high interest. The amount of propanol usually detected on the breath is of few ppb; this small quantity is a handicap for a reliable diagnostic. This limitation can be overcome if the sensor is equipped with a pre-concentrator. Our studies aim to provide an efficient material playing this role. This will contribute to the development of reliable and easy to use lung cancer detectors. For this, we investigate the properties of a few hydrophobic porous materials (chabazite, silicalite-1, and dealuminated faujasite). Hydrophobic structures are used to avoid saturation of materials by the water present in the exhaled breath. Our experimental and simulation results suggest that silicalite -1 (MFI) is the most suitable structure to be used as a pre-concentrator.

Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer

Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC.

Novel biomarkers used for early diagnosis and tyrosine kinase inhibitors as targeted therapies in colorectal cancer

Colorectal cancer (CRC) is the third most common and second most lethal type of cancer worldwide, presenting major health risks as well as economic costs to both people and society. CRC survival chances are significantly higher if the cancer is diagnosed and treated early. With the development of molecular biology, numerous initiatives have been undertaken to identify novel biomarkers for the early diagnosis of CRC. Pathological disorders can be diagnosed at a lower cost with the help of biomarkers, which can be detected in stool, blood, and tissue samples. Several lines of evidence suggest that the gut microbiota could be used as a biomarker for CRC screening and treatment. CRC treatment choices include surgical resection, chemotherapy, immunotherapy, gene therapy, and combination therapies. Targeted therapies are a relatively new and promising modality of treatment that has been shown to increase patients' overall survival (OS) rates and can inhibit cancer cell development. Several small-molecule tyrosine kinase inhibitors (TKIs) are being investigated as potential treatments due to our increasing awareness of CRC's molecular causes and oncogenic signaling. These compounds may inhibit critical enzymes in controlling signaling pathways, which are crucial for CRC cells' development, differentiation, proliferation, and survival. On the other hand, only one of the approximately 42 TKIs that demonstrated anti-tumor effects in pre-clinical studies has been licensed for clinical usage in CRC. A significant knowledge gap exists when bringing these tailored medicines into the clinic. As a result, the emphasis of this review is placed on recently discovered biomarkers for early diagnosis as well as tyrosine kinase inhibitors as possible therapy options for CRC.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

Analysis of a Broad Range of Carbonyl Metabolites in Exhaled Breath by UHPLC-MS

Analysis of volatile organic compounds (VOCs) in exhaled breath (EB) has shown great potential for disease detection including lung cancer, infectious respiratory diseases, and chronic obstructive pulmonary disease. Although many breath sample collection and analytical methods have been developed for breath analysis, analysis of metabolic VOCs in exhaled breath is still a challenge for clinical application. Many carbonyl compounds in exhaled breath are related to the metabolic processes of diseases. This work reports a method of ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) for the analysis of a broad range of carbonyl metabolites in exhaled breath. Carbonyl compounds in the exhaled breath were captured by a fabricated silicon microreactor with a micropillar array coated with 2-(aminooxy)ethyl-N,N,N-trimethylammonium (ATM) triflate. A total of six subgroups consisting of saturated aldehydes and ketones, hydroxy-aldehydes, and hydroxy-ketones, unsaturated 2-alkenals, and 4-hydroxy-2-alkenals were identified in the exhaled breath. The combination of a silicon microreactor for the selective capture of carbonyl compounds with UHPLC-MS analysis may provide a quantitative method for the analysis of carbonyls to identify disease markers in exhaled breath.

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q²Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q²Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q²Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q²Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study

BACKGROUND

Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing.

METHODS

The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs.

FINDINGS

In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%.

INTERPRETATION

Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs.

FUNDING

National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

A Panel of Bile Volatile Organic Compounds Servers as a Potential Diagnostic Biomarker for Gallbladder Cancer

As no reliable diagnostic methods are available, gallbladder cancer (GBC) is often diagnosed until advanced stages, resulting in a poor prognosis. In the present study, we assessed whether volatile organic compounds (VOCs) could be used as a diagnostic tool for GBC. The VOCs in bile samples collected from 32 GBC patients were detected by gas chromatography-ion mobility spectrometry (GC-IMS), and 54 patients with benign gallbladder diseases (BGD) were used as controls. Both principal component analysis and unsupervised hierarchical clustering analysis gave a clear separation of GBC and BGD based on the bile VOC data collected from GC-IMS. A total of 12 differentially expressed VOCs were identified, including four upregulated (cyclohexanone, 2-ethyl-1-hexanol, acetophenone, and methyl benzoate) and eight downregulated [methyl acetate, (E)-hept-2-enal, hexanal, (E)-2-hexenal, (E)-2-pentenal, pentan-1-ol, 1-octen-3-one, and (E)-2-octenal] in GBC compared with BGD. ROC analysis demonstrated a 12-VOC panel con-structed by four machine learning algorithms, which was superior to the traditional tumor marker, CA19-9. Among them, support vector machines and linear discriminant analysis provided the highest AUCs of 0.972, with a sensitivity of 100% and a specificity of 94.4% in the diagnosis of GBC. Collectively, VOCs might be used as a potential tool for the diagnosis of GBC.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

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.