Differentiation between genetic mutations of breast cancer by breath volatolomics

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

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

Identification potential biomarkers for diagnosis, and progress of breast cancer by using high-pressure photon ionization time-of-flight mass spectrometry

BACKGROUND

In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC).

METHODS

A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring.

RESULTS

On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC.

CONCLUSION

Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.

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.

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information

Background and aims

Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions.

Materials and methods

We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation.

Results

The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation.

Conclusion

The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.

Context-Responsive Nanoparticle Derived from Synthetic Zwitterionic Ionizable Phospholipids in Targeted CRISPR/Cas9 Therapy for Basal-like Breast Cancer

The majority of triple negative breast cancers (TNBCs) are basal-like breast cancers (BLBCs), which tend to be more aggressive, proliferate rapidly, and have poor clinical outcomes. A key prognostic biomarker and regulator of BLBC is the Forkhead box C1 (FOXC1) transcription factor. However, because of its functional placement inside the cell nucleus and its structural similarity with other related proteins, targeting FOXC1 for therapeutic benefit, particularly for BLBC, continues to be difficult. We envision targeted nonviral delivery of CRISPR/Cas9 plasmid toward the efficacious knockdown of FOXC1. Keeping in mind the challenges associated with the use of CRISPR/Cas9 in vivo, including off-targeting modifications, and effective release of the cargo, a nanoparticle with context responsive properties can be designed for efficient targeted delivery of CRISPR/Cas9 plasmid. Consequently, we have designed, synthesized, and characterized a zwitterionic amino phospholipid-derived transfecting nanoparticle for delivery of CRISPR/Cas9. The construct becomes positively charged only at low pH, which encourages membrane instability and makes it easier for nanoparticles to exit endosomes. This has enabled effective in vitro and in vivo downregulation of protein expression and genome editing. Following this, we have used EpCAM aptamer to make the system targeted toward BLBC cell lines and to reduce its off-target toxicity. The in vivo efficacy, biodistribution, preliminary pharmacokinetics, and biosafety of the optimized targeted CRISPR nanoplatform is then validated in a rodent xenograft model. Overall, we have attempted to knockout the proto-oncogenic FOXC1 expression in BLBC cases by efficient delivery of CRISPR effectors via a context-responsive nanoparticle delivery system derived from a designer lipid derivative. We believe that the nonviral approach for in vitro and in vivo delivery of CRISPR/Cas9 targeted toward FOXC1, studied herein, will greatly emphasize the therapeutic regimen for BLBC.

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.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

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.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Screening of Breast Cancer from Sweat Samples Analyzed by 2-Dimensional Gas Chromatography-Mass Spectrometry: A Preliminary Study

Breast cancer (BC) remains one of the most commonly diagnosed malignancies in women. There is increasing interest in the development of non-invasive screening methods. Volatile organic compounds (VOCs) emitted through the metabolism of cancer cells are possible novel cancer biomarkers. This study aims to identify the existence of BC-specific VOCs in the sweat of BC patients. Sweat samples from the breast and hand area were collected from 21 BC participants before and after breast tumor ablation. Thermal desorption coupled with two-dimensional gas chromatography and mass spectrometry was used to analyze VOCs. A total of 761 volatiles from a homemade human odor library were screened on each chromatogram. From those 761 VOCs, a minimum of 77 VOCs were detected within the BC samples. Principal component analysis showed that VOCs differ between the pre- and post-surgery status of the BC patients. The Tree-based Pipeline Optimization Tool identified logistic regression as the best-performing machine learning model. Logistic regression modeling identified VOCs that distinguish the pre-and post-surgery state in BC patients on both the breast and hand area with sensitivities close to 1. Further, Shapley additive explanations and the probe variable method identified the most important and pertinent VOCs distinguishing pre- and post-operative status which are mostly of distinct origin for the hand and breast region. Results suggest the possibility to identify endogenous metabolites linked to BC, hence proposing this innovative pipeline as a stepstone to discovering potential BC biomarkers. Large-scale studies in a multi-centered VOC analysis setting must be carried out to validate obtained findings.

Calculations of adsorption-dependent refractive indices of metal-organic frameworks for gas sensing applications

Detection of volatile organic compounds (VOCs) is one of the most challenging tasks in modelling breath analyzers because of their low concentrations (parts-per-billion (ppb) to parts-per-million (ppm)) in breath and the high humidity levels in exhaled breaths. The refractive index is one of the crucial optical properties of metal-organic frameworks (MOFs), which is changeable via the variation of gas species and concentrations that can be utilized as gas detectors. Herein, for the first time, we used Lorentz-Lorentz, Maxwell-Ga, and Bruggeman effective medium approximation (EMA) equations to compute the percentage change in the index of refraction (Δn%) of ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr) and HKUST-1 upon exposure to ethanol at various partial pressures. We also determined the enhancement factors of the mentioned MOFs to assess the storage capability of MOFs and the biosensors' selectivity through guest-host interactions, especially, at low guest concentrations.

The diagnostic breathprint of cancer; the past and the future

The main milestones in the exploration and validation of cancer breathprint for the advancement of personalised diagnosis and medicine are summarised here, with a special attention to the appraisal and translation of the accumulating knowledge from the laboratory to the Point-of-Care phase. An outlook into the opportunities of the use of breathprints and their wider availability for healthcare is offered.

The Versatility and Diagnostic Potential of VOC Profiling for Noninfectious Diseases

A variety of volatile organic compounds (VOCs) are produced and emitted by the human body every day. The identity and concentration of these VOCs reflect an individual's metabolic condition. Information regarding the production and origin of VOCs, however, has yet to be congruent among the scientific community. This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals. Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis. VOC research has prioritized the study of cancer, resulting in many research articles and reviews being written on the topic. This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research, including neurodegenerative and other noninfectious diseases. Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath, urine, blood, feces, and skin. New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results, and we compare their diagnostic dependability with gas chromatography- and mass spectrometry-based techniques. The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

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

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS)

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

Volatile Organic Compounds Analysis as a Potential Novel Screening Tool for Breast Cancer: A Systematic Review

Introduction

An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods

The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results

Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion

Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

Volatile Organic Compounds Frequently Identified after Hyperbaric Hyperoxic Exposure: The VAPOR Library

Diving or hyperbaric oxygen therapy with increased partial pressures of oxygen (pO₂) can have adverse effects such as central nervous system oxygen toxicity or pulmonary oxygen toxicity (POT). Prevention of POT has been a topic of interest for several decades. One of the most promising techniques to determine early signs of POT is the analysis of volatile organic compounds (VOCs) in exhaled breath. We reanalyzed the data of five studies to compose a library of potential exhaled markers for the early detection of POT. GC-MS data from five hyperbaric hyperoxic studies were collected. Wilcoxon signed-rank tests were used to compare baseline- and postexposure measurements; all ion fragments that significantly varied were compared by similarity using the National Institute of Standards and Technology (NIST) library. All identified molecules were cross-referenced with open-source databases and other scientific publications on VOCs to exclude compounds that occurred as a result of contamination, and to identify the compounds most likely to occur due to hyperbaric hyperoxic exposure. After identification and removal of contaminants, 29 compounds were included in the library. This library of hyperbaric hyperoxic-related VOCs can help to advance the development of an early noninvasive marker of POT. It enables validation by others who use more targeted MS-related techniques, instead of full-scale GC-MS, for their exhaled VOC research.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.

Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis

(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3-92.8%, I² = 47.7%), the specificity was 88.4% (95% CI, 87.1-89.5%, I² = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91-0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.

Odors and cancer: Current status and future directions

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

Hollow zeolitic imidazolate framework-7 coated stainless steel fiber for solid phase microextraction of volatile biomarkers in headspace gas of breast cancer cell lines

In this work, we reported the preparation of the hollow zeolitic imidazolate framework-7 (ZIF-7) via etching ZIF-7 with tannic acid, and further fabricated the hollow ZIF-7 coated fiber for the solid phase microextraction (SPME) of the five volatile biomarkers (acetone, isopropanol, hexanal, hexanol and decanal) generated from breast cancer cell lines. The hollow structure not only endowed higher extraction performance for the SPME of analytes, but also improved the diffusion rate of the analytes inside the hollow ZIF-7. Under the optimal conditions, the hollow ZIF-7 coated fiber offered high extraction capacity (25-153 mg g^-1) and enhancement factors (EFs, 2023-11250) for the five biomarkers, good linearity (R² > 0.9918) of acetone and isopropanol (2.5-500 μg L^-1) and hexanol, hexanal, and decanal (1.0-100 μg L^-1), low limits of detection (S/N = 3) of 0.07-0.53 μg L^-1 and the limit of quantifications (LOQs, S/N = 10) of 0.23-1.76 μg L^-1. The precisions (RSDs, %) for intra-day (n = 6), inter-day (n = 5) and fiber-to-fiber (n = 6) were 2.8-7.5%, 4.3-8.5%, and 4.2-14.6%, respectively. The high EFs of the hollow ZIF-7 coated fiber for the five biomarkers resulted from the integrated effects of the large surface area, the unique porous structure, hydrophobic interaction, gate-opening effect, and enhanced properties after etching including faster mass transport, multiple active components, and more exposed active sites. The fabricated hollow ZIF-7 coated fiber lasted at least 140 cycles of extraction/desorption/aging without obvious decrease of extraction ability and no change of crystal structure. Finally, the hollow ZIF-7 coated fiber combined with GC-FID had been successfully used to detect the five biomarkers in the headspace gas of human breast cancer cell lines (MDA-MB-231) and normal mammary cell lines (CCD-1095Sk) with the recoveries of 84-105%. These results revealed the prospect of hollow MOFs as efficient adsorbents for sample pretreatment.

A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer

Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.

Mass Spectrometry-Based Human Breath Analysis: Towards COVID-19 Diagnosis and Research

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath. Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers. Mass spectrometry (MS)-based approaches offer a promising analytical platform for human breath analysis due to their high speed, specificity, sensitivity, reproducibility, and broad coverage, as well as its versatile coupling methods with different chromatographic separation, and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19. Herein, we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples, including metabolites, proteins, microorganisms, and elements. New features of breath sampling and analysis are highlighted. Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

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.

Application of chemoresistive gas sensors and chemometric analysis to differentiate the fingerprints of global volatile organic compounds from diseases. Preliminary results of COPD, lung cancer and breast cancer

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis.

METHODS

A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP.

RESULTS

A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%.

CONCLUSIONS

The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Non-Invasive Biomarkers for Early Detection of Breast Cancer

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of "omics" strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Identification of profiles of volatile organic compounds in exhaled breath by means of an electronic nose as a proposal for a screening method for breast cancer: a case-control study

The objective of the present study was to identify volatile prints from exhaled breath, termed breath-print, from breast cancer (BC) patients and healthy women by means of an electronic nose and to evaluate its potential use as a screening method. A cross-sectional study was performed on 443 exhaled breath samples from women, of whom 262 had been diagnosed with BC by biopsy and 181 were healthy women (control group). Breath-print analysis was performed utilizing the Cyranose 320 electronic nose. Group data were evaluated by principal component analysis (PCA), canonical discriminant analysis (CDA), and support vector machine (SVM), and the test's diagnostic power was evaluated by means of receiver operating characteristic (ROC) curves. The results obtained using the model generated from the CDA, which best describes the behavior of the assessed groups, indicated that the breath-print of BC patients was different from that of healthy women and that they presented with a variability of up to 98.8% and a correct classification of 98%. The sensitivity, specificity, negative predictive value, and positive predictive value reached 100% according to the ROC curve. The present study demonstrates the capability of the electronic nose to separate between healthy subjects and BC patients. This research could have a beneficial impact on clinical practice as we consider that this test could probably be used at the first point before the application of established gold tests (mammography, ultrasound, and biopsy) and substantially improve screening tests in the general population.

Volatile organic compounds in breath can serve as a non-invasive diagnostic biomarker for the detection of advanced adenomas and colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer diagnosis in the Western world.

AIM

To evaluate exhaled volatile organic compounds (VOCs) as a non-invasive biomarker for the detection of CRC and precursor lesions using an electronic nose.

METHODS

In this multicentre study adult colonoscopy patients, without inflammatory bowel disease or (previous) malignancy, were invited for breath analysis. Two-thirds of the breath tests were randomly assigned to develop training models which were used to predict the diagnosis of the remaining patients (external validation). In the end, all data were used to develop final-disease models to further improve the discriminatory power of the algorithms.

RESULTS

Five hundred and eleven breath samples were collected. Sixty-four patients were excluded due to an inadequate breath test (n = 51), incomplete colonoscopy (n = 8) or colitis (n = 5). Classification was based on the most advanced lesion found; CRC (n = 70), advanced adenomas (AAs) (n = 117), non-advanced adenoma (n = 117), hyperplastic polyp (n = 15), normal colonoscopy (n = 125). Training models for CRC and AAs had an area under the curve (AUC) of 0.76 and 0.71 and blind validation resulted in an AUC of 0.74 and 0.61 respectively. Final models for CRC and AAs yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively.

CONCLUSIONS

This study suggests that exhaled VOCs could potentially serve as a non-invasive biomarker for the detection of CRC and AAs. Future studies including more patients could further improve the discriminatory potential of VOC analysis for the detection of (pre-)malignant colorectal lesions. (https://clinicaltrials.gov Identifier NCT03488537).

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Evolution and Evaluation of GC Columns

A chromatographic column is the fundamental element required for gas-chromatographic analysis. The separation of components coming from complex mixtures, prior to their detection was leading to a prominent revolution in different areas of science. Moreover, current advances in gas chromatographic (GC) columns technology and development have been providing almost unlimited possibilities for analysis employing diverse matrices. We aim through this review article to describe the evolution of chromatographic columns, by pointing the most important stages, as well as the new trends and future perspectives predicted for the new generation of GC columns. Furthermore, it was in our scope to present the main fundamentals regarding the theoretical relationships that describe the chromatographic separation, to introduce concepts related to columns selection in accordance with the required application as well as to discuss the available evaluation parameters for columns efficiency. Consequently, the early stages of first columns preparation up to the development of GC capillary columns used nowadays, together with examples of their applications are also reported and described in detail.

Light-activated inorganic CsPbBr2I perovskite for room-temperature self-powered chemical sensing

Halide perovskite materials are excellent light harvesters that have generated enormous interest for photovoltaic technology and an increasing number of other optoelectronic applications. Very recently, their use for miniaturized chemical sensors has shown a promising room-temperature response. Here, we present some insights on the use of CsPbBr2I (CPBI) perovskites for self-powered room-temperature sensing of several environmentally and medically relevant compounds demonstrating rapid detection of down to concentrations of 1 ppm. Notably, the photocurrent of these self-powered CPBI-based devices increases under exposure to both reducing (e.g. acetone, propane) and oxidizing (e.g. NO2, O2) gas molecules and decreases rapidly upon reverting to an inert atmosphere. In situ photoluminescence (PL) analysis of the CPBI during exposure to oxidizing molecules reveals a strongly increased PL intensity and longer lifetime indicating a prevalent role of CPBI trap states in the sensing mechanism. These findings provide new insights for the engineering of perovskite-based materials for their future chemical sensing applications.

Breath analysis of cancer in the present and the future

Most of the currently used diagnostics for cancerous diseases have yet to meet the standards of screening, as they are insufficiently accurate and/or invasive and risky. In this review, we describe the rationale, the progress made to date, and the potential of analysing the exhaled volatile organic compounds as a pathway for enabling early diagnosis of cancer and, therefore, for achieving better clinical prognosis and survival rates. The review highlights the major advancements made in this field, from fundamentals, up to translational phases and clinical trials, with a special emphasis on sensing platforms based on nanomaterials. The prospects for breath analysis in early cancerous disease are presented and discussed.

Graphene-Based Sensors for Human Health Monitoring

Since the desire for real-time human health monitoring as well as seamless human-machine interaction is increasing rapidly, plenty of research efforts have been made to investigate wearable sensors and implantable devices in recent years. As a novel 2D material, graphene has aroused a boom in the field of sensor research around the world due to its advantages in mechanical, thermal, and electrical properties. Numerous graphene-based sensors used for human health monitoring have been reported, including wearable sensors, as well as implantable devices, which can realize the real-time measurement of body temperature, heart rate, pulse oxygenation, respiration rate, blood pressure, blood glucose, electrocardiogram signal, electromyogram signal, and electroencephalograph signal, etc. Herein, as a review of the latest graphene-based sensors for health monitoring, their novel structures, sensing mechanisms, technological innovations, components for sensor systems and potential challenges will be discussed and outlined.

Nanostructured Chemiresistive Gas Sensors for Medical Applications

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors.

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.

Metabolomics and Biomarker Discovery

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

Profiling Single Cancer Cells with Volatolomics Approach

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

•

Measurement of VOCs was achieved at the single-cell level

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

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

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

Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers

Wearable devices are gaining considerable attention owing to the ease with which they can collect crucial information in real-time, both continuously and noninvasively, regarding a wearer's health. A concise summary is given of the three main elements that enable autonomous detection and monitoring of the likelihood or the existence of a health-risk state in continuous and real-time modes, with an emphasis on emerging materials and fabrication techniques in the relevant fields. The first element is the sensing technology used in the noninvasive detection of physiological markers relevant to the state of health. The second element is self-powered devices for longer periods of use by drawing energy from bodily movement and temperature. The third element is the self-healing properties of the materials used in the wearable devices to extended usage if they become scratched or cut. Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further improvement of skin-based wearable devices are also presented and discussed.

Guide to Selecting a Biorecognition Element for Biosensors

Biosensors are powerful diagnostic tools defined as having a biorecognition element for analyte specificity and a transducer for a quantifiable signal. There are a variety of different biorecognition elements, each with unique characteristics. Understanding the advantages and disadvantages of each biorecognition element and their influence on overall biosensor performance is crucial in the planning stages to promote the success of novel biosensor development. Therefore, this review will focus on selecting the optimal biorecognition element in the preliminary design phase for novel biosensors. Included is a review of the typical characteristics and binding mechanisms of various biorecognition elements, and how they relate to biosensor performance characteristics, specifically sensitivity, selectivity, reproducibility, and reusability. The goal is to point toward language needed to improve the design and development of biosensors toward clinical success.

Breath based volatile organic compounds in the detection of breast, lung, and colorectal cancers: A systematic review

BACKGROUND

Detecting volatile organic compounds (VOCs) could provide a rapid, noninvasive, and inexpensive screening tool for detecting cancer.

OBJECTIVE

In this systematic review, we identified specific exhaled breath VOCs correlated with lung, colorectal, and breast cancer.

METHODS

We identified relevant studies published in 2015 and 2016 by searching Pubmed and Web of Science. The protocol for this systematic review was registered in PROSPERO and the PRISMA guidelines were used in reporting. VOCs and performance data were extracted.

RESULTS

Three hundred and thirty three records were identified and 43 papers were included in the review, of which 20 were review articles themselves. We identified 17 studies that listed the VOCs with at least a subset of statistics on detection cutoff levels, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and gradient.

CONCLUSIONS

Breath analysis for cancer screening and early detection shows promise, because samples can be collected easily, safely, and frequently. While gas chromatography-mass spectrometry is considered the gold standard for identifying specific VOCs, breath analysis has moved into analyzing patterns of VOCs using a variety of different multiple sensor techniques, such as eNoses and nanomaterials. Further development of VOCs for early cancer detection requires clinical trials with standardized breath sampling methods.

Breath biopsy for early detection and precision medicine in cancer

Breath biopsy enables the non-invasive collection and analysis of volatile organic compounds (VOCs) in exhaled breath, providing valuable information about disease processes occurring in the body. Metabolic changes occur in cancer cells at the earliest stages of disease. We discuss progress in the use of breath biopsy for discovery of breath-based biomarkers for early detection of cancer, and potential applications for breath biopsy in enabling precision medicine in cancer.