|
1
|
Raimundo BS, Leitão PM, Vinhas M, Pires MV, Quintas LB, Carvalheiro C, Barata R, Ip J, Coelho R, Granadeiro S, Simões TS, Gonçalves J, Baião R, Rocha C, Alves S, Fidalgo P, Araújo A, Matos C, Simões S, Alves P, Garrido P, Pantarotto M, Carreiro L, Matos R, Bárbara C, Cruz J, Gil N, Luis-Ferreira F, Vaz PD. Breath Insights: Advancing Lung Cancer Early-Stage Detection Through AI Algorithms in Non-Invasive VOC Profiling Trials. Cancers (Basel) 2025; 17:1685. [PMID: 40427182 PMCID: PMC12110429 DOI: 10.3390/cancers17101685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2025] [Revised: 05/13/2025] [Accepted: 05/14/2025] [Indexed: 05/29/2025] Open
Abstract
Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection. Patients and methods: Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles. Results: GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%). Conclusions: Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis.
Collapse
Affiliation(s)
- Bernardo S. Raimundo
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Pedro M. Leitão
- Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal; (P.M.L.); (M.V.)
| | - Manuel Vinhas
- Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal; (P.M.L.); (M.V.)
| | - Maria V. Pires
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Laura B. Quintas
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Catarina Carvalheiro
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Rita Barata
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Joana Ip
- Serviço de Radiologia, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal;
| | - Ricardo Coelho
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Sofia Granadeiro
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Tânia S. Simões
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - João Gonçalves
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Renato Baião
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Carla Rocha
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Sandra Alves
- Unidade de Ensaios Clínicos, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal;
| | - Paulo Fidalgo
- Unidade de Risco e Diagnóstico Precoce, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (P.F.); (A.A.)
| | - Alípio Araújo
- Unidade de Risco e Diagnóstico Precoce, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (P.F.); (A.A.)
| | - Cláudia Matos
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Susana Simões
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Paula Alves
- Serviço de Pneumologia, Centro Hospitalar e Universitário Lisboa Norte, 1649-035 Lisboa, Portugal; (P.A.); (C.B.)
| | - Patrícia Garrido
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Marcos Pantarotto
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Luís Carreiro
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Rogério Matos
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Cristina Bárbara
- Serviço de Pneumologia, Centro Hospitalar e Universitário Lisboa Norte, 1649-035 Lisboa, Portugal; (P.A.); (C.B.)
| | - Jorge Cruz
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Nuno Gil
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| | - Fernando Luis-Ferreira
- Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal; (P.M.L.); (M.V.)
| | - Pedro D. Vaz
- Unidade de Pulmão, Centro Clínico Champalimaud, Fundação Champalimaud, 1400-038 Lisboa, Portugal; (B.S.R.); (M.V.P.); (L.B.Q.); (C.C.); (R.B.); (R.C.); (S.G.); (T.S.S.); (J.G.); (R.B.); (C.R.); (C.M.); (S.S.); (P.G.); (M.P.); (L.C.); (R.M.); (J.C.); (N.G.)
| |
Collapse
|
|
2
|
Szeitz A, Sutton AG, Hallam SJ. A matrix-centered view of mass spectrometry platform innovation for volatilome research. Front Mol Biosci 2024; 11:1421330. [PMID: 39539739 PMCID: PMC11557394 DOI: 10.3389/fmolb.2024.1421330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 10/15/2024] [Indexed: 11/16/2024] Open
Abstract
Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.
Collapse
Affiliation(s)
- Andras Szeitz
- Genome Science and Technology Program, University of British Columbia, Vancouver, BC, Canada
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
- Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Annika G. Sutton
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Steven J. Hallam
- Genome Science and Technology Program, University of British Columbia, Vancouver, BC, Canada
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
- Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry, Chulalongkorn University, Bangkok, Thailand
- Bradshaw Research Institute for Minerals and Mining (BRIMM), University of British Columbia, Vancouver, BC, Canada
- ECOSCOPE Training Program, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
|
3
|
Chaudhary V, Taha BA, Lucky, Rustagi S, Khosla A, Papakonstantinou P, Bhalla N. Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers. ACS Sens 2024; 9:4469-4494. [PMID: 39248694 PMCID: PMC11443536 DOI: 10.1021/acssensors.4c01524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 08/15/2024] [Accepted: 08/21/2024] [Indexed: 09/10/2024]
Abstract
Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.
Collapse
Affiliation(s)
- Vishal Chaudhary
- Physics
Department, Bhagini Nivedita College, University
of Delhi, 110043 Delhi, India
- Centre
for Research Impact & Outcome, Chitkara
University, Punjab 140401, India
| | - Bakr Ahmed Taha
- Department
of Electrical, Electronic and Systems Engineering, Faculty of Engineering
and Built Environment, Universiti Kebangsaan
Malaysia, UKM, 43600 Bangi, Malaysia
| | - Lucky
- Dr.
B. R. Ambedkar Center for Biomedical Research, University of Delhi, 110007 Delhi, India
| | - Sarvesh Rustagi
- School
of Applied and Life Sciences, Uttaranchal
University, Dehradun, Uttarakhand 248007, India
| | - Ajit Khosla
- School of
Advanced Materials and Nanotechnology, Xidian
University, Xi’an 710126, China
| | - Pagona Papakonstantinou
- Nanotechnology
and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, 2-24 York Street, Belfast, Northern Ireland BT15 1AP, United Kingdom
| | - Nikhil Bhalla
- Nanotechnology
and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, 2-24 York Street, Belfast, Northern Ireland BT15 1AP, United Kingdom
- Healthcare
Technology Hub, Ulster University, 2-24 York Street, Belfast, Northern Ireland BT15 1AP, United Kingdom
| |
Collapse
|
|
4
|
Brascia D, De Iaco G, Panza T, Signore F, Carleo G, Zang W, Sharma R, Riahi P, Scott J, Fan X, Marulli G. Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients. INTERDISCIPLINARY CARDIOVASCULAR AND THORACIC SURGERY 2024; 39:ivae149. [PMID: 39226187 PMCID: PMC11379464 DOI: 10.1093/icvts/ivae149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/10/2024] [Accepted: 08/30/2024] [Indexed: 09/05/2024]
Abstract
OBJECTIVES Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested. METHODS Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques. RESULTS Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs. CONCLUSIONS These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening. CLINICAL REGISTRATION NUMBER ClinicalTrials.gov Identifier: NCT06034730.
Collapse
Affiliation(s)
- Debora Brascia
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Thoracic Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Giulia De Iaco
- Thoracic Surgery Unit, Department of Precision and Regenerative Medicine and Jonic Area, University Hospital of Bari, Bari, Italy
| | - Teodora Panza
- Thoracic Surgery Unit, Department of Precision and Regenerative Medicine and Jonic Area, University Hospital of Bari, Bari, Italy
| | - Francesca Signore
- Thoracic Surgery Unit, Department of Precision and Regenerative Medicine and Jonic Area, University Hospital of Bari, Bari, Italy
| | - Graziana Carleo
- Thoracic Surgery Unit, Department of Precision and Regenerative Medicine and Jonic Area, University Hospital of Bari, Bari, Italy
| | - Wenzhe Zang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ruchi Sharma
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Pamela Riahi
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jared Scott
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Xudong Fan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Giuseppe Marulli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Thoracic Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| |
Collapse
|
|
5
|
Chou H, Godbeer L, Allsworth M, Boyle B, Ball ML. Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform. Metabolomics 2024; 20:72. [PMID: 38977623 PMCID: PMC11230972 DOI: 10.1007/s11306-024-02142-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/13/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review. AIM OF REVIEW This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research. KEY SCIENTIFIC CONCEPTS OF REVIEW VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.
Collapse
|
|
6
|
Kita K, Gawinowska M, Chełmińska M, Niedoszytko M. The Role of Exhaled Breath Condensate in Chronic Inflammatory and Neoplastic Diseases of the Respiratory Tract. Int J Mol Sci 2024; 25:7395. [PMID: 39000502 PMCID: PMC11242091 DOI: 10.3390/ijms25137395] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/28/2024] [Accepted: 06/29/2024] [Indexed: 07/16/2024] Open
Abstract
Asthma and chronic obstructive pulmonary disease (COPD) are among the most common chronic respiratory diseases. Chronic inflammation of the airways leads to an increased production of inflammatory markers by the effector cells of the respiratory tract and lung tissue. These biomarkers allow the assessment of physiological and pathological processes and responses to therapeutic interventions. Lung cancer, which is characterized by high mortality, is one of the most frequently diagnosed cancers worldwide. Current screening methods and tissue biopsies have limitations that highlight the need for rapid diagnosis, patient differentiation, and effective management and monitoring. One promising non-invasive diagnostic method for respiratory diseases is the assessment of exhaled breath condensate (EBC). EBC contains a mixture of volatile and non-volatile biomarkers such as cytokines, leukotrienes, oxidative stress markers, and molecular biomarkers, providing significant information about inflammatory and neoplastic states in the lungs. This article summarizes the research on the application and development of EBC assessment in diagnosing and monitoring respiratory diseases, focusing on asthma, COPD, and lung cancer. The process of collecting condensate, potential issues, and selected groups of markers for detailed disease assessment in the future are discussed. Further research may contribute to the development of more precise and personalized diagnostic and treatment methods.
Collapse
Affiliation(s)
- Karolina Kita
- Department of Allergology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Marika Gawinowska
- Department of Allergology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Marta Chełmińska
- Department of Allergology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Marek Niedoszytko
- Department of Allergology, Medical University of Gdansk, 80-210 Gdansk, Poland
| |
Collapse
|
|
7
|
Wang H, Wu Y, Sun M, Cui X. Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study. Sci Rep 2024; 14:8731. [PMID: 38627587 PMCID: PMC11021445 DOI: 10.1038/s41598-024-59474-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/11/2024] [Indexed: 04/19/2024] Open
Abstract
Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists' experience in computed tomography (CT)-based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction-time of flight-mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.
Collapse
Affiliation(s)
- Hao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yinghua Wu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Meixiu Sun
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
- Engineering Research Center of Pulmonary and Critical Care Medicine Technology and Device Ministry of Education, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
| | - Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.
| |
Collapse
|
|
8
|
Zhang A, Gao A, Zhou C, Xue C, Zhang Q, Fuente JMDL, Cui D. Confining Prepared Ultrasmall Nanozymes Loading ATO for Lung Cancer Catalytic Therapy/Immunotherapy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2303722. [PMID: 37748441 DOI: 10.1002/adma.202303722] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/18/2023] [Indexed: 09/27/2023]
Abstract
Nanozymes with inherent enzyme-mimicking catalytic properties combat malignant tumor progression via catalytic therapy, while the therapeutic efficacy still needs to be improved. In this work, ultrasmall platinum nanozymes (nPt) in a confined domain of a wormlike pore channel in gold nanobipyramidal-mesoporous silica dioxide nanocomposites, producing nanozyme carriers AP-mSi with photoenhanced peroxidase ability, are innovatively synthesized. Afterward, based on the prepared AP-mSi, a lung-cancer nanozymes probe (AP-HAI) is ingeniously produced by removing the SiO2 template, modifying human serum albumin, and loading atovaquone molecules (ATO) as well as IR780. Under NIR light irradiation, inner AuP and IR780 collaborate for photothermal process, thus facilitating the peroxidase-like catalytic process of H2 O2 . Additionally, loaded ATO, a cell respiration inhibitor, can impair tumor respiration metabolism and cause oxygen retention, hence enhancing IR780's photodynamic therapy (PDT) effectiveness. As a result, IR780's PDT and nPt nanozymes' photoenhanced peroxidase-like ability endow probes a high ROS productivity, eliciting antitumor immune responses to destroy tumor tissue. Systematic studies reveal that the obvious reactive oxygen species (ROS) generation is obtained by the strategy of using nPt nanozymes and reducing oxygen consumption by ATO, which in turn enables lung-cancer synergetic catalytic therapy/immunogenic-cell-death-based immunotherapy. The results of this work would provide theoretical justification for the practical use of photoenhanced nanozyme probes.
Collapse
Affiliation(s)
- Amin Zhang
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| | - Ang Gao
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| | - Cheng Zhou
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| | - Cuili Xue
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| | - Qian Zhang
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| | - Jesus M De La Fuente
- Institute of Nano Science and Technology, University of Zaragoza, Zaragoza, 50018, Spain
| | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, P. R. China
- National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
| |
Collapse
|
|
9
|
Wang H, Wei X, Wu Y, Zhang B, Chen Q, Fu W, Sun M, Li H. A combined screening study for evaluating the potential of exhaled acetone, isoprene, and nitric oxide as biomarkers of lung cancer. RSC Adv 2023; 13:31835-31843. [PMID: 37908654 PMCID: PMC10614752 DOI: 10.1039/d3ra04522f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 09/21/2023] [Indexed: 11/02/2023] Open
Abstract
Background: the early lung cancer (LC) screening strategy significantly reduces LC mortality. According to previous studies, lung cancer can be effectively diagnosed by analyzing the concentration of volatile organic compounds (VOCs) in human exhaled breath and establishing a diagnosis model based on the different VOCs. This method, called breath analysis, has the advantage of being rapid and non-invasive. To develop a non-invasive, portable breath detection instrument based on cavity ring-down spectroscopy (CRDS), we explored the feasibility of establishing a model with acetone, isoprene, and nitric oxide (NO) exhaled through human breath, which can be detected on the CRDS instrument. Methods: a total of 511 participants were recruited from the Cancer Institute and Hospital, Tianjin Medical University as the discovery set and randomly split (2 : 1) into training set and internal validation set with stratification. For external validation, 51 participants were recruited from the General Hospital, Tianjin Medical University. Acetone and isoprene from exhaled breath were detected by proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS), and NO was measured using CRDS. The model was constructed using the ensemble learning method that set eXtreme gradient boosting and logistic regression as the basis model and logistic regression as the senior model. The model was evaluated based on accuracy, sensitivity, and specificity. Results: the model achieved an accuracy of 78.8%, sensitivity of 81.0%, specificity of 70.0%, and area under the receiver operating curve (ROC, AUC) of 0.8341 (95% CI from 0.8055 to 0.8852) in the internal validation set. Furthermore, it attained an accuracy of 66.7%, sensitivity of 68.2%, specificity of 65.5%, and AUC of 0.6834 (95% CI from 0.5259 to 0.7956) in the external validation set. Conclusion: the model, established with acetone, isoprene, and NO as predictors, possesses the ability to identify LC patients from healthy control (HC) participants. The CRDS instrument, which simultaneously detects acetone, isoprene, and NO, is expected to be a non-invasive, rapid, portable, and accurate device for early screening of LC.
Collapse
Affiliation(s)
- Hao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College Tianjin China
| | - Xin Wei
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College Tianjin China
| | - Yinghua Wu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College Tianjin China
| | - Bojun Zhang
- State Key Laboratory of Separation Membrane and Membrane Processes, School of Materials Science and Engineering, Tianjin University of Technology Tianjin China
| | - Qing Chen
- Department of Cardio-Pulmonary Function, National Clinical Research Center for Cancer, Cancer Institute & Hospital, Tianjin Medical University Tianjin China
| | - Weigui Fu
- State Key Laboratory of Separation Membrane and Membrane Processes, School of Materials Science and Engineering, Tianjin University of Technology Tianjin China
| | - Meixiu Sun
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College Tianjin China
| | - Hongxiao Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College Tianjin China
| |
Collapse
|