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Einoch Amor R, Levy J, Broza YY, Vangravs R, Rapoport S, Zhang M, Wu W, Leja M, Behar JA, Haick H. Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer. ACS Sens 2023; 8:1450-1461. [PMID: 36926819 DOI: 10.1021/acssensors.2c02422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.
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Affiliation(s)
- Reef Einoch Amor
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Jeremy Levy
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Reinis Vangravs
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Shelley Rapoport
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Min Zhang
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Shaanxi 710126, P.R. China
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Joachim A Behar
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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Einoch Amor R, Zinger A, Broza YY, Schroeder A, Haick H. Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers. Adv Healthc Mater 2022; 11:e2200356. [PMID: 35765713 DOI: 10.1002/adhm.202200356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/24/2022] [Indexed: 01/27/2023]
Abstract
Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.
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Affiliation(s)
- Reef Einoch Amor
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Assaf Zinger
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Avi Schroeder
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
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Einoch Amor R, Nakhleh MK, Barash O, Haick H. Breath analysis of cancer in the present and the future. Eur Respir Rev 2019; 28:28/152/190002. [DOI: 10.1183/16000617.0002-2019] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 05/19/2019] [Indexed: 12/11/2022] Open
Abstract
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.
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