Exhaled Breath Biomarkers in Lung Cancer
| Tracking Information | |||||
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| First Received Date ICMJE | May 3, 2011 | ||||
| Last Updated Date | February 28, 2012 | ||||
| Start Date ICMJE | June 2011 | ||||
| Estimated Primary Completion Date | May 2016 (final data collection date for primary outcome measure) | ||||
| Current Primary Outcome Measures ICMJE | Not Provided | ||||
| Original Primary Outcome Measures ICMJE | Not Provided | ||||
| Change History | Complete list of historical versions of study NCT01386203 on ClinicalTrials.gov Archive Site | ||||
| Current Secondary Outcome Measures ICMJE | Not Provided | ||||
| Original Secondary Outcome Measures ICMJE | Not Provided | ||||
| Current Other Outcome Measures ICMJE | Not Provided | ||||
| Original Other Outcome Measures ICMJE | Not Provided | ||||
| Descriptive Information | |||||
| Brief Title ICMJE | Exhaled Breath Biomarkers in Lung Cancer | ||||
| Official Title ICMJE | Early Detection of Lung Cancer - Exhaled Breath Nano-Analysis | ||||
| Brief Summary | Analysis of volatile organic compounds (VOCs) is a new attractive non-invasive field in medical diagnostics. These VOCs can be detected via the exhaled breath. Together with Prof Haick group at the Technion Inst (Israel), the investigators data shows that there is a relation between the VOCs patterns of NSCLC and control cell lines and equivalent states in exhaled breath. The investigators demonstrated that there is a clear discrimination between the lung cancer and the healthy clusters . The investigators also analyzed the headspace of NSCLC and SCLC cell lines and the investigators could discriminate significantly between SCLC versus NSCLC based on their VOCs patterns. This analysis allowed us to identify the specific VOCs consumed or omitted by cancerous cells. Therefore, a non-invasive and highly sensitive test would be extremely valuable for the classification and early screening of lung cancer and for targeted therapy. In this study, the investigators will monitor the VOC pattern of patients with lung cancer as well as high risk cohort and patients under risk/evaluation for lung cancer. Likewise the investigators will monitor pts under and after therapy. In addition, the investigators will compare teh breath signature to other biomarkers of lung cancer, like circulating tumor cells and others. |
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| Detailed Description | Scientific background: Lung cancer is the most lethal cancer, responsible for 28% of cancer deaths and killing ~1.3 million people worldwide every year. Diagnosis and treatment of lung cancer in its early stages could increase the 5-year-survival rate 3-4 fold with a potential for cure4, 7. Therefore, the main goal of this study is early detection of lung cancer, and specifically focusing on the volatile biomarkers of lung cancer that will assist in easy, inexpensive diagnosis based on our previous findings. Currently available diagnostic tests of lung cancer are not suitable for screening, are extremely costly and involve invasive procedures (e.g. bronchoscopy), that are not free of complications. The goal of cancer screening is to detect tumors at an early stage in order to give treatment a better chance of success. Recently, the biggest lung cancer screening trial (NLST) has shown a mortality benefit of 21% per 5 years study favor low dose CT screening protocol compare to chest X-rays1. Therefore, there is an urgent requirement for a tool to allow a better definition of the high-risk cohort. Such a tool might be a panel of biomarkers. Focusing on the volatile biomarkers of lung cancer, our group has recently defined a volatile VOCs signature that can distinguish the breath of lung cancer patients from the breath of healthy individuals and from cancerous cells 2, 9. These significant findings have led us to the understanding that volatile biomarkers would fit for early detection of lung cancer and for discrimination between subtypes of lung cancer. Such discrimination will have significant implications on clinical decisions and on patients' benefits. Exhaled Breath Analysis as a Diagnostic Tool and Preliminary results: Analysis of volatile organic compounds (VOCs) is a new attractive non-invasive field in medical diagnostics. The principle behind this approach is based on the fact that cancers cells are distinguished from normal cell in their metabolism rate, cell apoptosis pathways and protein expression patterns and thus emit and or consume various VOCs. These VOCs can be detected either directly from the headspace of the cancer cells or via the exhaled breath. Together with Prof Haick group at the Technion Inst (Israel), our data shows that there is a relation between the VOCs patterns of NSCLC and control cell lines and equivalent states in exhaled breath. We demonstrated that there is a clear discrimination between the lung cancer and the healthy clusters. We also analyzed the headspace of NSCLC and SCLC cell lines and we could discriminate significantly between SCLC versus NSCLC based on their VOCs patterns. This analysis allowed us to identify the specific VOCs consumed or omitted by cancerous cells. This finding has clinical applications since SCLC is distinguished from NSCLC by its sensitivity to chemotherapy and radiotherapy and other characteristics. Therefore, a non-invasive and highly sensitive test would be extremely valuable for the classification and early screening of lung cancer and for targeted therapy. Breath Collection and the Artificial NOSE In a typical collection, after normal exhalation, the subject will breath through a mouthpiece a filtered air to remove all VOCs of any ambient contaminants. Individuals will exhale in a constant flow rate. The exhaled air will be contained through the mouthpiece by Mayler bags and/or will be passed through a container. The collected air breath samples will be analysed for VOC by gas-chromatography mass spectrometry (GC-MS), highly-sensitive nano-sensors (Nanomaterial-Based Devices, Technion - Israel Institute of Technology, Haifa, Israel) or as online mass spectrometry (Ionimed, Austria). Further, the signals will be analyzed by a pattern recognition algorithms, such as principal component analysis (PCA), supported vector machines (SVM), or neuronal network analysis can then be applied on the entire set of signals to acquire information on the identity, properties and chemical composition of the vapor exposed to the sensors array 5, 10. Research Objectives: Our goal is to isolate and define a volatile signature, which allows discrimination between lung cancer from a normal state. That will potential serve as a unique biomarker for lung cancer. Our Objectives are:
Study Population On the clinical setup, we will sample three populations: Group A - patients with lung cancer (NSCLC and SCLC), any stage; this group will be divided later as for:
Group C - age and co-morbidity matched controls without proof of cancer/pre-cancer. All collections will follow local IRB guidelines. Information will be collected from all subjects, including epidemiologic data, histologic characteristics, tumor's metabolic activity (SUV avidity through PET scan). The clinical information will include health status, lung cancer subtype, pathology sub-classification and differentiation, advanced analysis and staining if available, imaging results (including CT, PET scan and its SUV avidity), location of the cancer, total volume of the tumor, stage of disease, genetic classification of the tumor and epidemiological data, e.g. age, gender, smoking and family history, family history, respiratory disease, exposure to asbestos etc. If cancer, the selection for therapy are as per the standards of care and the routine established care provided by the staff at the local institute. This protocol is not intended to interfere with or dictate this process. Examination procedure The total duration of the study for each subject takes 10-20 minutes while subjects will stay on followup for up to 3 years. The study will continue for 5 years. Newly diagnosed patients with non small cell lung cancer: Breath tests:
Follow up phase: Every three months to coincide with patients regular follow up their treating physicians. As per the standards of care, at this point every patient will be monitored with a CT scan of chest and abdomen at regular intervals. This CT will be used for determination of disease recurrence or to document remission. Correlative studies
Collection of the Breath Samples In a typical collection, after normal exhalation, the subject will breath through a mouthpiece a filtered air to remove all VOCs of any ambient contaminants. Individuals will exhale in a constant flow rate. The exhaled air will be contained through the mouthpiece by Mayler bags and/or will be passed through a container. The collected air breath samples will be analysed for VOC by gas-chromatography mass spectrometry (GC-MS), highly-sensitive nano-sensors (Nanomaterial-Based Devices, Technion - Israel Institute of Technology, Haifa, Israel) or as online mass spectrometry (Ionimed, Austria). Further, the signals will be analyzed by a pattern recognition algorithms, such as principal component analysis (PCA), supported vector machines (SVM), or neuronal network analysis can then be applied on the entire set of signals to acquire information on the identity, properties and chemical composition of the vapor exposed to the sensors array 5, 10. |
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| Study Type ICMJE | Observational | ||||
| Study Design ICMJE | Observational Model: Case Control Time Perspective: Prospective |
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| Target Follow-Up Duration | Not Provided | ||||
| Biospecimen | Retention: Samples Without DNA Description: exhaled breath and CTC and biomarkers |
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| Sampling Method | Non-Probability Sample | ||||
| Study Population | Lung cancer patients vs. post therapy vs. COPD controls |
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| Condition ICMJE | Lung Cancer | ||||
| Intervention ICMJE | Not Provided | ||||
| Study Group/Cohort (s) |
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| Publications * | Not Provided | ||||
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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| Recruitment Information | |||||
| Recruitment Status ICMJE | Recruiting | ||||
| Estimated Enrollment ICMJE | 300 | ||||
| Estimated Completion Date | May 2018 | ||||
| Estimated Primary Completion Date | May 2016 (final data collection date for primary outcome measure) | ||||
| Eligibility Criteria ICMJE | Inclusion Criteria
Exclusion Criteria
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| Gender | Both | ||||
| Ages | 18 Years to 95 Years | ||||
| Accepts Healthy Volunteers | No | ||||
| Contacts ICMJE |
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| Location Countries ICMJE | Israel | ||||
| Administrative Information | |||||
| NCT Number ICMJE | NCT01386203 | ||||
| Other Study ID Numbers ICMJE | SHEBA-11-8663-NP-CTIL | ||||
| Has Data Monitoring Committee | No | ||||
| Responsible Party | Sheba Medical Center | ||||
| Study Sponsor ICMJE | Sheba Medical Center | ||||
| Collaborators ICMJE | Not Provided | ||||
| Investigators ICMJE |
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| Information Provided By | Sheba Medical Center | ||||
| Verification Date | February 2012 | ||||
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ICMJE Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP |
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