Exhaled Breath Biomarkers in Lung Cancer

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:

• To test the feasibility for detecting early stage lung cancer via analysis of exhaled breath irrespective of the sub-histology.
• To test the feasibility of the breath biomarkers for monitoring the response to lung cancer treatment (surgery, chemotherapy or other cancer treatments)
• To compare sensitivity and selectivity of the breath analysis to conventional cancer markers and/or diagnostics (CT, PET, circulating tumor cells, blood markers etc.)
• Correlation of histology and/or other clinical measures to the breath signature.

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:

1. SCLC (before and after therapy).
2. NSCLC:
3. Surgically treated (Before and after therapy 3 years follow up).
4. Advanced disease (Before and after therapy, 3 years follow up). Group B - high risk patients who are undergoing investigation related to Lung cancer or pulmonary nodule.

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:

1. Two tests immediately prior to any therapy (surgery/other; One week apart to test reproducibility).
2. If was operated for cure, then at 3,6,12,18,24,36 months later.
3. If was radiated, then at mid \& end of radiation and at 3,6,12,18,24,36 months.
4. If chemotherapy, then every 3 months, between cycles.

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

1. Sputum Cytology (Induced Sputum will be collected for cytologic examination).
2. Blood samples will be taken for Circulating tumor cells analysis and other systemic markers.

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.