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A. Onn
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MINI 12 - Biomarkers and Lung Nodule Management (ID 109)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:J.M. Siegfried, H.I. Pass
- Coordinates: 9/07/2015, 16:45 - 18:15, 401-404
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MINI12.07 - Exhaled Breath Analysis in Lung Cancer - One Stop Shop for Diagnosis, Staging and EGFR Analysis (ID 2431)
17:20 - 17:25 | Author(s): A. Onn
- Abstract
- Presentation
Background:
Lung cancer (LC) is the leading cause of cancer death in the United States with more than 158,000 estimated deaths in 2015. Early detection of LC has been well established as a significant key point in patients' survival and prognosis, yet unfortunately, the vast majority of new LC patients are being diagnosed at advanced disease stages. Exhaled breath analysis can serve as a non-invasive method in early detection of LC. The tumor's micro-environment releases various compounds to blood, some of which are then exhaled at breath as Volatile Organic Compounds (VOCs). This study evaluates the potential of exhaled breath analysis in LC detection and to further diagnose histology, EGFR mutational status and to discriminate early from advanced disease in a multinational study.
Methods:
Breath samples were taken from untreated LC patients and matching controls. Patients were enrolled in a large tertiary referral hospital in Israel. Analysis was performed by gold nanoparticle-based Artificial Olfactory System (NaNose®) and Pattern recognition methods were used to analyze the results obtained from the NaNose®. Histology, EGFR mutation status and staging was taken from patient's files.
Results:
A total of 174 patients participated in this study, and Inter-group analysis of 80 LC patients (64 advanced stage) and 31 matched controls showed a significant discrimination between disease and control. Among all patients, 83 were adenocarcinoma and 11 were squamous. EGFR mutations were detected in 24 patients. The comparisons resulted in: early LC versus control: p < 0.0001; accuracy 85.11%, advanced LC versus control: p < 0.0001; accuracy 82.11%, early LC versus advanced LC: p < 0.0001; accuracy 78.75%. Histology (Adenocarcinoma vs. Squamous cell carcinoma) and EGFR status was also significantly determined by the volatile signature.
Conclusion:
Breath analysis may support early detection of cancer as well as histological diagnoses, staging and mutational testing in lung cancer. This innovative method may pose as an important non-invasive tool for lung cancer early detection, thus promoting better prognosis and therapeutic possibilities for patients.
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MINI 15 - Chemotherapy Developments for Lung Cancer (ID 128)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Treatment of Advanced Diseases - NSCLC
- Presentations: 1
- Moderators:L. Crinò, C.P. Belani
- Coordinates: 9/08/2015, 16:45 - 18:15, Mile High Ballroom 1a-1f
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MINI15.14 - The Role of Breath Sampling in Monitoring Response to Treatment in Lung Cancer (ID 2551)
18:00 - 18:05 | Author(s): A. Onn
- Abstract
- Presentation
Background:
The current available method to monitor response to treatment in lung cancer patient is by Computerized Tomography (CT) scans. However, time intervals between consecutive CT scans might be too long to allow early identification of treatment failure. The aim of this study is to examine the use of breath sampling as a tool for monitoring response to anti-cancerous treatment in patients with advanced lung cancer.
Methods:
In a prospective study, repeated exhaled breath samples were collected from patients with advanced lung cancer before and under systemic therapy. VOCs[1] profiles were determined by GC-MS[2] and nanomaterial-based array of sensors and correlated with response to therapy, assessed by CT scans as Complete Response (CR), Partial Response (PR), Stable Disease (SD), or Progressive Disease (PD). [1] Volatile Organic Compounds [2] gas-chromatography/mass-spectrometry
Results:
One hundred forty three breath samples were collected from 39 patients with stage III/IV lung cancer. GC-MS anaylsis identified 3 VOCs as significantly indicating PR/SD samples. One of them was also significantly discriminated between PR/SD and PD. Further, the NA-NOSE signals were able to alarm per a change in tumor response across therapy, i.e. indicating lack of further response to therapy, or developement of resistance to therapy. PR/SD was detected in a sensitivity of 93%, specificity of 85% and accuracy of 89% and ppositive/negative predictive values (PPV; NPV) of 86% and 92% respectively. PD was detected with 100% specificity and 92% accuracy, but the sensitivity was only 28%. The PPV and NPV were 100% and 91%, respectively. The achieved results indicate high reliability in predicting a progression of the disease and detecting patient's lack of response to treatment (i.e., PD).
Conclusion:
Breath analysis may serve as a serogate marker for response to systemic therapy in lung cancer. Such a monitoring tool can provide the oncologist with a quick and simple method to identify patient's response to anti-cancerous treatment in shorter intervals than currently available by CT scans.
Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
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P2.06 - Poster Session/ Screening and Early Detection (ID 219)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.06-009 - Oral Glucose Tolerance Test as a Diagnostic Tool in Lung Cancer (ID 1040)
09:30 - 09:30 | Author(s): A. Onn
- Abstract
Background:
Previous studies have demonstrated that volatile organic compounds (VOCs) in exhaled breath can distinguish between healthy and affected individuals, and can even discern between SCLC and NSCLC and within the subtypes of lung cancer (LC) and its mutations status. The current study assessed the differences in glucose metabolism on the volatile signature in LC through an oral glucose tolerance test (OGTT).
Methods:
This cohort included forty participants (22 control participants whom are at high risk for LC, 18 study participants whom have active, naïve lung cancer). Pre-OGTT and Post-OGTT blood glucose levels and exhaled breath samples were measured with a lay period of 90 minutes. A proton transfer reaction mass spectrometer (PTR MS) detected and measured the VOCs. The data was then analyzed using a series of feature selection methods to identify relevant inputs for multilayer perceptron (MLP) models to distinguish LC patients from controls, with and without the consideration of the glucose effect.
Results:
The feature selection method “infogain” revealed a combination of 14 masses (m/e) that were different between the two groups without considering the glucose effect. All the average values of these masses were higher in the LC group except for m/e 52, which was higher in the high-risk group. These 14 masses enable us to distinguish between the two groups with an average accuracy of 91.67% for three internal validation tests of a MLP (threshold set at 0.45). The analysis of the effect of glucose revealed that several m/e increased more for the control group whereas others increased more for the LC group. Moreover, three feature selections, each with a different combination of 4 masses, allowed the design of three MLPs that yielded 90% for K-fold cross-validation accuracy. Figure 1
Conclusion:
This study showed that breath analysis could discriminate between the high-risk and LC group. Furthermore, it demonstrated that glucose metabolism leaves a unique VOC pattern in the LC group. These findings may assist in the development of a non-invasive screening method for lung cancer.
