Metabolomx' colorimetric sensor array can identify subtype of lung cancer

Metabolomx, a diagnostic company focused on the detection of the metabolomics signature of cancer from exhaled breath, today announces publication of results from the first clinical study demonstrating a breath test that can both detect lung cancer and differentiate between types of lung cancer in humans. This seminal study, conducted at the Cleveland Clinic and led by Dr. Peter Mazzone, used Metabolomx' first-generation colorimetric sensor array, and reported accuracy exceeding 80% in lung cancer detection, comparable to computerized tomography (CT) scan. Further, the study found that Metabolomx' first-generation colorimetric sensor array could identify the subtype of lung cancer (small cell versus adenocarcinoma versus squamous cell) with accuracy approaching 90%.

The availability of a low-cost, non-invasive metabolomic breath signature for lung cancer is particularly timely given the recently announced results of the National Cancer Institute's National Lung Screening Trial (NLST) calling for wider CT screening of high-risk citizens. The breath signature, which reports active tumor metabolism, is thought to provide complementary information to CT, potentially helping clinicians distinguish benign from malignant lung nodules.

The sensor detects the unique pattern of volatile organic compounds (the "metabolic biosignature") present in exhaled breath. The article, "Exhaled Breath Analysis with a Colorimetric Sensor Array for the Identification and Characterization of Lung Cancer," is appearing in the current, online issue of the Journal of Thoracic Oncology (JTO), the official Journal of the International Association for the Study of Lung Cancer.

James R. Jett, MD, Pulmonary Medicine and Medical Oncology, National Jewish Health and Editor-in-Chief of the Journal of Thoracic Oncology, stated, "The JTO is dedicated to publishing the best in clinical research that may one day make a real difference in the care of patients. The currently reported results, should they be confirmed in additional clinical testing, provide a provocative challenge for us to look more closely at 'biosignatures,' as described in the journal article, that may be complementary to detection modalities such as CT scan. The advent of the NLST results demonstrating the value of CT heightens the need for non-invasive, low-cost companion diagnostics, and a metabolomic breath test, if born out in continuing studies, is a candidate to play that role in early lung cancer detection."

"The Cleveland Clinic results just published by the Journal of Thoracic Oncology, the reference journal for lung cancer, demonstrate the broad potential for use of breath analysis in the early detection of lung cancer," commented Paul Rhodes, PhD, Founder and CEO of Metabolomx. "These results show that the first generation of our breath test technology compares well with CT scans. Detection of the metabolomic signature of lung cancer in exhaled breath is non-invasive, rapid, and inexpensive, and will become a valuable adjunct to help assess an indeterminate CT, and may come to have a central role in early detection and differentiation of lung cancer, while lowering costs to the healthcare system."

The article was authored by Peter J. Mazzone, MD, MPH, Cleveland Clinic; Xiao-Feng Wang, PhD, Cleveland Clinic; Yaomin Xu, PhD, Cleveland Clinic; Tarek Mekhail, MD, MSc, Florida Hospital; Mary C. Beukemann, Cleveland Clinic; Jie Na, MS, Cleveland Clinic; Jonathan W. Kemling, PhD, University of Illinois, Chicago; Kenneth S. Suslick, PhD, University of Illinois, Chicago; and Madhu Sasidhar, MD, Cleveland Clinic. Initial results from this study were presented at the American College of Chest Physicians conference in November 2010.

Dr. Mazzone commented, "Our research shows that breath testing may help identify patients with lung cancer, as well as provide us with information that can help with treatment decisions, such as the type of lung cancer, its stage, and prognosis. The accuracy of these non-invasive tests can be further augmented when combined with existing clinical predictors, such as health status and age."