In the United States alone, one of four cancer deaths occur from lung cancer and there are over 8 million individuals considered to be at high-risk due to cigarette smoking and other behaviors. It's well known that early detection of cancer significantly improves survival of this disease, however a lack of lung cancer screenings and analysis precludes fast results at a low cost.
Low-dose computer tomography (LD-CT) is the current standard and provides indication of potentially cancerous nodules above a certain size, however it suffers from low sensitivity and specificity (55 and 81%, respectively), can miss occult cancer (10% false negative rate), or incorrectly diagnose cancer when it is not there, with up to 90% false positive results. False negatives can lead to detection only at later-stages when treatment options are limited, while false positives can lead to additional, expensive testing, invasive biopsies, and patient anxiety. In addition, the compliance rate for LD-CT screening is less than 5% of the eligible population due to access to infrastructure and other chalenges.
Scientists in the NCI’s Laboratory of Human Carcinogenesis have proven a unique, non-invasive screening tool and diagnostic that detects lung cancer at an early stage utilizing liquid chromatography-mass spectrometry of urine samples. Urine samples minimize patient discomfort, unlike current early detection methods that are invasive, such as a blood or tissue biopsy or bronchoscopy, could be done easily during a routine exam, and could supplement existing LD-CT that cannot detect such early-stage nodules.
The NCI scientists validated a unique metabolite profile by profiling of urine samples obtained from smokers and non-smokers in three independent cohorts.The four componenets of the profile have shown high correlation to lung cancer (p < 0.00001). Patient data indicate the methdology can also provide prognostic data on patient survival and have led to an understanding of the mechanism of action that creates the metabolic profile.
The NCI seeks research collaborations to discover additional aspects of this methdology in patient samples, or licensing to commercialize the technology through CLIA or LDT.