Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Prognosis and survival are dependent on cell type, early detection, and surgical treatment. Hence, optimal screening strategies and new therapies are urgently required. Although surveillance with low-dose computed tomography can reduce lung cancer mortality by 20%, the number of false-positive detections is significant. Tissue diagnosis aids in the identification of benign nodules, reducing the number of false positive detections. To determine whether molecular testing of fine-needle aspirations (FNAs) can reduce false-positive detections, we developed a gene expression-based test that distinguishes normal from cancerous lung tissues. The test first was applied to published microarray data, showing overall sensitivity and specificity values of 95% (95% CI, 90%-98%) and 100% (95% CI, 40%-100%), respectively. Subsequently, it was validated on 30 solid and ex vivo FNA lung cancer tumor samples and matched normal lung specimens using real-time PCR. The validation test was 93% (95% CI, 78%-99%) sensitive and 100% (95% CI, 88%-100%) specific for the detection of tumor versus normal lung on solid samples, whereas FNA specimens yielded a sensitivity of 91% (95% CI, 72%-99%) and a specificity of 94% (95% CI, 70%-100%). This study supports the hypothesis that the gene-ratio approach reliably distinguishes normal lung from cancerous tissues in FNA samples and can be optimized to diagnose benign nodules.