ObjectiveSputum is a source of exfoliated respiratory epithelial cells transformed early in lung carcinogenesis. Malignant cells are hypomethylated and contain less genomic 5-methylcytosine (5mC). Validating a test that recognizes and quantifies aberrantly hypomethylated cells in sputum, we assessed its potential as a screening tool for detecting early-stage non–small cell lung cancer. MethodsCells extracted from sputum were immunofluorescence labeled with an anti-5-methylcytosine antibody and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) delineating global nuclear DNA (gDNA). Via confocal scanning and 3-dimensional image analysis, fluorescence 5mC and DAPI signals were measured in segmented cell nuclei, and a 5mC/DAPI co-distribution map was generated for each imaged cell. Cells were classified as hypomethylated based on 5mC load and 5mC/DAPI co-distribution. The proportion of hypomethylated epithelial cells in the sputum determines whether a patient has lung cancer. ResultsA total of 88 subjects were enrolled: 12 healthy subjects; 34 high-risk subjects with benign chronic lung disorders (10 with chronic obstructive pulmonary disease, 24 with idiopathic pulmonary fibrosis), and 43 subjects with non–small cell lung cancer (27 with stage I-II and 16 with stage III-IV). The test identified early-stage non–small cell lung cancer and distinguished it from the high-risk group with 95.8% (95% confidence interval, 78.9-99.9) sensitivity and 41.2% (95% confidence interval, 24.6-59.3) specificity applying only 5mC, 95.8% (95% confidence interval, 78.9-99.9) sensitivity and 26.5% (95% confidence interval, 12.9-44.4) specificity using solely 5mC/DAPI index, and 100% (95% confidence interval, 98.7-100) sensitivity and 26.1% (95% confidence interval, 26.2-27.8) specificity with the combined parameters. ConclusionsWe tested and validated a novel, noninvasive, highly sensitive screening test for non–small cell lung cancer. With the use of sputum, our test may impact lung cancer screening, evaluation of pulmonary nodules, and cancer surveillance algorithms.