Autoantibody-based biomarkers to aid in the early diagnosis of lung cancer

With the widespread availability of computed tomography (CT), an estimated 20 million chest CT-scans are now performed annually in the United States. From these studies about 4 million individuals have pulmonary nodules detected that require further evaluation. Only about 1-in-37 of these nodules will be lung cancer, even among high-risk individuals. Clinicians are charged with rapidly identifying the individuals with malignancy while minimizing the number of unnecessary testing to those without. The objective for the proposed studies is to develop a non-invasive test with excellent performance characteristics across the spectrum of clinical presentations of indeterminate pulmonary nodules would reduce the number of unnecessary invasive procedures and minimize the time from detection to diagnosis. Our hypothesis contends that, in the case of early-stage lung cancer, tumors possess a unique molecular phenotype that is discernable from highly-inflammatory, non-malignant/benign lesions commonly detected by CT-based imaging studies. A portion of these molecules are tumor-shed and provoke an immune response that can be exploited for diagnostic purposes. Our approach to address this matter is to use immunoproteomic methods to identify candidate biomarkers from clinical specimens that are useful for discerning non-neoplastic/benign nodules from malignacy. Next, we will develop and validate Luminex assays for no less than 20 candidate biomarkers and evaluate these internally to identify the optimal combination of biomarkers to form a diagnostic algorithm for this purpose. Finally, algorithm validation against external patient cohorts (in our possession) will be performed to evaluate the potential for future clinical application.