Hybrid Glycoproteomic and Autoantibody Biomarkers for Lung Cancer Early Detection

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Project Summary Lung cancer is the leading cause of cancer deaths worldwide. Typically, lung cancer is diagnosed at a late stage of disease since inexpensive, non-invasive and accurate screening methods are not available. Low-dose CT screening in high risk smokers reduces lung cancer mortality by ~20% by identifying small lung nodules, but at a 95% false positive rate. For lung cancer screening to be effective, the sensitivity and specificity to detect lung cancer must be improved. We have created a hybrid blood biomarker platform that identifies proteomic, glycomic and autoantibody changes with high sensitivity and specificity in multiple cancer types, including lung. Here, I propose to use our novel platform to identify and validate hybrid biomarkers to detect lung cancer early either as a pre-screen or after CT to guide follow-up treatment (e.g., bronchoscopy, continued CT surveillance or surgical biopsy). I have already identified and validated proteomic markers that show utility in lung cancer early detection in two pre-diagnostic plasma sample sets. These proteomic markers will be further analyzed for glycomic and autoantibody changes (Aim 1). Additionally, hybrid plasma biomarkers will be examined in diagnostic plasma samples from our Fred Hutchinson Lung Cancer Early Detection Clinic (Aim 2). In a multi-disciplinary collaboration, my identified biomarkers will be integrated with pulmonary nodule features to create a risk prediction model that distinguishes malignant from benign nodules identified on CT imaging. I will also delve further into biomarker biology by exploring the mechanism of intracellular glyco- protein export for our best peripheral biomarkers (Aim 3).