Lung cancer is the leading cause of cancer death in the United States and worldwide. In 2013, it is estimated that there will be at least 228,000 new cases of lung cancer diagnosed and more than 159,000 deaths in the United States - approximately equal to the next four most common causes of cancer-related mortality combined (colon, breast, prostate, pancreas). The NCI-sponsored, National Lung Screening Trial (NLST) found a 20% reduction in lung cancer specific mortality in high-risk subjects screened with low-dose chest computed tomography (CT). However, 26% of CT-scans reported noncalcified nodules >4 mm while only 5% of these positive findings would actually be expected to be cancer. Analysis of several screening cohorts indicates that 25-50% of smokers >50 years of age have CT identifiable pulmonary nodules but very few of them (~2.5%) are caused by lung cancer. Current practice guidelines for pulmonary nodule evaluation call for invasive biopsy procedures depending upon the size and characteristics of the nodule and key clinical parameters (e.g., age, smoking history), raising considerable cost/benefit and morbidity/mortality considerations even in this high-risk population. Clearly there is a need for additional risk stratification for subjects that have pulmonary nodules detected by CT imaging. Discovery of viable proteomic, glycomic and/or immunological biomarkers in blood to complement CT would be especially valuable to guide clinical care. However, no plasma markers have advanced sufficiently in validation trials to be viable FDA-approved candidates. We created a high density antibody array containing 3200 different antibodies that we use to interrogate pre-diagnostic sample sets from observational trials in a nested case-control design study to evaluate proteomic, glycomic and autoantibody differences. We have shown that this novel technology is highly sensitive and reproducible. Furthermore, we have confirmed known and found new viable proteomic biomarker candidates in ovarian, breast, colon and lung cancer. Using pre-diagnostic lung cancer samples from the Cardiovascular Health Study (CHS), we found 30 proteomic, glycomic or autoantibody biomarkers that were significantly increased (p<0.002) in people that are subsequently diagnosed with lung cancer. Here, we propose to use plasma samples from 297 lung nodule positive subjects that have been screened via CT and have known cancer/nodule status (147 were cancer) to test these 30 markers and potentially discover additional candidates. We will then combine these data with CT imaging parameters and clinical data to create a risk prediction model that we will test in a similar sized prospectivly collected cohort.
Our specific aims are: (1) Test the ability of putative proteomic and glycomic biomarkers to identify malignant pulmonary nodules. (2) Determine if autoantibodies present in plasma are tumor-derived and assess their utility for the detection of cancerous nodules. (3) Perform multivariate analyses of hybrid plasma biomarkers to distinguish malignant from benign nodules identified on CT chest imaging.
We propose to test the performance of our 30 putative proteomic, glycomic and autoantibody biomarkers to develop a 2-4 marker panel that when combined with results from computed tomography (CT) accurately predicts the presence of lung cancer to distinguish benign and cancerous nodules. Biomarkers that complement CT and that can be formally validated should significantly reduce lung cancer mortality and patient care costs.
| Lastwika, Kristin J; Kargl, Julia; Zhang, Yuzheng et al. (2018) Tumor-Derived Autoantibodies Identify Malignant Pulmonary Nodules. Am J Respir Crit Care Med : |
