Lung cancer is the leading cause of cancer death in the United States and world-wide. 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 potential benefits of lung cancer early detection are clear - when discovered at an early stage, lung cancer has 5 year survival rates over 50% while only 3.7% of metastatic lung cancer patients survive that long. To date, randomized controlled trials of most screening modalities such as chest x-ray and sputum cytology have not demonstrated any impact on lung cancer mortality. Also, no proteomic or genomic plasma markers have advanced sufficiently in validation trials to be viable FDA-approved candidates for widespread screening. Discovery of viable lung cancer early detection proteomic, glycomic and/or immunological biomarkers in blood that would work well on their own or in combination with low-dose chest computed tomography (CT) would be especially valuable since lung cancer is common, most often fatal and is expensive to treat - so it has high societal costs. We have taken a unique approach to biomarker discovery designed to overcome current obstacles. We created a high density antibody array containing 3200 different antibodies that we use to interrogate pre-diagnostic sample sets from multiple observational trials (i.e., hundreds of pre-diagnostic samples with well-matched cases and controls) in a nested case-control design study to evaluate proteomic, glycomic and autoantibody differences. Since our discovery method uses high affinity antibodies, our best performing markers can be readily transferred to ELISA-like methods facilitating formal validation and movement into the clinic. We have shown the technology is highly sensitive (low picogram levels) and reproducible (most coefficients of variation <10%). 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 diagnosed with lung cancer. Here, we propose to use samples from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to validate these candidates.
Our specific aims are: (1) To use PLCO samples to preliminarily validate 30 potential lung cancer early detection proteomic, glycomic or autoantibody biomarker candidates (e.g., those with >2 x increases, p<0.002, AUC >0.67). (2) To develop and assess the performance of a novel risk prediction model integrating the biomarkers assessed in Aim 1 with a variety of clinical and epidemiologic factors, including lung cancer subtype, smoking history, age, gender, body mass index, family history of lung and other cancers, radiation, asbestos and other exposures, that should potentially impact biomarker performance and risk prediction.
We propose to test the performance of our putative proteomic, glycomic and autoantibody biomarkers to predict subsequent onset of lung cancer using independent samples collected as part of the Prostate, Lung, Colon, Ovarian (PLCO) cohort. Biomarkers that pass this and subsequent validation steps 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 :|