Young Ju Jung1, Evaldas Katilius2, Rachel M Ostroff2, Youndong Kim3, Minkyoung Seok3, Sujin Lee3, Seongsoo Jang4, Woo Sung Kim5, Chang-Min Choi6. 1. Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; Health Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. 2. SomaLogic, Boulder, CO. 3. Aptamer Sciences Inc., Pohang, Korea. 4. Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. 5. Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. 6. Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. Electronic address: ccm9607@gmail.com.
Abstract
BACKGROUND: Lung cancer screening using low-dose computed tomography reduces lung cancer mortality. However, the high false-positive rate, cost, and potential harms highlight the need for complementary biomarkers. We compared the diagnostic performance of modified aptamer-based protein biomarkers with Cyfra 21-1. PATIENTS AND METHODS: Participants included 100 patients diagnosed with lung cancer, and 100 control subjects from Asan Medical Center (Seoul, Korea). We investigated candidate biomarkers with new modified aptamer-based proteomic technology and developed a 7-protein panel that discriminates lung cancer from controls. A naive Bayesian classifier was trained using sera from 75 lung cancers and 75 controls. An independent set of 25 cases and 25 controls was used to verify performance of this classifier. The panel results were compared with Cyfra 21-1 to evaluate the diagnostic accuracy for lung nodules detected by computed tomography. RESULTS: We derived a 7-protein biomarker classifier from the initial train set comprising: EGFR1, MMP7, CA6, KIT, CRP, C9, and SERPINA3. This classifier distinguished lung cancer cases from controls with an area under the curve (AUC) of 0.82 in the train set and an AUC of 0.77 in the verification set. The 7-marker naive Bayesian classifier resulted in 91.7% specificity with 75.0% sensitivity for the subset of individuals with lung nodules. The AUC of the classifier for lung nodules was 0.88, whereas Cyfra 21-1 had an AUC of 0.72. CONCLUSION: We have developed a protein biomarker panel to identify lung cancers from controls with a high accuracy. This integrated noninvasive approach to the evaluation of lung nodules deserves further prospective validation among larger cohorts of patients with lung nodules in screening strategy.
BACKGROUND:Lung cancer screening using low-dose computed tomography reduces lung cancer mortality. However, the high false-positive rate, cost, and potential harms highlight the need for complementary biomarkers. We compared the diagnostic performance of modified aptamer-based protein biomarkers with Cyfra 21-1. PATIENTS AND METHODS: Participants included 100 patients diagnosed with lung cancer, and 100 control subjects from Asan Medical Center (Seoul, Korea). We investigated candidate biomarkers with new modified aptamer-based proteomic technology and developed a 7-protein panel that discriminates lung cancer from controls. A naive Bayesian classifier was trained using sera from 75 lung cancers and 75 controls. An independent set of 25 cases and 25 controls was used to verify performance of this classifier. The panel results were compared with Cyfra 21-1 to evaluate the diagnostic accuracy for lung nodules detected by computed tomography. RESULTS: We derived a 7-protein biomarker classifier from the initial train set comprising: EGFR1, MMP7, CA6, KIT, CRP, C9, and SERPINA3. This classifier distinguished lung cancer cases from controls with an area under the curve (AUC) of 0.82 in the train set and an AUC of 0.77 in the verification set. The 7-marker naive Bayesian classifier resulted in 91.7% specificity with 75.0% sensitivity for the subset of individuals with lung nodules. The AUC of the classifier for lung nodules was 0.88, whereas Cyfra 21-1 had an AUC of 0.72. CONCLUSION: We have developed a protein biomarker panel to identify lung cancers from controls with a high accuracy. This integrated noninvasive approach to the evaluation of lung nodules deserves further prospective validation among larger cohorts of patients with lung nodules in screening strategy.
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