A novel detection method of non–small cell lung cancer using multiplexed bead-based serum biomarker profiling

• Published in: The Journal of thoracic and cardiovascular surgery Vol. 143; no. 2; pp. 421 - 427.e3
• Main Authors: Lee, Hyun Joo, MD, PhD, Kim, Young Tae, MD, PhD, Park, Pil Je, PhD, Shin, Yong Sung, MS, Kang, Kyung Nam, MS, Kim, Yongdai, PhD, Kim, Chul Woo, MD, PhD
• Format: Journal Article
• Language: English
• Published: New York, NY Mosby, Inc 01.02.2012; Elsevier
• Subjects: Aged; Algorithms; alpha 1-Antitrypsin - blood; alpha-Fetoproteins - analysis; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Antigens, Neoplasm - blood; Biological and medical sciences; Biomarkers, Tumor - blood; Carcinoma, Non-Small-Cell Lung - blood; Carcinoma, Non-Small-Cell Lung - diagnosis; Cardiology. Vascular system; Cardiothoracic Surgery; Case-Control Studies; Chemokine CCL5 - blood; Decision Support Techniques; Discriminant Analysis; Female; Flow Cytometry; Humans; Immunoassay; Insulin-Like Growth Factor I - analysis; Keratin-19 - blood; Linear Models; Logistic Models; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Male; Medical sciences; Middle Aged; Pneumology; Predictive Value of Tests; Prognosis; Reproducibility of Results; Republic of Korea; Support Vector Machine; Tumors of the respiratory system and mediastinum; Republic of Korea; alpha-1 antitrypsin; ADC; Apo; apolipoprotein; RANTES; LRM; regulated upon activation normal T cell expressed and secreted; 29; LDA; AFP; random forest; SVM; pro-Apo; insulin-like growth factor; linear discriminant analysis; logistic regression; A1AT; 10; IVDMIA; NSCLC; CYFRA; non–small cell lung cancer; adenocarcinoma; IGF; pro-apolipoprotein; alpha-fetoprotein; cytokeratin fragment; SCC; support vector machine; RF; in vitro diagnostic multivariate index assay; squamous cell carcinoma; Lung disease; Biological fluid; Respiratory disease; Biological marker; Malignant tumor; Method; non-small cell lung carcinoma; Medical screening; Bronchus disease; Anesthesia; Serum; Circulatory system; Technique; Cardiology; Detection; Cancer
• ISSN: 0022-5223; 1097-685X
• DOI: 10.1016/j.jtcvs.2011.10.046

Objectives Non–small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality. Development of an early diagnosis method may improve survivals. We aimed to develop a new diagnostic model for NSCLC using serum biomarkers. Methods We set up a patient group diagnosed with NSCLC (n = 122) and a healthy control group (n = 225). Thirty serum analytes were selected on the basis of previous studies and a literature search. An antibody-bead array of 30 markers was constructed using the Luminex bead array platform (Luminex Inc, Austin, Tex) and was analyzed. Each marker was ranked by importance using the random forest method and then selected. Using selected markers, multivariate classification algorithms were constructed and were validated by application to independent validation cohort of 21 NSCLC and 28 control subjects. Results There was no difference in demographics between patients and the control population except for age (64.8 ± 10.0 for patients vs 53.0 ± 7.6 years for the control group). Among the 30 serum proteins, 23 showed a difference between the 2 groups (12 increased and 11 decreased in the patient group). We found the highest accuracy of multivariate classification algorithms when using the 5 highest-ranked biomarkers (A1AT, CYFRA 21-1, IGF-1, RANTES, AFP). When we applied the algorithms on a validation cohort, each method recognized the patients from the controls with high accuracy (89.8% with random forest, 91.8% with support vector machine, 88.2% with linear discriminant analysis, and 90.5% with logistic regression). Conclusions We confirmed that a new diagnostic method using 5 serum biomarkers profiling constructed by multivariate classification algorithms could distinguish NSCLC from healthy controls with high accuracy.