Development of an autoantibody panel for early detection of lung cancer in the Chinese population

Abstract

Introduction: Tumor-associated autoantibodies have been revealed as promising biomarkers for the early detection of lung cancer. This study was designed to develop an autoantibody panel for early detection of lung cancer in the Chinese population.

Methods: Recruited prospectively in three clinical centers, the subjects (n = 991) who had a definite diagnosis during follow-up were included in the development of the autoantibody panel. The levels of 14 autoantibody candidates in plasma were detected.

Results: A panel of nine autoantibody markers (named as CN9), namely, P53, SOX2, SSX1, HuD, NY-ESO-1, CAGE, MAGE-A4, P62, and CK20, was preferably selected from 14 candidates. The overall specificity, sensitivity, and AUC were 90.5%, 40.8%, and 0.64, respectively. The CN9 panel demonstrated a reasonable detection rate in lung cancer patients at all stages, histological types, sizes of lesions, and risk levels. Its estimated overall accuracy is 85.5% and 90%, with PPV at 0.32 and 0.04, and NPV at 0.93 and 0.99 in the scenario of pulmonary nodules' characterizing and lung cancer screening, respectively. Two risk models were developed within the subgroups of malignant and benign pulmonary nodules in this study. By adding the CN9 result to the Mayo model indicators, it achieved a sensitivity of 41.3% and an AUC of 0.74 at a specificity of 91.3%. By adding the CN9 result to the Brock model indicators, it achieved a sensitivity of 47.7% and an AUC of 0.78 at a specificity of 91.3%. Both were improved compared with either the standalone Mayo or Brock model.

Discussion: This multi-center prospective study indicates a panel of nine autoantibody markers that can help in the detection of lung cancer and the classification of pulmonary nodules in the Chinese population.

Keywords: Chinese population; autoantibody; early detection; lung cancer; pulmonary nodule.

Figure 1

Schematic of patient inclusion.

Figure 2

Overview of the strategy used for the development and validation of an autoantibody panel to identify lung cancer.

Figure 3

ROC curves were constructed from multivariate data from the training (A), validation set (B), and overall case-control cohort (C).

Figure 4

Clinical performance of CN9 in subgroups. (A–D) The sensitivities in subgroups of different stages, histological types, lesion sizes, and risk factors. (E) The specificities in healthy controls (C-HC), benign nodules (C-BE), as well as other benign pulmonary diseases (C-IN). (F, G) The specificities in subgroups of different lesion sizes and risk factors.

Figure 5

Spearman's rank correlation coefficient between individual autoantibodies of the CN9 panel.

Figure 6

ROC curves of the prediction models of the Mayo Clinic and the newly developed Mayo-CN9 model (A), the Brock and the newly developed Brock-CN9 model (B) in the pulmonary nodule subgroup.