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. 2017 Oct 16;7(2):e1384108.
doi: 10.1080/2162402X.2017.1384108. eCollection 2018.

Early detection of lung cancer by using an autoantibody panel in Chinese population

Affiliations

Early detection of lung cancer by using an autoantibody panel in Chinese population

Shengxiang Ren et al. Oncoimmunology. .

Abstract

We have previously identified a panel of autoantibodies (AABs), including p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2 and GBU4-5, that was helpful in the early diagnosis of lung cancer. This large-scale, multicenter study was undertaken to validate the clinical value of this 7-AABs panel for early detection of lung cancer in a Chinese population. Two independent sets of plasma samples from 2308 participants were available for the assay of AABs (training set = 300; validation set = 2008). The concentrations of AABs were quantitated by enzyme-linked immunosorbent assay (ELISA), and the optimal cutoff value for each AAB was determined in the training set and then applied in the validation set. The value of the 7-AABs panel for the early detection of lung cancer was assessed in 540 patients who presented with ground-glass nodules (GGNs) and/or solid nodules. In the validation set, the sensitivity and specificity of the 7-AABs panel were 61% and 90%, respectively. For stage I and stage II non-small cell lung cancer (NSCLC), the sensitivity of the 7-AABs panel was 62% and 59%, respectively, and for limited stage small cell lung cancer (SCLC) it was 59%; these sensitivity values were considerably higher than for traditional biomarkers (including CEA, NSE and CYFRA21-1). Importantly, the combination of the 7-AABs panel and low-dose computed tomography (CT) scanning significantly improved the diagnostic yield in patients presenting with GGNs and/or solid nodules. In conclusion, our 7-AABs panel has clinical value for early detection of lung cancer, including early-stage lung cancer presenting as GGNs.

Keywords: Autoantibody; Biomarker; Early detection; Lung cancer; Tumor-associated antigen.

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Figures

Figure 1.
Figure 1.
Patient flow chart for the cohorts in the training set (A) and the validation set (B).
Figure 2.
Figure 2.
Reactivity of each autoantibody (AAB) in the training set. (A) p53; (B) PGP9.5; (C) SOX2; (D) GAGE7; (E) GBU4-5; (F) MAGEA1; and (G) CAGE. (H) Combined area-under-the-curve (AUC) for the 7-AABs panel in lung cancer sera versus healthy controls.
Figure 3.
Figure 3.
Diagnostic performance of the 7 autoantibodies (AABs) panel in the validation set. (A) sensitivity (patients with lung cancer); (B) specificity (control groups); (C) Venn diagram for patients who received the 7-AABs panel and/or CT scanning; (D) positive predictive value (PPV) of the 7-AABs panel combined with CT scanning in lung cancer patients. AID, autoimmune disease; BLD, benign lung disease; HC, healthy controls; OC, other cancers.
Figure 4.
Figure 4.
Effectiveness of the 7 autoantibodies (AABs) panel in patients with radiological ground-glass nodules (GGNs) and/or nodules. (A) sub-analysis of PPV according to size; (B) sub-analysis of PPV according to pathological type; (C) sub-analysis of the false-positive rate according to size; (D) sub-analysis of the false-positive rate according to pathological type.

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