Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China.
iCarbonX (Zhuhai) Company Limited, Zhuhai, China.
Cancer Epidemiol Biomarkers Prev. 2023 May 1;32(5):726-738. doi: 10.1158/1055-9965.EPI-22-0948.
Early diagnosis is critical to lung adenocarcinoma patients' survival but faces inadequacies in convenient early detection.
We applied a comprehensive microarray of 130,000 peptides to detect "autoantibody signature" that is autoantibodies binding to mimotopes for early detection of stage 0-I LUAD. Plasma samples were collected from 147 early-stage lung adenocarcinoma (Early-LUAD), 108 benign lung disease (BLD), and 122 normal healthy controls (NHC). Clinical characteristics, low-dose CT (LDCT), and laboratory tests were incorporated into correlation analysis.
We identified 143 and 133 autoantibody signatures, distinguishing Early-LUAD from NHC/BLD in the discovery cohort. Autoantibody signatures significantly correlated with age, stage, tumor size, basophil count, and IgM level (P < 0.05). The random forest models based on differential autoantibody signatures displayed AUC of 0.92 and 0.87 to discern Early-LUAD from NHC/BLD in the validation cohort, respectively. Compared with LDCT, combining autoantibody signature and LDCT improved the positive predictive value from 50% to 78.33% (P = 0.049). In addition, autoantibody signatures displayed higher sensitivity of 72.4% to 81.0% compared with the combinational tumor markers (cyfra21.1, NSE, SCC, ProGRP) with a sensitivity of 22.4% (P = 0.000). Proteins matched by differential peptides were enriched in cancer-related PI3K/Akt, MAPK, and Wnt pathways. Overlaps between matched epitopes and autoantibody signatures illustrated the underlying engagement of autoantibodies in immune recognition.
Collectively, autoantibody signatures identified by a high-throughput peptide microarray have the potential to detect Early-LUAD, which could assist LDCT to better diagnose Early-LUAD.
Novel sensitive autoantibody signatures can adjuvant LDCT to better diagnose LUAD at very early stage.
早期诊断对肺腺癌患者的生存至关重要，但在方便的早期检测方面存在不足。
我们应用了一个包含 130000 个肽的综合微阵列来检测“自身抗体特征”，即与模拟表位结合的自身抗体，以用于 0 期-I 期 LUAD 的早期检测。从 147 例早期肺腺癌（早期-LUAD）、108 例良性肺部疾病（BLD）和 122 例正常健康对照（NHC）中采集血浆样本。将临床特征、低剂量 CT（LDCT）和实验室检查纳入相关性分析。
我们在发现队列中鉴定了 143 和 133 个自身抗体特征，将早期-LUAD 与 NHC/BLD 区分开来。自身抗体特征与年龄、分期、肿瘤大小、嗜碱性粒细胞计数和 IgM 水平显著相关（P < 0.05）。基于差异自身抗体特征的随机森林模型在验证队列中分别将 AUC 提高到 0.92 和 0.87，以区分早期-LUAD 与 NHC/BLD。与 LDCT 相比，结合自身抗体特征和 LDCT 将阳性预测值从 50%提高到 78.33%（P = 0.049）。此外，与组合肿瘤标志物（cyfra21.1、NSE、SCC、ProGRP）的敏感性为 22.4%相比，自身抗体特征的敏感性更高，为 72.4%至 81.0%（P = 0.000）。差异肽匹配的蛋白质在癌症相关的 PI3K/Akt、MAPK 和 Wnt 途径中富集。匹配表位和自身抗体特征之间的重叠表明自身抗体在免疫识别中的潜在参与。
总之，高通量肽微阵列鉴定的自身抗体特征具有检测早期-LUAD 的潜力，这可以辅助 LDCT 更好地诊断早期-LUAD。
新型敏感的自身抗体特征可以辅助 LDCT 更好地诊断非常早期的 LUAD。
