多阶段筛选和鉴定肺癌相关抗原及其用于高危人群早期诊断和预警的评价

Lung cancer (LC) is the leading cause of cancer death worldwide and it often remain undiagnosed until the disease is symptomatic and has reached an advanced stage. The five-year survival rate is less than 20% due to the lack of detection method with high sensitivity as well as high specificity. Presently, there is nothing to offer for early diagnosis of lung cancer. Therefore, it is critical to explore and discover the sensitive and specific diagnostic biomarkers for the detection of early stage LC. The previous studies demonstrated that autoantibodies against tumor-associated antigens (TAAs) identified in patients with advanced LC might be detected in subjects with early LC or even predate the diagnosis. In the present study, based on our previous study, the combination of three immunomics approaches: genomics approach (Serological analysis of expression cDNA libraries, SEREX), proteomics approach (Serological Proteome Approach, SERPA) with data mining of bioinformatics will be used to screen the candidate LC associated TAAs which will be verified by using high-throughput approach protein array in sera from LC, lung disease and normal controls. In the following validation study, ELISA approach will be used to test the autoantibodies against verified TAAs in sera from LC patients and normal control individuals with large sample size, as well as in serial serum samples from 28 LC patients at early stage. We will further evaluate the diagnostic value of the validated TAAs by using Epidemiological approach based on individual TAA as well as combination of TAAs. The ultimate goal of this study is to develop a detection method of TAAs combination which may be used as an accurate and reliable tumor biomarker for lung cancer early diagnosis and early prediction in the high-risk population.

肺癌是全球发病率及死亡率第一的恶性肿瘤，由于缺乏灵敏特异的早期诊断方法，5年生存率不到20%。研究表明，血清中针对肿瘤相关抗原（TAAs）的自身抗体检测对高危人群的肺癌早期诊断具有广阔的应用前景。在前期研究基础上，本课题拟结合三种免疫组学技术经多阶段筛选和鉴定肺癌TAAs。首先，采用免疫基因组学技术（SEREX）、免疫蛋白组学技术（SERPA）两种实验技术和免疫组学生物信息挖掘技术，初步筛选可能的肺癌TAAs；进而利用高通量蛋白芯片技术检测肺癌、慢性肺病和正常人血清中TAAs自身抗体进行TAAs验证；随后利用定量ELISA技术检测大样本量肺癌和正常对照血清中自身抗体以进一步确认肺癌TAAs，最后对一批诊断前后肺癌系列血清及其对照血清进行检测。应用流行病学原理和技术评价TAAs的诊断价值并筛选最佳的TAAs组合，为高危人群的肺癌早期诊断和预警提供准确可靠的血清学肿瘤标志物检测新方法。

背景：肺癌是全球发病率及死亡率第一的恶性肿瘤，由于缺乏灵敏特异的早期诊断方法，5年生存率不到20%。研究表明，血清中针对肿瘤相关抗原（TAAs）的自身抗体检测对高危人群的肺癌早期诊断具有广阔的应用前景。研究内容：在前期研究基础上，本课题结合三种免疫组学技术：免疫基因组学技术（SEREX）、免疫蛋白组学技术（SERPA）两种实验技术和免疫组学生物信息挖掘技术，初步筛选可能的肺癌TAAs；进而利用高通量蛋白芯片技术检测肺癌、慢性肺病和正常人血清中TAAs自身抗体进行TAAs验证；随后利用定量ELISA技术检测大样本量肺癌和正常对照血清中自身抗体以进一步确认肺癌TAAs，最后对一批诊断前肺癌系列血清及对照血清进行检测。结合流行病学原理和技术评价TAAs的诊断价值并利用机器学方法筛选最佳的TAAs组合。重要结果：利用SEREX技术结合生物信息学方法筛选出10个TAAs指标，进一步的ELISA验证阶段发现4种有潜力的指标（ACTR3、PSIP1、TOP2A、RPS6KA5）。利用Oncomine数据库挖掘技术筛选出16个指标，经过ELISA验证发现6种有意义TAAs指标（ GREM1、HMGB3、MMP12、NUSAP1和ZWINT）。进一步结合癌症驱动基因列表，定制了含有154个蛋白的蛋白芯片进行TAAs的大规模筛选和验证，结果筛选出12种TAAs指标，大样本量ELISA血清学验证确定了8种有诊断价值的TAAs指标。由于单个指标的灵敏度受限，采用多种机器学习方法进行联合诊断模型的构建和验证，最终发现含有7种TAAs的决策树模型具有最佳的诊断价值，灵敏度为94.4％，特异度为84.9％，准确性为89.9％。该研究成果为高危人群的肺癌早期诊断和早期预警提供准确可靠的血清学肿瘤标志物检测方法。