筛选肺腺癌相关血清自身抗体标志物
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摘要
背景与目的:肺腺癌是肺癌中发病率最高的一种病理类型,大部分肺腺癌患者确诊时已为晚期,失去了最佳治疗时机。寻找出肿瘤标志物使其在早期发现肺癌是目前亟待解决的问题。本研究联合T7噬菌体展示技术(Phage display)和重组cDNA表达文库血清学分析(Serological analysis of recombinant cDNA expression library, SEREX)技术筛选肺腺癌相关抗原,以检测血清中肺腺癌相关自身抗体标志物。
方法:用10例肺腺癌和10例健康对照血清池(Pooled sera)对已构建好的肺腺癌T7噬菌体展示cDNA文库进行4轮生物淘选(Biopanning),用另20例肺腺癌血清池对4轮淘选后文库进行SEREX筛选。将筛选出来的阳性克隆克隆用96针手动阵列点样仪(96 Solid Pin Multi-Blot Replicator)点在硝酸纤维素膜上形成8×12阵列,并与40份单个血清(26例肺腺癌和14例健康对照)反应,通过聚类分析(Cluster Analysis)建立自身抗体检测模型。对模型中克隆进行测序,在GeneBank数据库中进行同源性比较。用193例单个血清(87例肺腺癌、77例健康对照和29例肺结核血清)进行模型验证。对结果进行判别分析(Discriminant Analysis)及交互校验(Leave-one-out cross validation)及其他统计分析。
结果:共筛选约22,000个噬菌体克隆,挑选出90个阳性克隆。建立了19个克隆组成的自身抗体检测模型,通过验证模型确定了几组自身抗体标志物。1为诊断性自身抗体标志物;2为肺腺癌与健康对照和肺结核鉴别的自身抗体标志物;3为鉴别肺腺癌与肺结核的自身抗体标志物,这三组标志物的敏感性、特异性均大于80.0%。在诊断性自身抗体标志物中,有些自身抗体与性别、淋巴结转移及T分期相关,当与CEA比较时,敏感性比CEA高,特异性比CEA稍差,与CEA联合检测时,敏感性不变,特异性略有增高。
在19个克隆中,没有一个展示肽段来源于人类正确的读码框,其中8个来源于移位的读码框,2个来源于插反了的序列,2个序列不是CDS,2个没有匹配的mRNA,还有5个是预测的miscRNA。
结论:联合T7噬菌体展示技术和SEREX技术筛选了肺腺癌相关抗原,并且其相关自身抗体为有效的诊断与鉴别诊断的标志物,可以用于肺腺癌的筛查。
Introduction Recently, there is a growing tendency to search for tumor-associated autoantibodies as tumor markers. Since there are still no satisfying biomarkers for lung cancer and lung adenocarcinoma has increasing incidence in lung cancer, autoantibody signatures may be good candidate biomarkers.
Methods A cDNA T7 phage-display library derived from ten lung adenocarcinoma tissues was constructed and tumor-associated antigens were enriched by 4 rounds of biopanning process. Through SEREX, positive phage clones were selected out. Arrayed positive phage clones were detected with 40 serum samples (26 from lung adenocarcinoma and 14 from healthy controls) and constructed a phage-clone diagnostic model. The clones in the model were sequenced and BLAST with GeneBank in NCBI. Subsequently, the phage-clone diagnostic model were validated by 193 serum samples (87 from lung adenocarcinoma, 77 from healthy controls and 29 from tuberculosis). The data was analyzed through discriminant analysis and leave-one-out cross validation were used to evaluate predictive accuracies of detectors. Other statistical analysis was performed.
Results Approximately, a total of 22,000 single colonies were screened and picked 90 positive phage clones. A 19-phage-clone diagnostic model was constructed. Through validation process, several panels of autoantibody detectors were identified. One is a diagnostic autoantibody detector, the other is an autoantibody detector differentiating adenocarcinoma from healthy control and tuberculosis, another is an autoantibody detector differentiating adenocarcinoma from tuberculosis. Sensitivity and specificity of them are all higher than 80%. Among the diagnostic autoantibody detector, certain autoantibodies were related with sex, lymph node metastasis or T stage. When compare with CEA, sensitivity of the diagnostic autoantibody detector was higher than CEA and specificity was a little bit lower than CEA. There was no change in sensitivity and specificity raised a little when combination of CEA and the diagnostic autoantibody detector.
Among the 19 phage peptides, there was no peptide streches derived from in-frame. 8 were shifted ORF, 2 was inserted reversely, 2 was out of CDS, 2 had no matched mRNA and 5 were predicted miscRNA.
Conclusion In this study, lung adenocarcinoma associated antigens were screened by combining of T7 phage display and SEREX. Serum autoantibodies against tumor-associated antigens is very promising biomarkers for diagnosis and differentiation. The detector also can be used for screening lung adenocarcinoma.
方法:用10例肺腺癌和10例健康对照血清池(Pooled sera)对已构建好的肺腺癌T7噬菌体展示cDNA文库进行4轮生物淘选(Biopanning),用另20例肺腺癌血清池对4轮淘选后文库进行SEREX筛选。将筛选出来的阳性克隆克隆用96针手动阵列点样仪(96 Solid Pin Multi-Blot Replicator)点在硝酸纤维素膜上形成8×12阵列,并与40份单个血清(26例肺腺癌和14例健康对照)反应,通过聚类分析(Cluster Analysis)建立自身抗体检测模型。对模型中克隆进行测序,在GeneBank数据库中进行同源性比较。用193例单个血清(87例肺腺癌、77例健康对照和29例肺结核血清)进行模型验证。对结果进行判别分析(Discriminant Analysis)及交互校验(Leave-one-out cross validation)及其他统计分析。
结果:共筛选约22,000个噬菌体克隆,挑选出90个阳性克隆。建立了19个克隆组成的自身抗体检测模型,通过验证模型确定了几组自身抗体标志物。1为诊断性自身抗体标志物;2为肺腺癌与健康对照和肺结核鉴别的自身抗体标志物;3为鉴别肺腺癌与肺结核的自身抗体标志物,这三组标志物的敏感性、特异性均大于80.0%。在诊断性自身抗体标志物中,有些自身抗体与性别、淋巴结转移及T分期相关,当与CEA比较时,敏感性比CEA高,特异性比CEA稍差,与CEA联合检测时,敏感性不变,特异性略有增高。
在19个克隆中,没有一个展示肽段来源于人类正确的读码框,其中8个来源于移位的读码框,2个来源于插反了的序列,2个序列不是CDS,2个没有匹配的mRNA,还有5个是预测的miscRNA。
结论:联合T7噬菌体展示技术和SEREX技术筛选了肺腺癌相关抗原,并且其相关自身抗体为有效的诊断与鉴别诊断的标志物,可以用于肺腺癌的筛查。
Introduction Recently, there is a growing tendency to search for tumor-associated autoantibodies as tumor markers. Since there are still no satisfying biomarkers for lung cancer and lung adenocarcinoma has increasing incidence in lung cancer, autoantibody signatures may be good candidate biomarkers.
Methods A cDNA T7 phage-display library derived from ten lung adenocarcinoma tissues was constructed and tumor-associated antigens were enriched by 4 rounds of biopanning process. Through SEREX, positive phage clones were selected out. Arrayed positive phage clones were detected with 40 serum samples (26 from lung adenocarcinoma and 14 from healthy controls) and constructed a phage-clone diagnostic model. The clones in the model were sequenced and BLAST with GeneBank in NCBI. Subsequently, the phage-clone diagnostic model were validated by 193 serum samples (87 from lung adenocarcinoma, 77 from healthy controls and 29 from tuberculosis). The data was analyzed through discriminant analysis and leave-one-out cross validation were used to evaluate predictive accuracies of detectors. Other statistical analysis was performed.
Results Approximately, a total of 22,000 single colonies were screened and picked 90 positive phage clones. A 19-phage-clone diagnostic model was constructed. Through validation process, several panels of autoantibody detectors were identified. One is a diagnostic autoantibody detector, the other is an autoantibody detector differentiating adenocarcinoma from healthy control and tuberculosis, another is an autoantibody detector differentiating adenocarcinoma from tuberculosis. Sensitivity and specificity of them are all higher than 80%. Among the diagnostic autoantibody detector, certain autoantibodies were related with sex, lymph node metastasis or T stage. When compare with CEA, sensitivity of the diagnostic autoantibody detector was higher than CEA and specificity was a little bit lower than CEA. There was no change in sensitivity and specificity raised a little when combination of CEA and the diagnostic autoantibody detector.
Among the 19 phage peptides, there was no peptide streches derived from in-frame. 8 were shifted ORF, 2 was inserted reversely, 2 was out of CDS, 2 had no matched mRNA and 5 were predicted miscRNA.
Conclusion In this study, lung adenocarcinoma associated antigens were screened by combining of T7 phage display and SEREX. Serum autoantibodies against tumor-associated antigens is very promising biomarkers for diagnosis and differentiation. The detector also can be used for screening lung adenocarcinoma.
引文
1 Casiano CA, Mediavilla-Varela M, Tan EM. Tumor-associated antigen arrays for the serological diagnosis of cancer. Mol Cell Proteomics. 2006 ;5(10):1745-59.
2 Imafuku Y, Omenn GS, Hanash S. Proteomics approaches to identify tumor antigen directed autoantibodies as cancer biomarkers. Dis Markers. 2004; 20(3):149-53.
3 Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature. 2008 3;452(7187):571-9
4 Lubin R, Zalcman G, Bouchet L,et al. Serum p53 antibodies as early markers of lung cancer. Nat Med. 1995;1(7):701-2.
5 Trivers GE, De Benedetti VM, Cawley HL, et al. Anti-p53 antibodies in sera from patients with chronic obstructive pulmonary disease can predate a diagnosis of cancer.Clin Cancer Res. 1996;2(10):1767-75.
6 Hirasawa Y, Kohno N, Yokoyama A,et al. Natural autoantibody to MUC1 is a prognostic indicator for non-small cell lung cancer. Am J Respir Crit Care Med. 2000;161(2 Pt 1):589-94.
7 Türeci O, Mack U, Luxemburger U, et al. Humoral immune responses of lung cancer patients against tumor antigen NY-ESO-1. Cancer Lett. 2006 ;236(1):64-71.
8 Yamamoto A, Shimizu E, Takeuchi E, et al. Infrequent presence of anti-c-Myc antibodies and absence of c-Myc oncoprotein in sera from lung cancer patients. Oncology. 1999;56(2):129-33.
9 Zhong L, Ge K, Zu JC,et al. Autoantibodies as potential biomarkers for breast cancer. Breast Cancer Res. 2008;10(3):R40.
10 Wang X, Yu J, Sreekumar A,et al.Autoantibody signatures in prostate cancer. N Engl J Med. 2005;353(12):1224-35.
11 Ran Y, Hu H, Zhou Z,et al. Profiling tumor-associated autoantibodies for the detection of colon cancer. Clin Cancer Res. 2008;14(9):2696-700.
12 Smith GP. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface.Science. 1985;228(4705):1315-7.
13 Sahin U, Türeci O, Schmitt H,et al. Human neoplasms elicit multiple specific immuneresponses in the autologous host. Proc Natl Acad Sci U S A. 1995 ;92(25):11810-3.
14岳文涛王子彤王钥等。非小细胞肺癌T7噬菌体展示文库的构建。中国肺癌杂志, 2008, 11(5): 686-690
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54 Ran Y, Hu H, Zhou Z, et al. Profiling tumor-associated autoantibodies for the detection of colon cancer. Clin Cancer Res. 2008;14(9):2696-700.
55 Lin HS, Talwar HS, Tarca AL, et al. Autoantibody approach for serum-based detection of head and neck cancer. Cancer Epidemiol Biomarkers Prev. 2007;16(11):2396-405.
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2 Imafuku Y, Omenn GS, Hanash S. Proteomics approaches to identify tumor antigen directed autoantibodies as cancer biomarkers. Dis Markers. 2004; 20(3):149-53.
3 Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature. 2008 3;452(7187):571-9
4 Lubin R, Zalcman G, Bouchet L,et al. Serum p53 antibodies as early markers of lung cancer. Nat Med. 1995;1(7):701-2.
5 Trivers GE, De Benedetti VM, Cawley HL, et al. Anti-p53 antibodies in sera from patients with chronic obstructive pulmonary disease can predate a diagnosis of cancer.Clin Cancer Res. 1996;2(10):1767-75.
6 Hirasawa Y, Kohno N, Yokoyama A,et al. Natural autoantibody to MUC1 is a prognostic indicator for non-small cell lung cancer. Am J Respir Crit Care Med. 2000;161(2 Pt 1):589-94.
7 Türeci O, Mack U, Luxemburger U, et al. Humoral immune responses of lung cancer patients against tumor antigen NY-ESO-1. Cancer Lett. 2006 ;236(1):64-71.
8 Yamamoto A, Shimizu E, Takeuchi E, et al. Infrequent presence of anti-c-Myc antibodies and absence of c-Myc oncoprotein in sera from lung cancer patients. Oncology. 1999;56(2):129-33.
9 Zhong L, Ge K, Zu JC,et al. Autoantibodies as potential biomarkers for breast cancer. Breast Cancer Res. 2008;10(3):R40.
10 Wang X, Yu J, Sreekumar A,et al.Autoantibody signatures in prostate cancer. N Engl J Med. 2005;353(12):1224-35.
11 Ran Y, Hu H, Zhou Z,et al. Profiling tumor-associated autoantibodies for the detection of colon cancer. Clin Cancer Res. 2008;14(9):2696-700.
12 Smith GP. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface.Science. 1985;228(4705):1315-7.
13 Sahin U, Türeci O, Schmitt H,et al. Human neoplasms elicit multiple specific immuneresponses in the autologous host. Proc Natl Acad Sci U S A. 1995 ;92(25):11810-3.
14岳文涛王子彤王钥等。非小细胞肺癌T7噬菌体展示文库的构建。中国肺癌杂志, 2008, 11(5): 686-690
15 Chatterjee M, Mohapatra S, Ionan A, et al. Diagnostic markers of ovarian cancer by high-throughput antigen cloning and detection on arrays. Cancer Res. 2006 ;66(2):1181-90.
16 Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature. 2008;452(7187):571-9.
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