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一种信息学算法快速筛选肿瘤特异抗原的方法

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发表于 2013-1-28 21:33:39 | 显示全部楼层 |阅读模式
关键词:Cancer Research,癌症
2012年11月14日讯 /生物谷BIOON/ 近日,由北京大学医学部免疫学系的研究人员和美国贝勒医学院合作,在筛选肿瘤特异抗原方面取得重要成果。最新代表性研究论文于近日在线发表在《癌症研究》(Cancer Research)上。
肿瘤特异抗原(Tumor-specific antigen, TSA)具有在正常组织低表达,而在肿瘤组织中高表达的特点,对肿瘤的早期诊断和肿瘤免疫治疗都十分关键。部分肿瘤特异抗原被证明能有效诱导体内免疫应答,在肿瘤疫苗的研制中有重要价值。
筛选肿瘤特异抗原的传统方法包括抑制性删减杂交(SSH)和重组cdna表达文库的血清学分析(SEREX)等,实验周期较长,且成本偏高。该研究团队首先采用生物信息学策略筛选肿瘤特异抗原,通过分析在临床上已经得到认可的肿瘤特异抗原的表达谱特点,开发出一套独特的算法HEPA(heterogeneous expression profile analysis),能有效从基因组中筛选出肿瘤中高表达的基因,并用实验方法进行验证,初步得到19个肿瘤特异抗原。研究人员随后开发了一种新的筛选血清中抗体的方法PARSE (Protein A/G based reverse serum ELISA) ,用以检测肿瘤患者血清中针对肿瘤特异抗原的自身特异性抗体。通过PARSE方法,研究人员发现在4~11的肺癌患者血清中,有针对四个新肿瘤抗原的自身特异性抗体,而在健康人血清中罕见。在两组独立的肺癌病人血清中,四种自身特异性抗体的联合ROC曲线下面积均达0.71以上,提示可以作为诊断肺癌的联合标志物。
该研究提供了从分析肿瘤特异抗原表达谱特点入手,开发信息学算法快速筛选肿瘤特异抗原,到表达谱验证,检测肿瘤患者体液免疫反应阳性率,再到肿瘤标志物联合诊断的一套完整思路,对肿瘤的早期诊断和肿瘤疫苗的研制都具有重要参考价值。(生物谷Bioon.com)



doi:10.1158/0008-5472.CAN-12-1656
PMC:
PMID:

An integrated genome-wide approach to discover tumor specific antigens as potential immunological and clinical targets in cancer
Tumor-specific antigens (TSAs) are central elements in the immune control of cancers. To systematically explore the TSA genome, we developed a computational technology called Heterogeneous Expression Profile Analysis (HEPA), which can identify genes relatively uniquely expressed in cancer cells in contrast to normal somatic tissues. Rating human genes by their HEPA score enriched for clinically useful TSA genes, nominating candidate targets whose tumor-specific expression was verified by RT-PCR. Coupled with HEPA, we designed a novel assay termed Protein A/G based Reverse Serological Evaluation (PARSE) for quick detection of serum autoantibodies against an array of putative TSA genes. Remarkably, highly tumor-specific autoantibody responses against seven candidate targets were detected in 4-11 of patients, resulting in distinctive autoantibody signatures in lung and stomach cancers. Interrogation of a larger cohort of 149 patients and 123 healthy individuals validated the predictive value of the autoantibody signature for lung cancer. Together, our results establish an integrated technology to uncover a cancer-specific antigen genome offering a reservoir of novel immunological and clinical targets.
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