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蛋白质组学方法在癌症诊断、治疗和监测中的应用。

Proteomic approaches to the diagnosis, treatment, and monitoring of cancer.

作者信息

Wulfkuhle Julia D, Paweletz Cloud P, Steeg Patricia S, Petricoin Emanuel F, Liotta Lance

机构信息

FDA/NCI Clinical Proteomics Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.

出版信息

Adv Exp Med Biol. 2003;532:59-68. doi: 10.1007/978-1-4615-0081-0_7.

Abstract

The field of proteomics holds promise for the discovery of new biomarkers for the early detection and diagnosis of disease, molecular targets for therapy and markers for therapeutic efficacy and toxicity. A variety of proteomics approaches may be used to address these goals. Two-dimensional gel electrophoresis (2D-PAGE) is the cornerstone of many discovery-based proteomics studies. Technologies such as laser capture microdissection (LCM) and highly sensitive MS methods are currently being used together to identify greater numbers of lower abundance proteins that are differentially expressed between defined cell populations. Newer technologies such as reverse phase protein arrays will enable the identification and profiling of target pathways in small biopsy specimens. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) analysis enables the high throughput characterization of lysates from very few tumor cells or body fluids and may be best suited for diagnosis and monitoring of disease. Such technologies are expected to supplement our arsenal of mRNA-based assays, and we believe that in the future, entire cellular networks and not just a single deregulated protein will be the target of therapeutics and that we will soon be able to monitor the status of these pathways in diseased cells before, during and after therapy.

摘要

蛋白质组学领域有望发现用于疾病早期检测和诊断的新生物标志物、治疗的分子靶点以及治疗效果和毒性的标志物。可以采用多种蛋白质组学方法来实现这些目标。二维凝胶电泳(2D-PAGE)是许多基于发现的蛋白质组学研究的基石。诸如激光捕获显微切割(LCM)和高灵敏度质谱方法等技术目前正结合使用,以鉴定在特定细胞群体之间差异表达的更多低丰度蛋白质。诸如反相蛋白质阵列等较新的技术将能够在小活检样本中鉴定和分析目标通路。表面增强激光解吸/电离飞行时间(SELDI-TOF)分析能够对来自极少肿瘤细胞或体液的裂解物进行高通量表征,可能最适合疾病的诊断和监测。此类技术有望补充我们基于mRNA的检测方法库,并且我们相信,未来整个细胞网络而非仅仅单个失调的蛋白质将成为治疗靶点,而且我们很快就能在治疗前、治疗期间和治疗后监测患病细胞中这些通路的状态。

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