Omenn Gilbert S
Department of Internal Medicine, Center for Computational Medicine and Biology, and Proteomics Alliance for Cancer Research, University of Michigan, Ann Arbor, MI 48109-0656, USA.
Proteomics. 2006 Oct;6(20):5662-73. doi: 10.1002/pmic.200600331.
Despite a voluminous literature on potential protein biomarkers and a compelling need for diagnostic tests based on biomarkers to detect cancers at much earlier, more treatable stages, progress has been limited. New methods and new instruments for analysis of differences in gene expression, gene methylation, and proteomics are being employed to try to accelerate the discovery phase. Given the heterogeneity of tumor mechanisms and the limitations of analytical methods, it is likely that a variety of strategies will be needed and will be complementary. That is the basis of this review of proteomic approaches. This article adopts a systems biology view, starting with mRNA transcripts in tumors and cultured tumor cells to detect mRNA overexpression, some of which will be correlated with protein overexpression. Some of those proteins may be secreted or released into proximal biofluids and plasma. Detection of low-abundance tumor proteins in the complex and dynamic mixture that is plasma requires combinations of increasingly powerful technologies. The biological amplification of protein signals through the immune system offers autoantibodies as potential biomarkers. Higher abundance proteins, including acute-phase reactants, may have practical value, especially if the proteins are modified as part of the cancer processes. Low molecular weight proteins, fragments, and peptides may offer complementary biomarkers. Promising biomarker candidates must be confirmed in independent studies. Then they must be submitted to higher-throughput methods practical for large-scale validation studies and, hopefully, for clinical and epidemiological applications. Standardized operating procedures for specimen handling, design and use of various reference standards, care to avoid bias and confounding, and guidelines for reporting findings and contributing datasets should enhance the prospects for predictive proteomic profiling of people at risk for cancers.
尽管关于潜在蛋白质生物标志物的文献众多，且迫切需要基于生物标志物的诊断测试来在更早、更可治疗的阶段检测癌症，但进展有限。分析基因表达、基因甲基化和蛋白质组学差异的新方法和新仪器正在被用于加速发现阶段。鉴于肿瘤机制的异质性和分析方法的局限性，可能需要多种策略且这些策略将相互补充。这就是本文对蛋白质组学方法进行综述的基础。本文采用系统生物学观点，从肿瘤和培养的肿瘤细胞中的mRNA转录本开始，以检测mRNA过表达，其中一些将与蛋白质过表达相关。其中一些蛋白质可能会分泌或释放到近端生物流体和血浆中。在血浆这种复杂且动态的混合物中检测低丰度肿瘤蛋白质需要越来越强大的技术组合。通过免疫系统对蛋白质信号进行生物放大可提供自身抗体作为潜在生物标志物。丰度较高的蛋白质，包括急性期反应物，可能具有实际价值，特别是如果这些蛋白质在癌症过程中发生了修饰。低分子量蛋白质、片段和肽可能提供互补的生物标志物。有前景的生物标志物候选物必须在独立研究中得到证实。然后它们必须提交给适用于大规模验证研究的高通量方法，并且有望用于临床和流行病学应用。标本处理的标准化操作程序、各种参考标准的设计和使用、避免偏差和混杂的注意事项以及报告结果和贡献数据集的指南，应能提高对癌症高危人群进行预测性蛋白质组分析的前景。
