Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Cancer J. 2011 Nov-Dec;17(6):423-8. doi: 10.1097/PPO.0b013e3182383cab.
Strategies to achieve personalized medicine and improve public health encompass assessment of an individual's risk for disease, early detection, and molecular classification of disease resulting in an informed choice of the most appropriate treatment instituted at an early stage of disease development. An unmet need in this field for which proteomics is well suited to make a major contribution is the development of blood-based tests for early cancer detection. This is illustrated in proteomic studies of epithelial cancer that encompass analysis of specimens collected both at the time of diagnosis and specimens collected before onset of symptoms that are particularly suited for the identification of early detection markers. This overarching effort benefits from the availability of plasmas from subject cohorts and of engineered mouse models that are sampled at early stages of tumor development. Integration of findings from plasma with tumor tissue and cancer cell proteomic and genomic data allows elucidation of signatures in plasma for altered signaling pathways. The discovery and further development of early detection markers take advantage of the availability of in-depth quantitative proteomics methods and bioinformatics resources for data mining.
实现个性化医学和改善公众健康的策略包括评估个体的疾病风险、早期检测和疾病的分子分类，从而为疾病发展的早期阶段选择最合适的治疗方案提供信息。在这一领域，蛋白质组学非常适合做出重大贡献的一个未满足的需求是开发基于血液的早期癌症检测测试。这在对上皮癌的蛋白质组学研究中得到了说明，这些研究包括在诊断时收集的标本和在症状出现前收集的标本的分析，这些标本特别适合于识别早期检测标志物。这项全面的工作得益于来自主题队列的血浆的可用性，以及在肿瘤发展的早期阶段采样的工程化小鼠模型。将来自血浆的研究结果与肿瘤组织和癌细胞蛋白质组学和基因组数据进行整合，可以阐明血浆中改变的信号通路的特征。早期检测标志物的发现和进一步发展利用了深度定量蛋白质组学方法和生物信息学资源进行数据挖掘。
