Goodison Steve, Urquidi Virginia
Department of Surgery, University of Florida, 653 West 8th Street, Jacksonville, FL 32209, USA.
Expert Rev Proteomics. 2008 Jun;5(3):457-67. doi: 10.1586/14789450.5.3.457.
The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.
预测患者癌症转移行为以及检测和根除此类复发的能力,仍然是肿瘤学领域的主要临床挑战。虽然此前已经鉴定和测试了许多潜在的分子生物标志物,但没有一种能比常规组织病理学标准大大提高标本评估的准确性,而且迄今为止,它们对个体预后的预测效果很差。高通量蛋白质组分析技术的不断发展为癌症研究开辟了新途径,通过应用于基于组织的研究和动物模型,将有助于识别与乳腺肿瘤细胞表型相关的分子特征。合理使用这些方法有可能提供有效的生物标志物,并增进我们对肿瘤生物学的了解。这反过来将推动旨在改善乳腺癌致命性扩散的靶向治疗的发展。在这篇综述中,我们重点关注乳腺肿瘤进展过程中不断积累的蛋白质组特征,特别是那些与远处转移发生相关的特征,并讨论该领域一些预期的未来发展。