Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center and Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA.
BMC Med. 2009 Dec 14;7:78. doi: 10.1186/1741-7015-7-78.
It is becoming increasingly apparent that cancer drug therapies can only reach their full potential through appropriate patient selection. Matching drugs and cancer patients has proven to be a complex challenge, due in large part to the substantial molecular heterogeneity inherent to human cancers. This is not only a major hurdle to the improvement of the use of current treatments but also for the development of novel therapies and the ability to steer them to the relevant clinical indications. In this commentary we discuss recent studies from Kuo et al., published this month in BMC Medicine, in which they used a panel of cancer cell lines as a model for capturing patient heterogeneity at the genomic and proteomic level in order to identify potential biomarkers for predicting the clinical activity of a novel candidate chemotherapeutic across a patient population. The findings highlight the ability of a 'systems approach' to develop a better understanding of the properties of novel candidate therapeutics and to guide clinical testing and application.See the associated research paper by Kuo et al: http://www.biomedcentral.com/1741-7015/7/77.
越来越明显的是，癌症药物治疗只有通过适当的患者选择才能充分发挥其潜力。由于人类癌症固有的大量分子异质性，药物与癌症患者的匹配已被证明是一项复杂的挑战。这不仅是提高现有治疗方法使用的主要障碍，也是开发新疗法的障碍，也是将它们引导到相关临床适应症的障碍。在这篇评论中，我们讨论了本月发表在 BMC Medicine 上的 Kuo 等人的最新研究，他们使用一系列癌细胞系作为模型，在基因组和蛋白质组水平上捕获患者异质性，以确定预测新型候选化疗药物在患者群体中的临床活性的潜在生物标志物。研究结果强调了“系统方法”的能力，即更好地了解新型候选治疗药物的特性，并指导临床测试和应用。
