Andre Fabrice, Mazouni Chafika, Hortobagyi Gabriel N, Pusztai Lajos
Department of Breast Medical Oncology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, United States.
Biochim Biophys Acta. 2006 Dec;1766(2):197-204. doi: 10.1016/j.bbcan.2006.08.002. Epub 2006 Aug 9.
Chemotherapy provides variable benefit to patients with breast cancer, with usually modest but occasionally severe side effects. Hence, there is a need to identify predictive biomarkers for its efficacy. DNA arrays have been used in this setting as potential novel predictive diagnostic tools. Several gene signatures and single gene markers were proposed to predict response to chemotherapy. Although this technology offers interesting perspectives through large-scale analysis of the transcriptome, its ability to identify clinically relevant predictors is highly dependent on study design. In the present manuscript, we will review currently available results of breast cancer pharmacogenomics and focus on aspects of study design that are critical to reliably identify predictive biomarkers using DNA array technology. We will discuss whether studies should be done in the overall, unselected breast cancer population or in specific homogeneous molecular subclasses. Next, we will compare advantages and limitations of cohort-based and case-control studies. The choice of end-point to discriminate between sensitive and resistant patients will also be examined.
化疗对乳腺癌患者的疗效各异,通常副作用较小,但偶尔也会很严重。因此,有必要确定其疗效的预测生物标志物。DNA阵列已在这种情况下用作潜在的新型预测诊断工具。人们提出了几种基因特征和单基因标记来预测化疗反应。尽管这项技术通过对转录组进行大规模分析提供了有趣的前景,但其识别临床相关预测指标的能力高度依赖于研究设计。在本手稿中,我们将回顾目前乳腺癌药物基因组学的可用结果,并关注使用DNA阵列技术可靠识别预测生物标志物至关重要的研究设计方面。我们将讨论研究是否应该在总体未选择的乳腺癌人群中进行,还是在特定的同质分子亚类中进行。接下来,我们将比较队列研究和病例对照研究的优缺点。还将研究区分敏感和耐药患者的终点选择。