在激素受体阳性乳腺癌新辅助生物标志物研究中的微阵列数据分析。
Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer.
出版信息
Breast Cancer Res. 2010;12(4):112. doi: 10.1186/bcr2616. Epub 2010 Aug 20.
Abstract
Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer. Typically, these data have focused on analyses conducted on the diagnostic specimen. However, dynamic temporal changes in gene expression associated with treatment may deliver significant improvements to the current generation of predictive models. We present and discuss some statistical issues relevant to the paper by Taylor and colleagues, who conducted studies to model the prognostic potential of gene expression changes that occur after endocrine treatment.
摘要
微阵列数据已被广泛用于发现预测雌激素受体阳性乳腺癌对内分泌治疗反应的生物标志物。通常，这些数据集中在对诊断标本进行的分析上。然而，与治疗相关的基因表达的动态时间变化可能会为当前一代预测模型带来显著的改进。我们介绍并讨论了与 Taylor 及其同事的论文相关的一些统计问题，他们进行了研究，以构建内分泌治疗后基因表达变化的预后潜力模型。
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