The molecular portraits of breast tumors are conserved across microarray platforms
The molecular portraits of breast tumors are conserved across microarray platforms
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DOI：
10.1186/1471-2164-7-96
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发表时间：
2006-04-27
期刊：
影响因子：
4.4
通讯作者：
Perou, Charles M.
中科院分区：
文献类型：
--
作者：
Hu, Zhiyuan;Fan, Cheng;Perou, Charles M.
工作背景：在独立的数据集中验证新的基因表达特征是开发用于癌症患者风险分层的临床有用测试的关键步骤。然而，验证通常是没有说服力的，因为测试集的大小通常很小。为了克服这个问题，我们使用公开的乳腺癌基因表达数据集和一种新的方法来数据融合，以验证一个新的乳腺肿瘤内在list.Results：一个105肿瘤训练集包含26个样本对，用于获得一个新的乳腺肿瘤内在基因列表。该内在列表包含1300个基因和先前乳腺内在基因组中不存在的增殖特征。我们测试了这个列表作为一个生存预测的311个肿瘤的数据集汇编从三个独立的微阵列研究，融合成一个单一的数据集使用距离加权判别。当使用新的内在基因集对该组合测试集进行分层聚类时，肿瘤被分组为LumA、LumB、基底样、HER 2 +/ER-和正常乳腺样肿瘤亚型，我们在之前的数据集中证明了这一点。这些亚型与无复发生存期和总生存期的显著差异相关。多变量考克斯分析的组合测试集显示，内在亚型分类增加了显着的预后信息，是独立的标准临床预测。从组合的测试集，我们开发了一个客观的和不变的分类的基础上五个内在亚型平均表达谱（即质心），这是专为单样本预测（SSP）。SSP的方法被施加到两个额外的独立的数据集和一致的预测生存在两个系统治疗和未治疗的患者groups.Conclusion：本研究验证了“乳腺肿瘤内在”亚型分类作为一个客观的手段，肿瘤分类，应转化为临床检测进一步的回顾性和前瞻性验证。此外，我们结合现有数据集的方法可用于稳健地验证任何新基因表达谱的潜在临床价值。
Background: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.Results: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.Conclusion: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.