Repeated observation of breast tumor subtypes in independent gene expression data sets
Repeated observation of breast tumor subtypes in independent gene expression data sets
复制标题
DOI：
10.1073/pnas.0932692100
复制
发表时间：
2003-07-08
影响因子：
11.1
通讯作者：
Botstein, D
中科院分区：
文献类型：
--
作者：
Sorlie, T;Tibshirani, R;Botstein, D
DNA微阵列测量的基因表达特征模式已被用于将肿瘤分类为临床相关的亚组。在这项研究中，我们提炼了以前定义的乳腺肿瘤的亚型，这些亚型可以通过它们不同的基因表达模式来区分。根据534个“固有”基因的表达模式，对115例恶性乳腺肿瘤进行了系统聚类法分析，发现其分为一个基底样亚组、一个ERBB2过表达亚组、两个腔样亚组和一个正常乳腺组织样亚组。用于分类的基因是根据从同一肿瘤中连续采集的两对样本之间的相似表达水平来选择的，这些样本被新辅助治疗15周分开。对来自不同实验室的代表不同患者队列的两个已发表的独立数据集进行了类似的聚类分析，发现了一些相同的乳腺癌亚型。在一个包含发生远处转移的时间信息的数据集中，亚型与这一临床特征的显著差异有关。通过将BRCA1携带者的一组肿瘤包括在分析中，我们发现这种基因型倾向于基本的肿瘤亚型。我们的结果有力地支持了这样的观点，即许多这些乳腺肿瘤亚型代表着生物学上不同的疾病实体。
Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.