Transcription profiling of breast cancer tumor samples to identify subtypes

Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.

研究背景 既往研究表明，可基于DNA微阵列（DNA microarray）表达谱将乳腺癌肿瘤组织样本划分为不同亚型。最新研究提供证据显示存在五种乳腺癌亚型：正常乳腺样型、基底样型、管腔A型、管腔B型以及ERBB2+型。
研究结果 本研究采用全新分析方法，对包含5个独立cDNA微阵列（cDNA microarray）数据集的599份微阵列样本开展分析，结果证实乳腺癌肿瘤组织微阵列可被稳定划分为以下三种亚型：ESR1+/ERBB2-、ESR1-/ERBB2-以及ERBB2+型（以下统称为ESR1/ERBB2亚型）。本研究对这三种亚型均完成统计学验证，并证实样本所属亚型可显著预测患者总生存期与无远处转移概率。
研究结论 经本次统计学验证流程，本研究得到一组亚型质心，可应用于任意以UniGene簇ID（UniGene Cluster ID）进行索引的微阵列样本，将其归类至ESR1/ERBB2亚型之一。此外，本研究定义ESR1/ERBB2亚型的方法并非仅适用于乳腺癌，该方法可用于存在至少两组独立疾病样本微阵列数据集的任意疾病的亚型识别。