Loi Sherene, Haibe-Kains Benjamin, Desmedt Christine, Lallemand Françoise, Tutt Andrew M, Gillet Cheryl, Ellis Paul, Harris Adrian, Bergh Jonas, Foekens John A, Klijn Jan G M, Larsimont Denis, Buyse Marc, Bontempi Gianluca, Delorenzi Mauro, Piccart Martine J, Sotiriou Christos
Jules Bordet Institute; Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium.
J Clin Oncol. 2007 Apr 1;25(10):1239-46. doi: 10.1200/JCO.2006.07.1522.
A number of microarray studies have reported distinct molecular profiles of breast cancers (BC), such as basal-like, ErbB2-like, and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER) -positive subtypes has been inconsistent. Therefore, refinement of their molecular definition is needed.
We have previously reported a gene expression grade index (GGI), which defines histologic grade based on gene expression profiles. Using this algorithm, we assigned ER-positive BC to either high-or low-genomic grade subgroups and compared these with previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome.
Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biologic pathway, these were highly comparable to the previously described luminal A and B classification and significantly correlated to the risk groups produced using the 21-gene recurrence score. The two subtypes were associated with statistically distinct clinical outcome in both systemically untreated and tamoxifen-treated populations.
The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple data sets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC.
多项微阵列研究报告了乳腺癌（BC）不同的分子特征，如基底样、ErbB2样以及两到三种管腔样亚型。这些特征与不同的临床结果相关。然而，尽管基底和ErbB2亚型被反复识别，但雌激素受体（ER）阳性亚型的鉴定一直不一致。因此，需要完善其分子定义。
我们之前报道了一种基因表达分级指数（GGI），它基于基因表达谱定义组织学分级。使用该算法，我们将ER阳性BC分为高基因组分级或低基因组分级亚组，并将这些亚组与之前报道的ER阳性分子分类进行比较。作为进一步验证，我们将666个ER阳性样本分类为亚型并评估其临床结果。
使用GGI可以定义两个ER阳性分子亚组（高基因组分级和低基因组分级）。尽管追踪的是单一生物学途径，但这些亚组与之前描述的管腔A和B分类高度可比，并且与使用21基因复发评分产生的风险组显著相关。在未经全身治疗和接受他莫昔芬治疗的人群中，这两种亚型均与统计学上不同的临床结果相关。
基因组分级的使用能够以简单且高度可重复的方式在多个数据集中识别出两种临床上不同的ER阳性分子亚型。本研究强调了增殖相关基因在预测ER阳性BC预后中的重要作用。
