Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
PLoS One. 2010 Dec 1;5(12):e15031. doi: 10.1371/journal.pone.0015031.
Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome.
METHODOLOGY/PRINCIPAL FINDINGS: Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer.
CONCLUSION/SIGNIFICANCE: Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies.
乳腺癌中雌激素受体（ER）的表达预测内分泌治疗的获益。为了识别所有应接受内分泌治疗的 ER 阳性乳腺癌患者，从而改善临床结局，将所有假阴性 ER 状态分类最小化至关重要。在常规肿瘤学实践中，通过免疫组织化学（IHC）或其他免疫测定等半定量方法来确定 ER 状态，其中 ER 表达水平与经验阈值进行比较。基于基因表达的 ER 亚型与基于 IHC 的测定相比的临床相关性尚未系统评估。在这里，我们尝试使用两种基因表达方法来减少假阴性 ER 状态分类的频率，并根据与临床结局的预测和预后一致性来比较这些方法与基于 IHC 的 ER 状态。
方法/主要发现：首先，通过将 ESR1 的双峰表达拟合到混合高斯模型来区分 ER 状态。ESR1 的判别能力表明双峰表达是一种有效的分层乳腺癌的方法；因此，我们确定了一组表达既强烈双峰、模拟 ESR 表达状态、又在乳腺上皮细胞系中高度表达的基因，以得出基于 23 个基因的 ER 表达特征分类器。我们通过比较基于基因的 ER 状态与基于 IHC 的 ER 状态作为未治疗和他莫昔芬治疗队列中临床结局的预测因子，在七个已发表的乳腺癌队列中评估了我们的分类器。在未治疗的乳腺癌队列中，与基于 IHC 的 ER 状态相比，基于 23 个基因的 ER 状态显著改善了预后能力（P=0.006）。在他莫昔芬治疗的队列中，基于 23 个基因的 ER 表达特征预测了临床结局（HR=2.20，P=0.00035）。这些互补的 ER 基于特征的策略估计，在基于 IHC 的 ER 阴性状态中，有 15.1%至 21.8%的患者将被归类为 ER 阳性乳腺癌。
结论/意义：基于表达的 ER 状态分类可以补充 IHC，以最小化假阴性 ER 状态分类并优化内分泌治疗的患者分层。
