Bièche Ivan, Tozlu Sengül, Girault Igor, Lidereau Rosette
Laboratoire d'Oncogénétique-INSERM E0017, Centre René Huguenin, St-Cloud, France.
Mol Cancer. 2004 Dec 20;3(1):37. doi: 10.1186/1476-4598-3-37.
The clinical course of breast cancer is difficult to predict on the basis of established clinical and pathological prognostic criteria. Given the genetic complexity of breast carcinomas, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. The use of gene expression profiles could result in more accurate and objective prognostication.
To this end, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of a large panel (n = 47) of genes previously identified as candidate prognostic molecular markers in a series of 100 ERalpha-positive breast tumor samples from patients with known long-term follow-up. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) as an independent prognostic marker (P = 0.007 by univariate analysis; P = 0.006 by multivariate analysis). This "poor prognosis" signature was then tested on an independent panel of ERalpha-positive breast tumors from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone: although this "poor prognosis" signature was associated with shorter relapse-free survival in univariate analysis (P = 0.029), it did not persist as an independent prognostic factor in multivariate analysis (P = 0.27).
Our results confirm the value of gene expression signatures in predicting the outcome of breast cancer.
基于已确立的临床和病理预后标准,乳腺癌的临床病程难以预测。鉴于乳腺癌的基因复杂性,与个体基因异常的相关性也不尽人意也就不足为奇了。基因表达谱的应用可能会带来更准确、客观的预后判断。
为此,我们使用实时定量逆转录聚合酶链反应(RT-PCR)分析方法,对一系列来自有长期随访记录患者的100例雌激素受体α(ERα)阳性乳腺肿瘤样本中,之前被鉴定为候选预后分子标志物的大量基因(n = 47)的mRNA表达进行定量。我们确定了一个三基因表达特征(BRCA2、DNMT3B和CCNE1)作为独立的预后标志物(单因素分析P = 0.007;多因素分析P = 0.006)。然后,在一个由104例接受一期手术并随后单独使用他莫昔芬辅助治疗的绝经后乳腺癌患者组成的明确队列中,对一组独立的ERα阳性乳腺肿瘤进行了该“不良预后”特征检测:尽管在单因素分析中该“不良预后”特征与无复发生存期缩短相关(P = 0.029),但在多因素分析中它并未作为独立的预后因素持续存在(P = 0.27)。
我们的结果证实了基因表达特征在预测乳腺癌预后方面的价值。