Wang Yixin, Klijn Jan G M, Zhang Yi, Sieuwerts Anieta M, Look Maxime P, Yang Fei, Talantov Dmitri, Timmermans Mieke, Meijer-van Gelder Marion E, Yu Jack, Jatkoe Tim, Berns Els M J J, Atkins David, Foekens John A
Veridex LLC, a Johnson & Johnson Company, San Diego, CA, USA.
Lancet. 2005;365(9460):671-9. doi: 10.1016/S0140-6736(05)17947-1.
Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer.
We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment.
In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult.
The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.
全基因组基因表达测量能够识别基因活性模式,这些模式可对肿瘤进行亚分类,并且可能为淋巴结阴性乳腺癌患者的个体风险评估提供比现有方法更好的手段。
我们使用Affymetrix Human U133a基因芯片,分析了286例未接受辅助全身治疗的淋巴结阴性患者的冷冻肿瘤样本总RNA中22000条转录本的表达情况。
在115个肿瘤的训练集中,我们确定了一个由76个基因组成的特征图谱,其中包括60个雌激素受体(ER)阳性患者的基因和16个ER阴性患者的基因。在随后的171例淋巴结阴性患者的独立测试集中,该特征图谱显示出93%的敏感性和48%的特异性。即使在多变量分析中校正了传统预后因素后,该基因谱在识别5年内发生远处转移的患者方面也具有很高的信息量(风险比5.67 [95%可信区间2.59 - 12.4])(5.55 [2.46 - 12.5])。76个基因的图谱对于84例绝经前患者(9.60 [2.28 - 40.5])、87例绝经后患者(4.04 [1.57 - 10.4])以及79例肿瘤大小为10 - 20毫米的患者(14.1 [3.34 - 59.2])亚组中的转移发生也是一个强有力的预后因素,对于这组患者,预后预测尤其困难。
所确定的特征图谱为识别远处复发高风险患者提供了一个强大的工具。在独立验证后,识别预后良好患者的能力可使临床医生避免辅助全身治疗或选择侵袭性较小的治疗方案。