A three-gene expression signature model for risk stratification of patients with neuroblastoma.
A three-gene expression signature model for risk stratification of patients with neuroblastoma.
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DOI：
10.1158/1078-0432.ccr-11-2483
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发表时间：
2012-04-01
期刊：
影响因子：
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通讯作者：
Lavarino C
中科院分区：
文献类型：
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作者：
Garcia I;Mayol G;Ríos J;Domenech G;Cheung NK;Oberthuer A;Fischer M;Maris JM;Brodeur GM;Hero B;Rodríguez E;Suñol M;Galvan P;de Torres C;Mora J;Lavarino C
神经母细胞瘤是一种胚胎性肿瘤，具有不同的临床过程。尽管有精心的分层策略，精确的临床风险评估仍然是一个挑战。本研究的目的是建立一个基于PCR的预测模型，以提高神经母细胞瘤患者的临床风险评估。该模型使用来自96个样本的实时PCR基因表达数据开发，并在从包括362名患者的实时PCR和微阵列研究获得的单独表达数据集上进行测试。基于我们先前对有利和不利的神经母细胞瘤亚群中差异表达基因的研究，我们确定了三个与患者预后密切相关的基因，CHD 5，PAFAH 1B 1和NME 1。这些基因的表达模式用于开发基于PCR的单评分预测模型。该模型将患者分为两组，两组临床结局显著不同[组1：5年总生存期（OS）：0.93 ± 0.03 vs. 0.53 ± 0.06，5年无事件生存期（EFS）：0.85 ± 0.04 vs. 0.042 ± 0.06，均P < 0.001;组2 OS：0.97 ± 0.02 vs. 0.61 ± 0.1，P = 0.005，EFS：0.91 ± 0.8 vs. 0.56 ± 0.1，P = 0.005;组3 OS：0.99 ± 0.01 vs. 0.56 ± 0.06，EFS：0.96 ± 0.02 vs. 0.43 ± 0.05，均P < 0.001]。多变量分析显示，该模型是生存的独立标志物（P < 0.001，全部）。与公认的风险分层系统相比，该模型在总队列和不同临床相关风险亚组中对患者进行了稳健分类。我们首次在神经母细胞瘤中提出了一种技术上简单的基于PCR的预测模型，可以帮助完善当前的风险分层系统。
Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems.