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Abstract：
Neuroblastoma (NB) is a heterogeneous disease with respect to genomic abnormalities and clinical behaviors. Despite recent advances in our understanding of the association between the genetic aberrations and clinical features, it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this disease. The aim of this study was to develop an effective prognosis prediction model for NB patients.
We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p (Chr1p_del) and chromosome 11q (Chr11q_del) as well as chromosome 11q whole loss (Chr11q_wls). We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets (the number of samples ranges from 94 to 498, with a total of 2120) generated from both RNA sequencing and microarray platforms.
MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN expression. Similarly, the Chr1p_del score was more prognostic than Chr1p status. The activity scores of MYCN, Chr1p_del and Chr11q_del were associated with poor prognosis, while the Chr11q_wls score was linked to good outcome. We integrated the activity scores of MYCN, Chr1p_del, Chr11q_del, and Chr11q_wls and clinical variables into an integrative prognostic model, which displayed significant performance over the clinical variables or each genomic aberration alone.
Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information, and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.
We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p (Chr1p_del) and chromosome 11q (Chr11q_del) as well as chromosome 11q whole loss (Chr11q_wls). We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets (the number of samples ranges from 94 to 498, with a total of 2120) generated from both RNA sequencing and microarray platforms.
MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN expression. Similarly, the Chr1p_del score was more prognostic than Chr1p status. The activity scores of MYCN, Chr1p_del and Chr11q_del were associated with poor prognosis, while the Chr11q_wls score was linked to good outcome. We integrated the activity scores of MYCN, Chr1p_del, Chr11q_del, and Chr11q_wls and clinical variables into an integrative prognostic model, which displayed significant performance over the clinical variables or each genomic aberration alone.
Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information, and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.
摘要：
神经母细胞瘤（NB）是基因组异常和临床行为方面的异质性疾病。尽管我们对遗传畸变和临床特征之间的关联的理解最近取得了进展，预测预后和对患者进行分层以确定该疾病的个性化治疗仍然是主要挑战之一.本研究旨在建立一种有效的NB患者预后预测模型。
我们整合了多种计算分析来定义反映MYCN活性和染色体畸变的基因特征，包括染色体1p（Chr1p_del）和染色体11q（Chr11q_del）的缺失以及染色体11q整体丢失（Chr11q_wls）。我们在从RNA测序和微阵列平台生成的七个NB基因表达数据集（样品数量为94至498，总共2120个）中评估了这些特征的预后和预测值。
MYCN特征是比MYCN扩增状态和MYCN表达更有效的预后标志物。同样，Chr1p_del评分比Chr1p状态更具预后性。MYCN的活动分数，Chr1p_del和Chr11q_del与不良预后相关，而Chr11q_wls得分与良好的结果有关。我们整合了MYCN的活动分数，Chr1p_del,Chr11q_del,将Chr11q_wls和临床变量转化为综合预后模型，在单独的临床变量或每个基因组畸变上显示出显著的表现。
我们的整合基因签名模型显示了具有预后和预测信息的显着改善的预测性能，因此可以作为一种生物标志物对NB患者进行分层，用于预后评估和监测计划。
我们整合了多种计算分析来定义反映MYCN活性和染色体畸变的基因特征，包括染色体1p（Chr1p_del）和染色体11q（Chr11q_del）的缺失以及染色体11q整体丢失（Chr11q_wls）。我们在从RNA测序和微阵列平台生成的七个NB基因表达数据集（样品数量为94至498，总共2120个）中评估了这些特征的预后和预测值。
MYCN特征是比MYCN扩增状态和MYCN表达更有效的预后标志物。同样，Chr1p_del评分比Chr1p状态更具预后性。MYCN的活动分数，Chr1p_del和Chr11q_del与不良预后相关，而Chr11q_wls得分与良好的结果有关。我们整合了MYCN的活动分数，Chr1p_del,Chr11q_del,将Chr11q_wls和临床变量转化为综合预后模型，在单独的临床变量或每个基因组畸变上显示出显著的表现。
我们的整合基因签名模型显示了具有预后和预测信息的显着改善的预测性能，因此可以作为一种生物标志物对NB患者进行分层，用于预后评估和监测计划。
