Center for Clinical Molecular Medicine, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, 400014, P. R. China.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA.
Cancer Commun (Lond). 2020 Mar;40(2-3):105-118. doi: 10.1002/cac2.12016.
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.
神经母细胞瘤（NB）在基因组异常和临床行为方面存在异质性。尽管我们最近在理解遗传异常与临床特征之间的关系方面取得了进展，但预测预后和对患者进行分层以确定个体化治疗仍然是该疾病的主要挑战之一。本研究旨在为 NB 患者开发一种有效的预后预测模型。
我们整合了多种计算分析方法，定义了反映 MYCN 活性以及染色体异常（包括 1 号染色体短臂缺失（Chr1p_del）和 11 号染色体长臂缺失（Chr11q_del）以及 11 号染色体长臂整体缺失（Chr11q_wls））的基因特征。我们评估了这些特征在七个 NB 基因表达数据集中的预后和预测价值（样本数量范围为 94 至 498，共有 2120 个样本），这些数据是从 RNA 测序和微阵列平台生成的。
MYCN 特征比 MYCN 扩增状态和 MYCN 表达更有效。同样，Chr1p_del 评分比 Chr1p 状态更具预后意义。MYCN、Chr1p_del 和 Chr11q_del 的活性评分与预后不良相关，而 Chr11q_wls 评分与良好结局相关。我们将 MYCN、Chr1p_del、Chr11q_del 和 Chr11q_wls 的活性评分与临床变量整合到一个综合预后模型中，该模型在临床变量或每个基因组异常方面的表现均显著提高。
我们的综合基因特征模型显示出显著改善的预测性能，具有预后和预测信息，因此可以作为分层 NB 患者进行预后评估和监测计划的生物标志物。
