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BMC Med Genomics. 2019 Dec 12;12(1):192. doi: 10.1186/s12920-019-0620-6.

Systematic computational identification of prognostic cytogenetic markers in neuroblastoma.

Author information

1
Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, No.3 Shangyuancun, Beijing, 100044, Haidian District, China.
2
Department of Medicine, Baylor College of Medicine, BCM451, Suite 100D, Houston, TX, 77030, USA.
3
Center for Clinical Molecular Medicine, Children's Hospital, Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Key Laboratory of Pediatrics in Chongqing, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Chongqing, 400014, China.
4
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA.
5
Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA.
6
Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, No.3 Shangyuancun, Beijing, 100044, Haidian District, China. yqsun@bjtu.edu.cn.
7
Department of Medicine, Baylor College of Medicine, BCM451, Suite 100D, Houston, TX, 77030, USA. chao.cheng@bcm.edu.

Abstract

BACKGROUND:

Neuroblastoma (NB) is the most common extracranial solid tumor found in children. The frequent gain/loss of many chromosome bands in tumor cells and absence of mutations found at diagnosis suggests that NB is a copy number-driven cancer. Despite the previous work, a systematic analysis that investigates the relationship between such frequent gain/loss of chromosome bands and patient prognosis has yet to be implemented.

METHODS:

First, we analyzed two NB CNV datasets to select chromosomal bands with a high frequency of gain or loss. Second, we applied a computational approach to infer sample-specific CNVs for each chromosomal band selected in step 1 based on gene expression data. Third, we applied univariate Cox proportional hazards models to examine the association between the resulting inferred copy number values (iCNVs) and patient survival. Finally, we applied multivariate Cox proportional hazards models to select chromosomal bands that remained significantly associated with prognosis after adjusting for critical clinical variables, including age, stage, gender, and MYCN amplification status.

RESULTS:

Here, we used a computational method to infer the copy number variations (CNVs) of sample-specific chromosome bands from NB patient gene expression profiles. The resulting inferred CNVs (iCNVs) were highly correlated with the experimentally determined CNVs, demonstrating CNVs can be accurately inferred from gene expression profiles. Using this iCNV metric, we identified 58 frequent gain/loss chromosome bands that were significantly associated with patient survival. Furthermore, we found that 7 chromosome bands were still significantly associated with patient survival even when clinical factors, such as MYCN status, were considered. Particularly, we found that the chromosome band chr11p14 has high potential as a novel candidate cytogenetic biomarker for clinical use.

CONCLUSION:

Our analysis resulted in a comprehensive list of prognostic chromosome bands supported by strong statistical evidence. In particular, the chr11p14 gain event provided additional prognostic value in addition to well-established clinical factors, including MYCN status, and thereby represents a novel candidate cytogenetic biomarker with high clinical potential. Additionally, this computational framework could be readily extended to other cancer types, such as leukemia.

KEYWORDS:

Chr11p14; Chr11q23; Cytogenetic marker; Neuroblastoma; Prognosis

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