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Systematic computational identification of prognostic cytogenetic markers in neuroblastoma

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 t...

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Autores principales: Qin, Chao, He, Xiaoyan, Zhao, Yanding, Tong, Chun-Yip, Zhu, Kenneth Y., Sun, Yongqi, Cheng, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909636/
https://www.ncbi.nlm.nih.gov/pubmed/31831008
http://dx.doi.org/10.1186/s12920-019-0620-6
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author Qin, Chao
He, Xiaoyan
Zhao, Yanding
Tong, Chun-Yip
Zhu, Kenneth Y.
Sun, Yongqi
Cheng, Chao
author_facet Qin, Chao
He, Xiaoyan
Zhao, Yanding
Tong, Chun-Yip
Zhu, Kenneth Y.
Sun, Yongqi
Cheng, Chao
author_sort Qin, Chao
collection PubMed
description 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.
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spelling pubmed-69096362019-12-30 Systematic computational identification of prognostic cytogenetic markers in neuroblastoma Qin, Chao He, Xiaoyan Zhao, Yanding Tong, Chun-Yip Zhu, Kenneth Y. Sun, Yongqi Cheng, Chao BMC Med Genomics Research Article 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. BioMed Central 2019-12-12 /pmc/articles/PMC6909636/ /pubmed/31831008 http://dx.doi.org/10.1186/s12920-019-0620-6 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Qin, Chao
He, Xiaoyan
Zhao, Yanding
Tong, Chun-Yip
Zhu, Kenneth Y.
Sun, Yongqi
Cheng, Chao
Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title_full Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title_fullStr Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title_full_unstemmed Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title_short Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
title_sort systematic computational identification of prognostic cytogenetic markers in neuroblastoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909636/
https://www.ncbi.nlm.nih.gov/pubmed/31831008
http://dx.doi.org/10.1186/s12920-019-0620-6
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