Project description:Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-risk neuroblastoma cell lines established before and after therapy. Using partial least squares-discriminant analysis (PLS-DA) with least absolute shrinkage and selection operator (LASSO) and leave-one-out cross-validation, we identified a panel of 55 messenger RNAs and 17 long non-coding RNAs (lncRNAs) which were significantly altered in the expression between cell lines isolated from primary and recurrent tumors. From a neuroblastoma patient cohort, we found 20 of the 55 protein-coding genes to be differentially expressed in patients with unfavorable compared with favorable outcome. We further found a twofold increase or decrease in hazard ratios in these genes when comparing patients with unfavorable and favorable outcome. Gene set enrichment analysis (GSEA) revealed that these genes were involved in proliferation, differentiation and regulated by Polycomb group (PcG) proteins. Of the 17 lncRNAs, 3 upregulated (NEAT1, SH3BP5-AS1, NORAD) and 3 downregulated lncRNAs (DUBR, MEG3, DHRS4-AS1) were also found to be differentially expressed in favorable compared with unfavorable outcome. Moreover, using expression profiles on both miRNAs and mRNAs in the same cohort of cell lines, we found 13 downregulated and 18 upregulated experimentally observed miRNA target genes targeted by miR-21, -424 and -30e, -29b, -138, -494, -181a, -34a, -29b, respectively. The advantage of analyzing biomarkers in a clinically relevant neuroblastoma model system enables further studies on the effect of individual genes upon gene perturbation. In summary, this study identified several genes, which may aid in the prediction of response to therapy and tumor recurrence.

Project 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.

Project description:Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-risk neuroblastoma cell lines established before and after therapy. Using partial least squares-discriminant analysis (PLS-DA) with least absolute shrinkage and selection operator (LASSO) and leave-one-out cross-validation, we identified a panel of 55 messenger RNAs and 17 long non-coding RNAs (lncRNAs) which were significantly altered in the expression between cell lines isolated from primary and recurrent tumors. From a neuroblastoma patient cohort, we found 20 of the 55 protein-coding genes to be differentially expressed in patients with unfavorable compared with favorable outcome. We further found a twofold increase or decrease in hazard ratios in these genes when comparing patients with unfavorable and favorable outcome. Gene set enrichment analysis (GSEA) revealed that these genes were involved in proliferation, differentiation and regulated by Polycomb group (PcG) proteins. Of the 17 lncRNAs, 3 upregulated (NEAT1, SH3BP5-AS1, NORAD) and 3 downregulated lncRNAs (DUBR, MEG3, DHRS4-AS1) were also found to be differentially expressed in favorable compared with unfavorable outcome. Moreover, using expression profiles on both miRNAs and mRNAs in the same cohort of cell lines, we found 13 downregulated and 18 upregulated experimentally observed miRNA target genes targeted by miR-21, -424 and -30e, -29b, -138, -494, -181a, -34a, -29b, respectively. The advantage of analyzing biomarkers in a clinically relevant neuroblastoma model system enables further studies on the effect of individual genes upon gene perturbation. In summary, this study identified several genes, which may aid in the prediction of response to therapy and tumor recurrence.

Dataset Information

Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk.

Publications

Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk.

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