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Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified neuroblastomas. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown. Here, we investigate whether transcriptional subtyping of MYCN non-amplified neuroblastomas can identify molecular subtypes with discrete prognosis and therapeutic vulnerabilities. Using tumour expression data and ConsensusClusterPlus, we demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup 2 has the worst prognosis and this group shows a "MYCN" signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup 3 is characterised by an "inflamed" gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates unique prognosis and vulnerability to investigational therapies. We propose that matching baseline tumour subtype to therapy may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.
Xiaoxiao Hu and Yilu Zhou contributed equally to this work. Correspondence should be addressed to YW (e-mail: [email protected]) or ZW (e-mail: [email protected])
Keywords
MYCN non-amplified neuroblastoma, tumour expression, prognosis, therapy
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This is a list of supplementary files associated with this preprint. Click to download.
- TableS1.docx
Table S1. List of datasets collected for meta-analysis.
- TableS2.xlsx
Table S2. List of top 50% variable genes for consensus clustering.
- TableS3.docx
Table S3. Univariate and multivariate regression analysis in MYCN non-amplified neuroblastomas (n = 1,120).
- TableS4.xlsx
Table S4. DEGs (differentially expressed genes) in subgroups.
- TableS5.xlsx
Table S5. GSEA (gene set enrichment analysis) in subgroups.
- TableS6.xlsx
Table S6. WGCNA (weighted gene co-expression network analysis) in subgroups.
- TableS7.xlsx
Table S7. List of genes in PPI (protein–protein interaction) network analysis.
- TableS8.xlsx
Table S8. List of 46 immune-related gene sets.
- TableS9.xlsx
Table S9. Analysis of clinically actionable genes and drug response.
- SupplementaryMaterialssubmittedLuwang.pdf
Supplementary Methods 1. Data Collection 2. Data Preparation 3. Quality Control 4. Consensus Clustering 5. Defining the Differentially Expressed Genes (DEGs) and Pathway Analysis 6. Weighted Gene Co-expression Network Analysis (WGCNA) and Protein Protein Interaction (PPI) Analysis 7. Clinical Characterisation of Subtypes 8. Submap Analysis 9. Single cell RNA-seq (scRNA-seq) analysis 10. CIBERSORTx Analysis 11. Analysis of Clinically Actionable Genes and Drug Response 12. Identification of Independent Predictors Supplementary Figures Supplementary Figure 1. Characterisation of molecular subtypes in MYCN non-amplified neuroblastomas. Supplementary Figure 2. Clinical characterisation of subtypes within MYCN non-amplified neuroblastomas identifies key distinguishing features. Supplementary Figure 3. Defining molecular features of 3 subtypes in MYCN non-amplified neuroblastomas. Supplementary Figure 4. Subgroup 2 shows a "MYCN" signature, potentially induced by Aurora Kinase A (AURKA) overexpression. Supplementary Figure 5. Subgroup 3 is accompanied by an "inflamed" gene signature. Supplementary Figure 6. Identification of independent predictors to subgroup patients within MYCN non-amplified neuroblastomas and evaluation of different patient stratification strategies.
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
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History
CURRENT STATUS: Posted
Version 1
Posted 28 May, 2021
No community comments so far
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Comments: 0
PDF Downloads: ...
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License:
This work is licensed under a CC BY 4.0 License. Read Full License
Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified neuroblastomas. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown. Here, we investigate whether transcriptional subtyping of MYCN non-amplified neuroblastomas can identify molecular subtypes with discrete prognosis and therapeutic vulnerabilities. Using tumour expression data and ConsensusClusterPlus, we demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup 2 has the worst prognosis and this group shows a "MYCN" signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup 3 is characterised by an "inflamed" gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates unique prognosis and vulnerability to investigational therapies. We propose that matching baseline tumour subtype to therapy may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.
Xiaoxiao Hu and Yilu Zhou contributed equally to this work. Correspondence should be addressed to YW (e-mail: [email protected]) or ZW (e-mail: [email protected])
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
(Not answered)
This is a list of supplementary files associated with this preprint. Click to download.
- TableS1.docx
Table S1. List of datasets collected for meta-analysis.
- TableS2.xlsx
Table S2. List of top 50% variable genes for consensus clustering.
- TableS3.docx
Table S3. Univariate and multivariate regression analysis in MYCN non-amplified neuroblastomas (n = 1,120).
- TableS4.xlsx
Table S4. DEGs (differentially expressed genes) in subgroups.
- TableS5.xlsx
Table S5. GSEA (gene set enrichment analysis) in subgroups.
- TableS6.xlsx
Table S6. WGCNA (weighted gene co-expression network analysis) in subgroups.
- TableS7.xlsx
Table S7. List of genes in PPI (protein–protein interaction) network analysis.
- TableS8.xlsx
Table S8. List of 46 immune-related gene sets.
- TableS9.xlsx
Table S9. Analysis of clinically actionable genes and drug response.
- SupplementaryMaterialssubmittedLuwang.pdf
Supplementary Methods 1. Data Collection 2. Data Preparation 3. Quality Control 4. Consensus Clustering 5. Defining the Differentially Expressed Genes (DEGs) and Pathway Analysis 6. Weighted Gene Co-expression Network Analysis (WGCNA) and Protein Protein Interaction (PPI) Analysis 7. Clinical Characterisation of Subtypes 8. Submap Analysis 9. Single cell RNA-seq (scRNA-seq) analysis 10. CIBERSORTx Analysis 11. Analysis of Clinically Actionable Genes and Drug Response 12. Identification of Independent Predictors Supplementary Figures Supplementary Figure 1. Characterisation of molecular subtypes in MYCN non-amplified neuroblastomas. Supplementary Figure 2. Clinical characterisation of subtypes within MYCN non-amplified neuroblastomas identifies key distinguishing features. Supplementary Figure 3. Defining molecular features of 3 subtypes in MYCN non-amplified neuroblastomas. Supplementary Figure 4. Subgroup 2 shows a "MYCN" signature, potentially induced by Aurora Kinase A (AURKA) overexpression. Supplementary Figure 5. Subgroup 3 is accompanied by an "inflamed" gene signature. Supplementary Figure 6. Identification of independent predictors to subgroup patients within MYCN non-amplified neuroblastomas and evaluation of different patient stratification strategies.
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