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Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification

Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification
Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification
Background: Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified cases. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown.

Methods: A total of 1566 samples from 16 datasets were identified in Gene Expression Omnibus (GEO) and ArrayExpress. Characterisation of the subtypes was analysed by ConsensusClusterPlus. Independent predictors for subgrouping were constructed from the single sample predictor based on the multiclassPairs package. Findings were verified using immunohistochemistry and CIBERSORTx analysis.

Results: 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 a unique prognosis and vulnerability to investigational therapies. A total of 420 genes were identified as independent subgroup predictors with average balanced accuracy of 0.93 and 0.84 for train and test datasets, respectively.

Conclusion: We propose that transcriptional subtyping may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.
0007-0920
Hu, Xiaoxiao
9c761575-9b30-4e87-b612-60c3011cd48e
Zhou, Yilu
1878565d-39e6-467d-a027-7320bf4cdaf2
Hill, Charlotte
6d1cfed3-11b1-48af-b171-b8726ab673eb
Chen, Kai
bf7361ef-e398-4f40-bd5c-c08d728830e8
Cheng, Cheng
8c9245f5-9766-43c8-b4cf-76a29d58c616
Liu, Xiaowei
254cc4bc-4a08-435b-a75b-195d3f965c5e
Duan, Peiwen
39027941-a6a5-4e0a-93a3-da4d253a4a63
Gu, Yaoyao
1d9eb9c6-ad87-4427-ae1a-3e94b76be00c
Wu, Yeming
ed671704-b595-475c-b09c-b454ce77ccbe
Ewing, Rob
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Li, Zhongrong
2ed296af-6928-4d2a-a501-ccfadd5fc101
Wu, Zhixiang
4ba94757-1db5-4979-9dd6-94b8fd5cba11
Wang, Yihua
f5044a95-60a7-42d2-87d6-5f1f789e3a7e
Hu, Xiaoxiao
9c761575-9b30-4e87-b612-60c3011cd48e
Zhou, Yilu
1878565d-39e6-467d-a027-7320bf4cdaf2
Hill, Charlotte
6d1cfed3-11b1-48af-b171-b8726ab673eb
Chen, Kai
bf7361ef-e398-4f40-bd5c-c08d728830e8
Cheng, Cheng
8c9245f5-9766-43c8-b4cf-76a29d58c616
Liu, Xiaowei
254cc4bc-4a08-435b-a75b-195d3f965c5e
Duan, Peiwen
39027941-a6a5-4e0a-93a3-da4d253a4a63
Gu, Yaoyao
1d9eb9c6-ad87-4427-ae1a-3e94b76be00c
Wu, Yeming
ed671704-b595-475c-b09c-b454ce77ccbe
Ewing, Rob
022c5b04-da20-4e55-8088-44d0dc9935ae
Li, Zhongrong
2ed296af-6928-4d2a-a501-ccfadd5fc101
Wu, Zhixiang
4ba94757-1db5-4979-9dd6-94b8fd5cba11
Wang, Yihua
f5044a95-60a7-42d2-87d6-5f1f789e3a7e

Hu, Xiaoxiao, Zhou, Yilu, Hill, Charlotte, Chen, Kai, Cheng, Cheng, Liu, Xiaowei, Duan, Peiwen, Gu, Yaoyao, Wu, Yeming, Ewing, Rob, Li, Zhongrong, Wu, Zhixiang and Wang, Yihua (2024) Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification. British Journal of Cancer. (doi:10.1038/s41416-024-02666-y).

Record type: Article

Abstract

Background: Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified cases. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown.

Methods: A total of 1566 samples from 16 datasets were identified in Gene Expression Omnibus (GEO) and ArrayExpress. Characterisation of the subtypes was analysed by ConsensusClusterPlus. Independent predictors for subgrouping were constructed from the single sample predictor based on the multiclassPairs package. Findings were verified using immunohistochemistry and CIBERSORTx analysis.

Results: 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 a unique prognosis and vulnerability to investigational therapies. A total of 420 genes were identified as independent subgroup predictors with average balanced accuracy of 0.93 and 0.84 for train and test datasets, respectively.

Conclusion: We propose that transcriptional subtyping may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.

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Accepted/In Press date: 19 March 2024
e-pub ahead of print date: 29 March 2024
Additional Information: For the purpose of open access, the authors have applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Identifiers

Local EPrints ID: 488619
URI: http://eprints.soton.ac.uk/id/eprint/488619
ISSN: 0007-0920
PURE UUID: 6eaa4274-9c10-4633-8463-bf3c611394a3
ORCID for Yilu Zhou: ORCID iD orcid.org/0000-0002-4090-099X
ORCID for Rob Ewing: ORCID iD orcid.org/0000-0001-6510-4001
ORCID for Yihua Wang: ORCID iD orcid.org/0000-0001-5561-0648

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Date deposited: 27 Mar 2024 17:55
Last modified: 09 May 2024 01:45

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Contributors

Author: Xiaoxiao Hu
Author: Yilu Zhou ORCID iD
Author: Charlotte Hill
Author: Kai Chen
Author: Cheng Cheng
Author: Xiaowei Liu
Author: Peiwen Duan
Author: Yaoyao Gu
Author: Yeming Wu
Author: Rob Ewing ORCID iD
Author: Zhongrong Li
Author: Zhixiang Wu
Author: Yihua Wang ORCID iD

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