Diagnostic classification of childhood cancer using multiscale transcriptomics.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
21
03
2022
accepted:
13
01
2023
pubmed:
19
3
2023
medline:
25
3
2023
entrez:
18
3
2023
Statut:
ppublish
Résumé
The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.
Identifiants
pubmed: 36932241
doi: 10.1038/s41591-023-02221-x
pii: 10.1038/s41591-023-02221-x
pmc: PMC10033451
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
656-666Subventions
Organisme : CIHR
ID : 162267
Pays : Canada
Informations de copyright
© 2023. The Author(s).
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