Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
16 02 2023
16 02 2023
Historique:
received:
12
05
2022
accepted:
25
01
2023
entrez:
16
2
2023
pubmed:
17
2
2023
medline:
22
2
2023
Statut:
epublish
Résumé
Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.
Identifiants
pubmed: 36797360
doi: 10.1038/s42003-023-04529-3
pii: 10.1038/s42003-023-04529-3
pmc: PMC9935626
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
179Subventions
Organisme : NCI NIH HHS
ID : U01 CA238475
Pays : United States
Informations de copyright
© 2023. The Author(s).
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