Histopathological biomarkers for predicting the tumour accumulation of nanomedicines.
Journal
Nature biomedical engineering
ISSN: 2157-846X
Titre abrégé: Nat Biomed Eng
Pays: England
ID NLM: 101696896
Informations de publication
Date de publication:
08 Apr 2024
08 Apr 2024
Historique:
received:
10
10
2022
accepted:
08
02
2024
medline:
9
4
2024
pubmed:
9
4
2024
entrez:
8
4
2024
Statut:
aheadofprint
Résumé
The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score's effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.
Identifiants
pubmed: 38589466
doi: 10.1038/s41551-024-01197-4
pii: 10.1038/s41551-024-01197-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 864121
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : 331065168
Informations de copyright
© 2024. The Author(s).
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