Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.
Cancer
Cell phenotypes
Cell signaling
Data integration
Drug response
Drug sensitivity
Kinase inhibitor treatment
Machine learning
Predictive modeling
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2023
2023
Historique:
received:
10
02
2023
accepted:
03
10
2023
medline:
1
12
2023
pubmed:
29
11
2023
entrez:
29
11
2023
Statut:
epublish
Résumé
Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC
Identifiants
pubmed: 38025707
doi: 10.7717/peerj.16342
pii: 16342
pmc: PMC10657565
doi:
Substances chimiques
Phosphotransferases
EC 2.7.-
Protein Kinase Inhibitors
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e16342Informations de copyright
©2023 Joisa et al.
Déclaration de conflit d'intérêts
Shawn M. Gomez is an Academic Editor for PeerJ.
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