BRAF
BRAF V600E
Network medicine
Prediction of response
Vemurafenib
Journal
Endocrine
ISSN: 1559-0100
Titre abrégé: Endocrine
Pays: United States
ID NLM: 9434444
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
30
01
2019
accepted:
01
03
2019
pubmed:
10
3
2019
medline:
12
5
2020
entrez:
10
3
2019
Statut:
ppublish
Résumé
Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAF We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAF We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one. We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAF
Identifiants
pubmed: 30850937
doi: 10.1007/s12020-019-01890-4
pii: 10.1007/s12020-019-01890-4
doi:
Substances chimiques
Antineoplastic Agents
0
Vemurafenib
207SMY3FQT
BRAF protein, human
EC 2.7.11.1
Proto-Oncogene Proteins B-raf
EC 2.7.11.1
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
406-413Références
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