Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis.
ERBB signaling
breast cancer
mathematical modeling
signal transduction
systems biology
targeted therapy
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
12 May 2022
12 May 2022
Historique:
received:
31
03
2022
revised:
06
05
2022
accepted:
10
05
2022
entrez:
28
5
2022
pubmed:
29
5
2022
medline:
29
5
2022
Statut:
epublish
Résumé
Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand-drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L
Identifiants
pubmed: 35625984
pii: cancers14102379
doi: 10.3390/cancers14102379
pmc: PMC9139462
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Federal Ministry of Education and Research
ID : 031A429
Organisme : Federal Ministry of Education and Research
ID : 031L0048
Organisme : Else Kröner-Fresenius-Stiftung
ID : HERe
Organisme : Deutsche Forschungsgemeinschaft
ID : 390939984
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