Proteomic profiling across breast cancer cell lines and models.
Sci Data
; 10(1): 514, 2023 08 04.
Article
em En
| MEDLINE
| ID: mdl-37542042
ABSTRACT
We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Proteômica
Tipo de estudo:
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
Sci Data
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos