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Título:
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Functional proteomics outlines the complexity of breast cancer molecular subtypes
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Autores:
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Gamez-Pozo, A. ;
Trilla-Fuentes, L. ;
Berges-Soria, J. ;
Selevsek, N. ;
López-Vacas, R. ;
Díaz-Almiron, M. ;
Nanni,, P. ;
Arevalillo, J. M. ;
Navarro, H. ;
Grossmann, J. ;
Moreno, F. G. ;
Rioja, R. G. ;
Prado-Vazquez, G. ;
Zapater-Moros, A. ;
Main Yaque, Paloma ;
Feliu, J. ;
del Prado, P. ;
Zamora, P. ;
Ciruelos, E. ;
Espinosa, E. ;
Vara, J. A.F.
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Tipo de documento:
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texto impreso
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Editorial:
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Nature Publishing Group, 2017
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Dimensiones:
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application/pdf
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Nota general:
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cc_by
info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Matemáticas: Estadística aplicada
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Tipo = Artículo
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Resumen:
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Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptorpositive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expressionbased probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.
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En línea:
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https://eprints.ucm.es/id/eprint/44777/1/Main10.pdf
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