Universidad Complutense de Madrid
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Functional proteomics outlines the complexity of breast cancer molecular subtypes

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Gamez-Pozo, A. y Trilla-Fuentes, L. y Berges-Soria, J. y Selevsek, N. y López-Vacas, R. y Díaz-Almiron, M. y Nanni,, P. y Arevalillo, J. M. y Navarro, H. y Grossmann, J. y Moreno, F. G. y Rioja, R. G. y Prado-Vazquez, G. y Zapater-Moros, A. y Main Yaque, Paloma y Feliu, J. y del Prado, P. y Zamora, P. y Ciruelos, E. y Espinosa, E. y Vara, J. A.F. (2017) Functional proteomics outlines the complexity of breast cancer molecular subtypes. Scientific reports, 7 . ISSN 2045-2322

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URL Oficial: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5577137/



Resumen

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.


Tipo de documento:Artículo
Materias:Ciencias > Matemáticas > Estadística aplicada
Código ID:44777
Depositado:22 Sep 2017 11:38
Última Modificación:25 Sep 2017 08:05

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