%0 Journal Article
%A Gamez Pozo, A.
%A Trilla Fuentes, L.
%A Berges Soria, J.
%A Selevsek, N.
%A López Vacas, R.
%A Díaz Almiron, M.
%A Nanni,, P.
%A Arevalillo, J. M.
%A Navarro, H.
%A Grossmann, J.
%A Moreno, F. G.
%A Rioja, R. G.
%A Prado Vazquez, G.
%A Zapater Moros, A.
%A Main Yaque, Paloma
%A Feliu, J.
%A Del Prado, P.
%A Zamora, P.
%A Ciruelos Gil, Eva María
%A Espinosa, E.
%A Vara, J. A.F.
%T Functional proteomics outlines the complexity of breast cancer molecular subtypes
%D 2017
%@ 2045-2322
%U https://hdl.handle.net/20.500.14352/18103
%X 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.
%~