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, PalomaFeliu, J.Del Prado, P.Zamora, P.Ciruelos Gil, Eva MaríaEspinosa, E.Vara, J. A.F.2023-06-172023-06-172017Gámez-Pozo A, Trilla-Fuertes L, Berges-Soria J, Selevsek N, López-Vacas R, Díaz-Almirón M, et al. Functional proteomics outlines the complexity of breast cancer molecular subtypes. Sci Rep 2017;7:10100. https://doi.org/10.1038/s41598-017-10493-w.2045-232210.1038/s41598-017-10493-whttps://hdl.handle.net/20.500.14352/18103Breast 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.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/journal articlehttps//doi.org/10.1038/s41598-017-10493-whttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5577137/open access519.22-7311Estadística aplicada