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Functional proteomics outlines the complexity of breast cancer molecular subtypes

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Gamez-Pozo, A. and Trilla-Fuentes, L. and Berges-Soria, J. and Selevsek, N. and López-Vacas, R. and Díaz-Almiron, M. and Nanni,, P. and Arevalillo, J. M. and Navarro, H. and Grossmann, J. and Moreno, F. G. and Rioja, R. G. and Prado-Vazquez, G. and Zapater-Moros, A. and Main Yaque, Paloma and Feliu, J. and del Prado, P. and Zamora, P. and Ciruelos, E. and Espinosa, E. and 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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Official URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5577137/



Abstract

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.


Item Type:Article
Subjects:Sciences > Mathematics > Applied statistics
ID Code:44777
Deposited On:22 Sep 2017 11:38
Last Modified:25 Sep 2017 08:05

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