Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12530/30613
Title: Functional proteomics outlines the complexity of breast cancer molecular subtypes.
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Issue Date: 2017
Citation: Sci Rep.2017 08;(7)1:10100
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 receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based 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.
PMID: 28855612
URI: https://hdl.handle.net/20.500.12530/30613
Rights: openAccess
Appears in Collections:Hospitales > H. U. 12 de Octubre > Artículos
Fundaciones e Institutos de Investigación > IIS H. U. 12 de Octubre > Artículos
Fundaciones e Institutos de Investigación > IIS H. U. La Paz > Artículos

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