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. | |
Authors: | ||
Mesh: | ||
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 Hospitales > H. U. La Paz > Artículos | |
Files in This Item:
File | Description | Size | Format | |
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PMC5577137.pdf | 2.6 MB | Adobe PDF | View/Open |
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