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Sci Rep. 2017 Aug 30;7(1):10100. doi: 10.1038/s41598-017-10493-w.

Functional proteomics outlines the complexity of breast cancer molecular subtypes.

Author information

1
Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain.
2
Biomedica Molecular Medicine SL, Madrid, Spain.
3
Functional Genomics Center Zürich, University of Zürich/ETH Zürich, Zürich, Switzerland.
4
Department of Statistics, Biostatistics Unit, La Paz University Hospital - IdiPAZ, Madrid, Spain.
5
Operational Research and Numerical Analysis, National Distance Education University (UNED), Madrid, Spain.
6
Medical Laboratory Service, La Paz University Hospital Health Research Institute-IdiPAZ, Madrid, Spain.
7
Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain.
8
Medical Oncology Service, La Paz University Hospital-IdiPAZ, Madrid, Spain.
9
Medical Oncology Service, Basurto Hospital, Bilbao, Spain.
10
Medical Oncology Service, Hospital 12 de Octubre (i+12) Health Research Institute, Madrid, Spain.
11
Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain. juanangel.fresno@salud.madrid.org.

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
PMCID:
PMC5577137
DOI:
10.1038/s41598-017-10493-w
[Indexed for MEDLINE]
Free PMC Article

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