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Cancer Res. 2015 Jun 1;75(11):2243-53. doi: 10.1158/0008-5472.CAN-14-1937. Epub 2015 Apr 16.

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications.

Author information

1
Molecular Oncology and Pathology Lab, Instituto de Genética Médica y Molecular-INGEMM, Instituto de Investigación Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
2
Department of Statistics, Operational Research and Numerical Analysis, University Nacional Educacion a Distancia (UNED), Madrid, Spain.
3
Functional Genomics Centre Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
4
Departamento de Investigación, Instituto Nacional de Enfermedades Neoplásicas, Lima, Surquillo-Lima, Peru.
5
Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain.
6
Biostatistics Unit, Instituto de Investigación Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
7
Medical Oncology Service, Instituto de Investigación Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
8
Medical Oncology Service, Instituto de Investigación Hospital Universitario Doce de Octubre-i+12, Madrid, Spain.
9
Molecular Oncology and Pathology Lab, Instituto de Genética Médica y Molecular-INGEMM, Instituto de Investigación Hospital Universitario La Paz-IdiPAZ, Madrid, Spain. juanangel.fresno@salud.madrid.org.

Abstract

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.

PMID:
25883093
DOI:
10.1158/0008-5472.CAN-14-1937
[Indexed for MEDLINE]
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