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

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 þ ) 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 þ 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. Cancer Res; 75(11); 2243-53. Ó2015 AACR.

Label free quantitative proteomics reveals the role of miR-200b in androgen-independent prostate cancer cells

“…Proteomics would enable the unbiased comparison of different cellular states in biology and medicine at a systems-wide level. Deep proteomics analyses are necessary to characterize the complete scenario of signaling pathways and biologic processes altered as a result of the specific state of cells or tissues [18]. Thus, proteomics method combined with bioinformatics is an ideal strategy to analyze the exact relationship between miRNA-target proteins interaction and the resulting phenotype.…”

The “busy life” of unliganded estrogen receptors

“…Combined proteomic approaches have been useful to identify key players in ER-positive BC, unravelling putative biomarkers for BC recurrence [78] and endocrine therapy resistance [79]. Moreover, integration of proteomic data with gene and microRNA expression profiling led to the identification of molecular differences implicated in the clinical context [80]. Indeed, the systems biology approach, based on the combination of interaction or quantitative proteomics and functional genomics, represents an exciting opportunity to better clarify ER biology providing a new overview of the whole set of molecules acting in concert with ERs in the cell [81].…”

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“…These statistically inferred networks provide a deeper level of biological understanding in two main directions: giving support to previously identified biological observations, and giving new insights regarding novel biological interactions. Moreover, the transcriptional network approach has proven to be useful to unveil transcriptional regulation in breast cancer [10,11]. The objective of this study was to evaluate differences in gene expression patterns of breast cancer tumors from patients who had undergone neoadjuvant chemotherapy through a Systems Biology perspective.…”

“…The resulting graph was divided in eighteen branches (functional nodes) and a main function was assigned to each node by gene ontology analysis. The structure of the probabilistic graphical model clearly reflected different biological functions ( Figure 1) Functional node activities were then calculated as previously showed [10,15].…”

“…A functional structure was developed using undirected probabilistic graphical models (PGMs) as previously described [10]. Briefly, PGMs compatible with high-dimensionality were chosen.…”