Functional proteomics outlines the complexity of breast cancer molecular subtypes

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.

Molecular characterization of breast cancer cell response to metabolic drugs

“…We assessed the model reliability by growth kinetics studies in ER+ (MCF7 and T47D) and TNBC (MDAMB231 and MDAMB468) cells. This approach allows new hypotheses and provides a global vision of metabolism, and has been previously used to characterize metabolism in samples from patients with breast cancer, which enables us to address clinically relevant questions (Gámez-Pozo et al, 2017).…”

“…Proteomics data were functionally explored using probabilistic graphical models. This tool has been used previously to identify functional differences using clinical samples (Gámez-Pozo et al, 2015;Gámez-Pozo et al, 2017). Regarding metabolic reactions, we propose the use of functional flux activities to compare complete pathways.…”

“…Everolimus, an inhibitor of mammalian target of rapamycin (mTOR), has clinical activity and has been approved for use in patients with breast cancer and other tumors (Beck, 2015). We previously observed significant differences in glucose metabolism between two of the main breast cancer subtypes: hormone-receptor positive (ER+) and triple-negative (TNBC) (Gámez-Pozo et al, 2015;Gámez-Pozo et al, 2017).…”

“…High-throughput mass spectrometry-based proteomics allow the quantification of thousands of proteins and the acquisition of direct information about biological process effectors. Combined with probabilistic graphical models, proteomics enables the characterization of various biological processes between various conditions using expression data without other a priori information (Gámez-Pozo et al, 2015;Gámez-Pozo et al, 2017).…”

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“…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].…”