Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment

Abstract

Early ducts of breast tumors are unequivocally acidic. High rates of glycolysis combined with poor perfusion lead to a congestion of acidic metabolites in the tumor microenvironment, and pre-malignant cells must adapt to this acidosis to thrive. Adaptation to acidosis selects cancer cells that can thrive in harsh conditions and are capable of outgrowing the normal or non-adapted neighbors. This selection is usually accompanied by phenotypic change. Epithelial mesenchymal transition (EMT) is one of the most important switches correlated to malignant tumor cell phenotype and has been shown to be induced by tumor acidosis. New evidence shows that the EMT switch is not a binary system and occurs on a spectrum of transition states. During confirmation of the EMT phenotype, our results demonstrated a partial EMT phenotype in our acid-adapted cell population. Using RNA sequencing and network analysis we found 10 dysregulated network motifs in acid-adapted breast cancer cells playing a role in EMT. Our further integrative analysis of RNA sequencing and SILAC proteomics resulted in recognition of S100B and S100A6 proteins at both the RNA and protein level. Higher expression of S100B and S100A6 was validated in vitro by Immunocytochemistry. We further validated our finding both in vitro and in patients' samples by IHC analysis of Tissue Microarray (TMA). Correlation analysis of S100A6 and LAMP2b as marker of acidosis in each patient from Moffitt TMA approved the acid related role of S100A6 in breast cancer patients. Also, DCIS patients with higher expression of S100A6 showed lower survival compared to lower expression. We propose essential roles of acid adaptation in cancer cells EMT process through S100 proteins such as S100A6 that can be used as therapeutic strategy targeting both acid-adapted and malignant phenotypes.

Biomarkers of tumor invasiveness in proteomics (Review)

“…Moreover, the serum S100B level has been shown to be independently associated with a poor outcome of patients with metastatic breast cancer (314), while treatment with S100B inhibitors blocks glioma growth through the alteration of the polarization and trafficking of tumor-associated myeloid-derived cells (315). Notably, although S100B was not detected in the data described in the present review, a recent study based on an integrative analysis of transcriptomic and proteomic data of MCF7 cells submitted to acid adaptation reported high expression levels of both S100B and S100A6 in the course of EMT process affecting breast cancer cells (316). Additionally, proteomics and microarray data from breast cancer patients have revealed a shorter long-term survival in two subsets of patients with a combined high expression of S100B, kallikrein and S100A7 or S100A14 -S100A16 (317).…”

Single-Cell RNA-seq Reveals Obesity-Induced Alterations in the Brca1-Mutated Mammary Gland Microenvironment

“…Based on these known roles of galectin-1, HFD-induced upregulation of Lgals1 expression in memory T cells may have an immunosuppressive effect. S100a6, encoding S100 calcium binding protein A6, is associated with poor prognosis of DCIS patients and several cancer types overexpress this S100 gene, suggesting S100a6 plays an oncogenic role in tumorigenesis [56]. S100a6 can be secreted from several tumor cell types, and has been implicated in regulation of cell cycle and CXCL14, a pro-inflammatory chemokine [57].…”

GRAPES-DD: Exploiting Decision Diagrams for Index-Driven Biomedical Databases Search

“…The search for substructures, also called embeddings, in biochemical structures is widely involved in many bioinformatic approaches as well as in the field of computational chemistry. It is a preliminary step in findings motifs in biological networks [5,6,7], or in tuning parameters in biomolecular and molecular simulations [8]. In addition, it is a base procedure in biomedical database systems [1] for drug repurposing studies [9], for prioritizing disease-associated genes [10], or for knowledge discovery and hypotheses generation in plant and crop researchers [11].…”