Deciphering the Functional Status of Breast Cancers through the Analysis of Their Extracellular Vesicles

Abstract

Breast cancer (BC) accounts for the highest incidence of tumor-related mortality among women worldwide, justifying the growing search for molecular tools for the early diagnosis and follow-up of BC patients under treatment. Circulating extracellular vesicles (EVs) are membranous nanocompartments produced by all human cells, including tumor cells. Since minimally invasive methods collect EVs, which represent reservoirs of signals for cell communication, these particles have attracted the interest of many researchers aiming to improve BC screening and treatment. Here, we analyzed the cargoes of BC-derived EVs, both proteins and nucleic acids, which yielded a comprehensive list of potential markers divided into four distinct categories, namely, (i) modulation of aggressiveness and growth; (ii) preparation of the pre-metastatic niche; (iii) epithelial-to-mesenchymal transition; and (iv) drug resistance phenotype, further classified according to their specificity and sensitivity as vesicular BC biomarkers. We discuss the therapeutic potential of and barriers to the clinical implementation of EV-based tests, including the heterogeneity of EVs and the available technologies for analyzing their content, to present a consistent, reproducible, and affordable set of markers for further evaluation.

Keywords: breast cancer; extracellular vesicles; nanomedicine; omics; theranostics.

Figure 1

Challenges and perspectives regarding omics research on BC-derived EVs. This review presents the current state of the art of the most-studied omics topics on EVs from BC, namely, transcriptomics and proteomics. However, we must fill omics-related gaps before proposing reliable EV-based tools for this disease. Here, we cited some challenges for future research. As vesicles are heterogeneous in terms of size, biogenesis, and cargo, authors must standardize the reporting of methods for the isolation, quantification, characterization, and profiling of EVs. Furthermore, consistent findings in relation to EVs are characterized by their ability to be replicated. Nevertheless, many studies use targeted analysis approaches, which can bias observations. In addition, such replicability must be related to characterizing different individuals of the same subgroup or cell lines of the same subtype. To evaluate this correctly, it is necessary to increase the number of studies comparing less-studied BC cell lines and include a translational approach between tumor cell markers and their vesicular pairs. Regarding associations with the subtype of BC patients, there are gaps produced by the lack of available information about the molecular or clinical profiles of these patients, which can complicate future secondary analysis. After conquering this challenge, we can promisingly combine data from different omics studies of BC-derived EVs and select potentially tumor-derived EVs via liquid biopsies from patients to debug or edit these vesicles and induce a beneficial effect in BC patients. Image created on BioRender.com.

Figure 2

Extracellular vesicle (EV) miRNAs in breast-cancer-related studies. Sankey plots show the number of studies mentioning each relevant vesicular miRNA from cell supernatant (A) or human bodily fluids (B). The cell lines in which the EV cargo was analyzed are classified into the main BC subtypes following the criteria given in Dai et al.’s (2017) study [124]. For studies on EVs collected from BC patients, the subtype information was retrieved from each study. H: Her2, TNA: Triple-Negative A, TNB: Triple-Negative B, LA: Luminal A, and LB: Luminal B.

Figure 3

Relevant putative markers in BC-derived EVs. Breast cancer cells produce a great diversity of EVs. These EVs can be classified into subpopulations based on their proteomic and transcriptomic cargo. In this review, we associate some BC-derived EV subpopulations with tumor-related functions. In addition, we include putative markers related to their types (miRNA, lncRNA, mRNA, circRNA, or protein) for each subpopulation. Image created on BioRender.com.

Figure 4

Extracellular vesicle (EVs) proteins in breast cancer proteome studies. (A) The most frequently identified proteins in the evaluated studies. The donut graph shows the corresponding subcellular locations. (B) Subcellular localization of proteins identified in EVs in at least two studies. (C) Cell-cycle-related and p53 pathways in which EV proteins participate.