Epithelial-Mesenchymal Transition towards Clinical Applications in Cancer: History
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Contributor:
Adilson Fonseca Teixeira
,
Siqi Wu
,
Rodney Luwor
,
Hong-Jian Zhu

Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described.

  • ancer metastasis
  • EMT
  • genomics
  • multiomics
  • proteomics
  • secretome
  • transcriptomic

1. Introduction to EMT

Epithelial-mesenchymal transition (EMT) is recognized as a critical turning point for epithelial cells to acquire a mesenchymal phenotype with new biological functionalities. EMT is a process that happens to epithelial cells with appropriate external stimuli, where downregulation of epithelial traits and upregulation of mesenchymal characteristics allow these cells to escape mechanical and biochemical constraints while facilitating cell detachment and movement [1][2][3]. This process involves alterations in cell junctions, polarity, and cytoskeletal arrangements, which are all important factors in determining the cell phenotype [1][2][4]. Due to its essential roles in both physiological and disease states, EMT gained vast interest from researchers in different biomedical fields, especially in cancer research.
During cancer progression, particularly for carcinomas, EMT-related alterations enhance the migratory and invasive potential of malignant cells, critically contributing to the early stages of metastasis. In recent years, an enormous number of epigenomics, transcriptomics, and proteomics studies have helped to characterize the regulatory mechanisms and consequences of EMT on cancer [5][6][7][8].

2. Benefits and Pitfalls of Analyzing Cancer Cell EMT at the DNA/RNA Level

In order to evaluate the gene expression profile of cancer cells undergoing EMT, it is important to successfully recapitulate this process under experimental conditions. Different models have been used to study EMT-related alterations and their impact on cancer progression. In vitro studies, as those discussed here, frequently rely on the treatment of cancer cells with known EMT inducers. Alternatively, the exogenously introduced overexpression of such molecules can also be used to investigate downstream consequences on cell transcriptomes. Moreover, while EMT is known to drive therapeutic resistance, recent studies have demonstrated that drug treatment can also induce EMT in cancer cells [9][10]. Noteworthy, because the phenotypic changes associated with EMT are very well-established, researchers can use different approaches to induce EMT that are easily verifiable, increasing the reliability of the conclusions obtained. In this section, selected studies are briefly discussed aiming at exemplifying the main findings in the field while allowing further comparison with changes observed at protein level in cancer cells undergoing EMT.
Among other results, evaluating gene expression alterations that are associated with cancer cell EMT contributed to portraying the many modifications triggered during cancer progression [11][12][13][14]. Such studies helped to determine not only the duration of EMT-related alterations at the molecular level but also to establish how different effectors cooperate to regulate EMT over time. In bladder carcinoma cells, for example, while alterations in typical EMT markers (e.g., Keratins and Vimentin) were only observed 24 h after FGF-1 treatment, changes in the expression of different transcription factors (e.g., ETS and JUNB) were reported to start and end shortly (2 h) after cell stimulation [11]. Interestingly, alterations in the gene expression of transcription factors were also observed in prostate cancer cells undergoing EMT [15]. Exogenous expression of Runx2 in these cancer cells led to SOX9 and SNAI2 upregulation, reinforcing the existence of cooperation between transcription factors notoriously associated with EMT towards the amplification of this process [15]. Indeed, the relevance of EMT-TFs for the regulation of the EMT program over time has been recently reinforced by Frey and colleagues investigating the pro-metastatic phenotype of SMAD4-defective colorectal cancer cells [16]. Although SMAD4 is a transcription factor critical to canonical TGF-β and Bone morphogenetic protein (BMP) signaling activity, overexpression of the EMT-TF SNAI1 in SMAD4-mutant colorectal cancer cells compensated for this deficiency, inducing cancer cell elongation and invasion in vitro [16]. Accordingly, SMAD4-mutant cells overexpressing SNAI1 also exhibited typical transcriptome changes expected in cells undergoing EMT, including the activation of other pro-EMT molecular pathways (e.g., Wnt signaling pathway) and increased activity of other EMT-TFs (e.g., FOXO4) [16]. These studies emphasize the complexity of the EMT program where distinct signaling pathways can cooperate to enhance this phenotypic shift or compensate each other in case of specific losses or deficiencies.
Interestingly, different studies have reported the failure to establish a good correlation between particular pairs of mRNA and proteins in the EMT context. Although protein degradation must certainly account for part of this observation, post-transcriptional modifications are also likely to contribute to this result. Post-transcriptional modifications respond to an additional layer of complexity in the regulatory mechanisms of the EMT by modifying mRNA stability. For instance, Shapiro and colleagues (2011) described little overlap between genes regulated at the expression level and at the splicing level in HMLE mammary cells undergoing EMT [17]. Yet, both regulatory mechanisms were reported to largely impact the levels of mRNA transcribed from genes associated with cytoskeleton assembling and cell-cell junctions [17]. The impact of splicing events on mRNA levels during EMT was also characterized in prostate cancer cells where 900 differential alternative splicing events were described, impacting the expression of several genes associated with EMT, migration and invasion [13]. Therefore, these and other studies in the field found a tight control of the EMT program that actively operates on RNA species and, more specifically, not only precedes but also follows transcription.
Still, whereas post-transcriptional modifications partially respond to changes in RNA levels, alterations in the epigenome are also expected to contribute to this process. For example, Taube and colleagues (2013) reported a 10-fold gain in the DNA methylation levels at the promoter of the microRNA-203 (miR-203) in Twist-overexpressing HMLE cells [18]. Interestingly, reduced levels of miR-203 were also observed in breast cancer cell lines naturally exhibiting mesenchymal traits (e.g., MDA-MB-231 breast cancer cells), whereas increased miR-203 levels were detected in epithelial counterparts (e.g., MCF-7 breast cancer cells) [18]. Also investigating epigenetic alterations, Peixoto et al. (2019) reported an association between EMT and increased methylation in histone H3 residues, such as in lysine 4 (H3K4) [19]. Moreover, 23% of all upregulated genes characterized in this study coincided with genes potentially activated by H3K4 bi-methylation (H3K4me2) [19]. In fact, increased levels of the inducer mark H3K4me2 and decreased levels of the repressive mark H3K27me3 were found at the promoter of Matrix metalloproteinase 9 (MMP9), a proteinase typically associated with ECM remodeling and cell invasion [19].
In addition to alterations previously described that can regulate epithelial and mesenchymal characteristics, the activity of non-coding RNA can also interfere with mRNA levels. Loboda et al. (2011), for example, reported a negative correlation between the expression of miR-200 family members and EMT-related genes in colorectal cancers (CRCs) [14]. In head and neck squamous cell carcinomas (HNSCCs), the expression of the long non-coding RNA (lncRNA) lnc-LCE5A-1 and lnc-KCTD6-3 was associated with reduced overall survival, while the ectopic expression of this lncRNA decreased Vimentin mRNA levels and impaired HNSCC cell migration in vitro [20]. Reinforcing the importance of characterizing alterations in non-coding RNA, Liao et al. (2017) showed that the impact of TGF-β treatment on the global levels of lncRNA is even higher than its impact on mRNA levels in MCF10A cells [21]. Moreover, the knockdown of the RP6-65G23.5 lncRNA in this model decreased E-cadherin and ZO-1 expression while increasing Vimetin, N-cadherin and Fibronectin levels, and inducing cell migration and invasion [21].
Beyond the evaluation of mRNA levels using in vitro models, transcriptome studies of human tumor samples have consolidated the association between cancer cell EMT and poor prognosis, highlighting the significance of transcriptome analysis in clinical applications. For example, Marquardt et al. (2014) reported the enrichment of an EMT signature in advanced hepatocellular carcinoma (HCC) samples which was also correlated with reduced survival and increased recurrence [22]. Interestingly, several genes differentially regulated in this molecular signature are known targets of TGF-β, Notch, and Vascular endothelial growth factor (VEGF), and are related to cell adhesion, cytoskeleton remodeling and EMT [22]. Nevertheless, it is well-known that changes in EMT-related genes may be impacted by the occurrence of non-cancer cells within tumor masses. This is particularly concerning when considering stroma-rich tumors, where cells from mesenchymal origin expressing high levels of mesenchymal-related genes may mislead the interpretation of bulk transcriptomic analyses of tumor samples with low cancer cell purity [23][24][25]. To overcome this limitation, the increasing use of single-cell (sc)RNA-seq in cancer studies has helped to better understand the significance of such modifications [26][27]. Instead of necessarily refuting previous studies, new methodologies may point to directions otherwise counterintuitive [24][28]. For instance, while cancer cell EMT and invasion are commonly associated, the invasive side of endometrioid adenocarcinomas is reported to be poorly represented by cancer cells expressing EMT-related genes. Such an EMT-related pattern, however, is observed in the endometrial side of these tumors [28]. Moreover, whereas the expression of epithelial and mesenchymal traits are often anticorrelated, increasing evidence highlights the coexistence of such phenotypes. Indeed, scRNA-seq coupled with trajectory inference analysis of HNSCCs has demonstrated that while some cancer cells show low levels of epithelial and mesenchymal markers, they can transition into a phenotype that simultaneously expresses high levels of epithelial- and mesenchymal-related genes [29]. Additionally, other HNSCC cancer cells can sustain elevated epithelial traits while dynamically regulating the expression of mesenchymal genes [29]. Noteworthy, these and other observations have driven the development of methods that enable estimating the composition of tumor samples analyzed by bulk transcriptomics through a combination of deconvolution and inference of tumor purity [30][31][32][33]. These approaches improve our comprehension of the relevance of cancer cells and stromal components during cancer progression while additionally increasing the accuracy of analyses relying on EMT signatures.
As discussed in this section, very important findings regarding cancer cell EMT originated from studies exploring alterations at the DNA/RNA level. Methodologies such as microarrays, RNA-seq, scRNA-seq, and assay for transposase-accessible chromatin with sequencing (ATAC)-seq can be used in a less biased way to reveal crucial players that must then be further investigated in more restricted conditions using additional controls. Still, however important, RNA shows limited functions and the main phenotypic changes observed during EMT come from protein activity. Thus, using similar EMT models to investigate the proteome of cancer cells is also critical to deepening our understanding of the main drivers involved in this phenotypic transition.

3. Proteomics Translated from Bench-to-Bedside

Characterizing specific protein functions has always been greatly important in cancer research due to the multiple roles these molecules can play in distinct cellular processes. Changes in protein level, localization, and activity—which are regulated by post-translational modifications and interaction with other molecules—largely affect EMT. However, similar to studying alterations in mRNA levels, focusing on one or a few proteins may be detrimental to portraying the complex scenario that involves EMT. Proteome studies help to overcome this limitation by reducing bias while analyzing this process. Moreover, if integrated into the transcriptome and epigenome analysis, proteomics can be used to identify crucial biomarkers and molecular pathways not only altered as a consequence of EMT but also responsible for driving this molecular program.

3.1. Using In Vitro Models to Analyze the Proteome of Cancer Cells Undergoing EMT

As previously discussed regarding the characterization of cancer cell epigenome or transcriptome, the use of cell cultures as models helps us to understand how individual or combined signaling pathways are altered and/or alter cancer cell EMT. Again, strategies employed for this purpose mainly include the induction of cancer cell EMT by stimulation with EMT-related growth factors or ectopic expression of EMT-inducers.
It is interesting to note that many studies evaluating alterations in the proteome of cells undergoing EMT frequently report cytoskeleton and other structural proteins among the main molecules differentially expressed. As discussed before, while a proteome-based investigation enables the broadest identification of global changes, the sensitivity of most methods is still low if compared with techniques used to evaluate modifications at the DNA/RNA level. Therefore, improving the sensitivity of these methods is a notorious goal for most studies that attempt to reveal the proteome of cancer cells or patient samples, and the analysis of sub-proteomes can help to achieve this aim.

3.2. Looking towards New EMT Biomarkers in Primary Tumors by Using Proteomics

Although in vitro models are useful for exploring specific mechanisms that drive or block the EMT process, they usually lack natural intratumor and intertumor heterogeneity otherwise observed in real cancers. In addition, representing the interaction between cancer cells and non-cancer cells within the tumor microenvironment is a complex task. Therefore, characterizing the proteome of only one cell type may also mislead the interpretation of its real significance. In this context, the best approach to understanding the contribution of the EMT to the progression of real tumors may demand a careful analysis of the proteome of tumor samples. Still, as confounding factors might be more difficult to isolate in this scenario than in vitro, additional considerations should be kept in mind, such as the existence of distinctive molecular subtypes and clinicopathological characteristics for the patients included in the study.
Considering that most proteomic techniques are very expensive and time-consuming, studies aiming to evaluate the proteome of human cancers would hardly be designed to exclusively evaluate EMT-related effectors or biomarkers. Otherwise, such proteins may eventually emerge among the set of differentially expressed molecules, particularly considering their well-established relevance in cancer invasion and metastasis, as discussed before. For instance, Moreira et al. (2004) have observed this pattern of differentially expressed proteins when comparing the proteome of bladder specimens derived from normal tissues and transitional cell carcinomas (TCCs) [34]. Sixty percent of the tumors analyzed expressed high levels of Vimentin and PGP9.5 [also known as Ubiquitin C-terminal hydrolase L1 (UCHL1)], suggesting the presence of cancer cells undergoing EMT [34]. Additionally, invasive tumors showed lower levels of the epithelial protein 14-3-3σ (also known as Stratifin) than normal tissues and non-invasive tumors [34]. Interestingly, the evaluation of tumors exhibiting heterogeneous staining for 14-3-3σ demonstrated the progressive loss of its expression, with noteworthy negative staining in invasive areas [34]. Similarly, Sun and colleagues reported that the mesenchymal marker Vimentin was consistently overexpressed in hepatocellular carcinomas (HCCs) compared with cirrhotic and normal liver tissues, reinforcing the association between EMT and cancer progression [35].
As discussed before for in vitro studies, the presence of highly expressed proteins can mask the presence of less abundant molecules able to play critical roles in the biological process evaluated. Although sub-proteomes of human tumors have not been frequently evaluated, some examples highlight their relevance. The analysis of the cytosolic fraction of BCs, for example, associated higher levels of Ferritin light chain (FTL) with reduced metastasis-free survival [36][37]. Interestingly, histological analyses revealed that FTL was mostly expressed by stromal cells and its levels were correlated with the expression of CD138 (also known as Syndecan) [36]. Because CD138 is a typical mesenchymal marker, its expression in both stromal cells and cancer cells suggested the occurrence of EMT in at least part of these breast cancers [36].
Altogether, these studies confirm that many EMT-related proteins identified in vitro show potential use as biomarkers for different types of cancer, being associated with cancer recurrence, lymph node metastasis, and distant metastasis. Moreover, while important EMT-related alterations have been characterized in vitro, many proteins are regulated by the interactions between cancer cells and stromal cells. This observation reinforces the importance of integrative studies analyzing the proteome of immortalized cancer cells and human cancer samples. Still, because analyzing proteins from tumor samples demands highly invasive approaches to obtain biopsies and resected samples, this may be a problem when monitoring cancer patients before/after treatment. In the next section, studies focused on overcoming this limitation by investigating and establishing biomarkers in biological fluids are discussed.

3.3. Biological Fluids: An Easier Access to EMT-Related Biomarkers

The analysis of tumor samples, particularly their proteomes, requires invasive procedures to obtain enough tissue to detect low-abundance proteins. Otherwise, the evaluation of biomarkers in biological fluids such as blood, saliva, and urine requires less invasive methodologies that enable a closer and easier follow-up of the patients. Moreover, analysis of patient-derived data obtained over time (e.g., before, during, and after therapeutic intervention) may help to more accurately characterize the dynamic networks that regulate such a fluid process as the EMT.
Aiming to establish biomarkers for bladder cancers, research led by Celis [38] and Ostergaard [39] reported increased levels of Psoriasin (also known as S100A7) in squamous cell carcinomas (SCCs) and urine samples from cancer patients. Interestingly, Psoriasin was not observed among the serum proteins of SCC patients, indicating its specific use as a biomarker to be screened in the urine of these patients [38][39]. In another example, Sun and colleagues reported Vimentin as a sensitive and specific biomarker when analyzing the proteome of serum samples from HCC patients and non-neoplastic controls [35]. In this study, Vimentin levels were used to distinguish even patients with small HCCs (<2 cm) from non-neoplastic controls [35].
Whereas the detection of cancer proteins in biological fluids shows clear benefits, it also incurs a methodological issue associated with the presence of highly expressed proteins that may mask less abundant molecules. To improve the detection of relevant molecules, particularly from serum and plasma samples, the depletion of excessively abundant proteins is recommended. In BC patients, proteomic analysis of albumin-depleted serum samples demonstrated the association between LN metastasis and several proteins related to the cytoskeleton and ECM structure or remodeling, including collagen α4I, Serpin C1, Fibrinogen gamma chain (FGG), and Tenascin XB (TNXB) [40]. Moreover, in this study, TNXB was detected in the serum samples of all patients diagnosed with benign breast diseases and LN-negative cancer patients, but not in LN-positive patients, indicating that loss of circulating TNXB could be used as a biomarker of LN metastasis [40].
Several cancer types lack specific diagnostic or prognostic biomarkers. Even for cancers better characterized (e.g., breast, colorectal, and lung cancers), only a small number of biomarkers exist, and their use is restricted by reduced sensitivity and specificity. Moreover, many different conditions with clinical relevance cannot be determined by current biomarkers, including the progression toward resistance to anti-cancer therapies and the development of locoregional and distant recurrence. As observed by the studies discussed here, EMT-related proteins could be used as cancer biomarkers, particularly if considering their typical association with cancer progression and metastasis. Still, the reduced number of publications evaluating cohorts specifically grouped according to EMT-associated outcomes limits the generalization of the conclusions obtained. For instance, additional proteome-based studies including patients who progressed to LN metastasis or distant metastasis, may confirm the reliability of EMT-related proteins for this purpose. Investigations focused on recurrence and resistance to therapy have also been overlooked, and cancer types with lower incidence have been often ignored in this kind of evaluation. In addition to an experimental design focused on EMT-associated modifications, improved technologies may allow the characterization of cancer protein profiles, as has been currently performed for the characterization of cancer transcriptomes.

4. Integration of Multiomics and Spatio-Temporal Analyses for a Comprehensive Understanding of EMT-Driven Cancer Progression

In contrast to studying the changes at the DNA/RNA level, current proteomic methods cannot satisfactorily cover the entire diversity of proteins within cancer cells or the tumor mass. Additionally, evaluating the protein profile of multiple samples is highly expensive and time-consuming. These and other reasons led to a better understanding of how modifications in the epigenome and transcriptome impact biological processes while depicting global changes in protein levels was left behind. But to what extent should we rely on a single omics when translating EMT-related findings to the clinic? Although several studies individually demonstrated global alterations either in the epigenome, the transcriptome, or the proteome of cells undergoing EMT, the direct comparison of these results is still an issue. Besides analyzing different cancer types, in vitro studies frequently induce EMT by exploring only one EMT inducer at a time. In addition to differences across studies, this strategy limits the investigation of possible crosstalk between multiple signaling pathways. Therefore, multiomics may provide a more reliable source for the comparison of the many regulatory mechanisms impacting cancer cells at multiple levels during EMT—particularly when analyzing cancer patient samples that are inherently affected by several EMT regulators.
In fact, seminal studies published throughout the last decade have already begun to adopt multiomics as an approach to analyze alterations in cancer samples that simultaneously impact DNA, RNA, and protein levels. Although most of these studies investigated different cancer types, some similarities are worth mentioning. Among them is the common divergence between transcriptome- or proteome-derived data, such as that reported in colorectal [41], breast [42][43], ovarian [44], gastric [45], and lung [46] cancers. Besides methodological parameters influencing this correlation, spatio-temporal changes in RNA species largely affect their localization and availability for translation, thus, impacting protein levels [47][48][49][50]. Importantly, this effect is reported to change in pathological conditions, being increased in cancers when compared with normal tissues, and particularly enhanced with cancer progression [47]. Moreover, copy number alterations (CNAs) and post-translational modifications are also shown to significantly impact gene expression in a way that is not necessarily translated into protein modifications [42][46]. In addition to reinforcing an important difference in the mechanisms that impact cancers at the molecular level, this analytical divergence has a profound impact on the ability to determine patient prognosis. Remarkably, depending on the method of choice, patients showing significantly different probabilities of survival cannot be distinguished by such omic analysis. For example, in CRCs, only a proteome-based clustering—but not other types of analysis—revealed a typical EMT signature correlated with poor prognosis [41]. Furthermore, the identification of a molecular subtype associated with cell invasion and poor survival rate in early-onset gastric cancers (EOGCs) required an integrative clustering using global mRNA, proteome, phosphoproteome, and N-glycoproteome [45]. This observation reinforces a critical problem as the use of single omic methods may not suffice to accurately describe the myriad of alterations within a tumor, therefore, representing an obvious issue in determining therapeutic approaches.
Although the integration of multiple omics helps to overcome limitations otherwise imposed by the individual use of each approach, spatial alterations are often masked in bulk analysis, and detecting temporal modifications remains unfeasible—especially considering a clinical context. As mentioned before, the increasing association of deconvolution strategies and transcriptomic-focused methods with single-cell resolution has been instrumental in depicting the contribution of different tumor compartments regarding EMT-related alterations. Tissue microdissection also partially helps to overcome this limitation to individually investigate molecular signatures associated with either tumor epithelium or stroma. For instance, in microdissected prostate cancers, a gradual decline in phosphorylated (p-) Mitogen-activated protein kinase (ERK) levels and concurrent increase in p-AKT levels have been associated with cancer progression [51]. Similar results were also reported in microdissected CRCs, where decreased p-ERK and p-p38 levels were observed in cancer tissues compared with uninvolved mucosa [52]. Thus, integrating tissue microdissection and tissue microarray may increase the understanding of spatial modifications otherwise overlooked by the analysis of bulk samples.
Further, a combined approach can also help to characterize alterations in rare samples that are not located within the cancer mass but have been shed by the tumor and may be scattered throughout the body, such as CTCs. For instance, in a study simulating CTCs by spiking immortalized cancer cells into blood samples, 4000 proteins were identified by one-dimensional high-resolution porous layer open tube-liquid chromatography (LC)-MS in samples spiked with 100–200 MCF7 breast cancer cells [53]. Impressive results were also described by using nano-LC-MS for the analysis of 1–5 LNCaP prostate cancer cells spiked and recovered from blood samples [54]. In HNSCC patient samples, the use of mass cytometry and unsupervised clustering allowed the identification of epithelial and EMT sub-groups of CTCs, where the latter accounted for more than 80% of all CTCs [55]. Interestingly, the expression of immune checkpoint proteins (e.g., PD-L1 and CTLA4) was lower in CTCs with an EMT phenotype when compared with epithelial counterparts [55]. Further, analysis of molecular pathway activity revealed that CTCs expressing EMT traits were also enriched in p-CREB and p-ERK proteins, but showed reduced levels of other intracellular effectors, such as p-STAT3, p-STAT5, p-PARP, and p-AKT [55]. Establishing the profile and significance of immune checkpoints and intracellular effectors in CTCs may have a profound impact on the development of therapeutic strategies focused on overcoming metastatic progression and resistance to therapy. Noteworthy, while methodological improvement is still required to analyze the proteome of patient CTCs, innovative studies have already begun to characterize the genome, transcriptome, and metabolome of these shedded cells [56][57][58][59][60][61][62][63][64][65][66]. Such analyses have not only helped to elucidate how mutations and molecular programs impact the dissemination of cancer cells but have also validated the perspective of employing a multiomic strategy to improve our knowledge of cancer progression.
As for CTCs, few studies have comprehensively investigated the protein profile of EVs isolated from cancer patient biofluids. Nevertheless, initial studies in circulating EVs have already demonstrated an association between HCC progression and increased Galectin-3-binding protein (LG3BP) levels [67]. In CRC, Transferrin receptor protein 1 (TFR1) was reported to be enriched in circulating EVs from non-metastatic patients [68]. In BC, the establishment of protein signatures for circulating EVs (including EGFR, p-cadherin, and fibronectin) enabled differentiating cancer patients from healthy subjects and was further associated with cancer progression, and relapse [69]. Moreover, while the isolation and characterization of EVs from biofluids remains challenging, the development of microfluidic devices for the isolation and enrichment of such membranous particles brings interesting possibilities for the diagnosis and monitoring of cancer patients. For example, it has been recently reported that the analysis of epithelial and mesenchymal markers on plasma EVs captured by microfluidic devices can be successfully used to establish the prognosis of patients with pancreatic cystic lesions [70]. This strategy is particularly important as it uses tumor-derived EVs to monitor EMT dynamics in pancreatic cells and further inform on whether these patients may or may not undergo surgery [70]. Similarly, quantification of the EMT markers in melanoma-EVs through microfluidic devices has been described as an innovative strategy to monitor disease progression. In this context, increased levels of mesenchymal markers (N-cadherin and ABCB5) compared to epithelial markers (E-cadherin and THBS1) characterized a shift in the serum EVs of melanoma patients that was also correlated with the development of drug resistance [71]. Overall, although limited in number, the significance of these studies is remarkable and may be increased if combined with those where DNA and RNA species transported by cancer patient EVs are analyzed and also associated with diagnostic or prognostic potential [72][73][74]. Furthermore, since EVs and CTCs can both be obtained from blood samples and separated based on physicochemical properties, new methods are emerging to optimize their sequential isolation and analysis in a parallelized multidimensional analytic framework [75][76]. Importantly, such methods must not be understood simplistically as additional strategies for the discovery of EMT-related biomarkers. Rather, innovations improving the analysis of rare samples in liquid biopsies (e.g., CTCs and EVs) are paramount to generate a holistic view of the signaling pathways underlying EMT while also providing information on its dynamic regulation during metastasis. In this scenario, biomarkers emerge from an in-depth understanding of the molecular machinery that drives disease progression. Consequently, the translation of such biomarkers into clinical practice will improve existing diagnostic and monitoring methods due to enhanced specificity and sensitivity.

This entry is adapted from the peer-reviewed paper 10.3390/cells12232740

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