A surface coating of anti-EpCAM antibody enables the microfluidic CTC-Chip to capture EpCAM+ cells in its channel while eliminating those that are negative under precisely controlled laminar flow conditions. Etched in silicon and no larger than a standard microscope slide [25], the CTC-Chip contains an array of microposts functionalized with anti-EpCAM antibodies and a pneumatic pump to establish flow, all enclosed by a manifold. Once a blood sample has been pumped through, the microchip is gradually flushed with PBS to remove any nonspecifically bound cells. To identify CTCs, the microchip is then stained with DAPI, pan-cytokeratins (1, 4, 5, 6, 8, 10, 13, 18, and 19), and CD45 using immunocytochemistry. Cells meeting the morphological characteristics of malignant tumor cells (such as cell size, shape, and nuclear size) and positive for cytokeratins are considered CTCs. Assessment of cell membrane integrity following this method showed substantial viability (98.5 ± 2.3%). Additionally, the CTC-Chip captures cells with low EpCAM expression as efficiently as cells with high expression. The CTC-Chip successfully identified CTCs in the peripheral blood of patients with metastatic disease in 115 of 116 (99%) samples, with a range of 5–1281 CTCs per ml [26].

#### *2.1.4. MagSweeper*

immunomagnetically labeled EpCAM+

122 Breast Cancer - From Biology to Medicine

*2.1.2. Adnatest (AdnaGen AG)*

/MUC1+

tion step, EpCAM+

*2.1.3. CTC-Chip*

EpCAM+

cells are concentrated using an external magnetic field.

Finally, the immunomagnetically labeled cells are analyzed using either the CellSpotter Analyzer or the CellTracks Analyser II which examine cell morphology and staining patterns for CTC confirmation [17, 18]. The CTC criteria used in these methods are (1) an intact cell with a round to oval morphology and at least 4 μm in size; (2) positive for DAPI with a nucleus inside the cytoplasm (of at least >50%) and a nucleus area smaller than the cytoplasm, and (3) positive for cytokeratins and negative for CD45 [19]. One advantage of the CellSearch® System is that blood is collected in a CellSave tube which contains a mixed fixative. CTCs remain stable for 96 hours and can be transported at room temperature for later analysis. Since the CellSearch® system is semiautomated, the

Using CellSearch®, Cristofanilli et al. were the first to report that circulating epithelial cells are rare in healthy women and those with benign tumors (0.1 ± 0.2 per 7.5 ml blood). Additionally, they found 5 CTCs per 7.5 ml blood to be a reliable cutoff for the prediction of patient survival among women with malignant breast tumors. A study of 517 breast cancer patients showed that patients at or above this cutoff had a shorter median PFS (2.7 months vs. 7.0 months; *p* < 0.001) and shorter overall survival (OS) (10.1 months vs. >18.0 months; *p* < 0.001) when compared to those with fewer than 5 CTCs. These data demonstrate that the number of CTCs prior to treat-

Adnatest is an immunomolecular assay that combines immunomagnetic-based enrichment with multiplex reverse transcription polymerase chain reaction (RT-PCR). In the initial isola-

tified by tumor-associated gene expression [20–22]. Since phenotypic changes occur in cancer cells throughout the disease course and in response to therapies, cancer cells in the primary tumor as well as in the circulation are diverse in their gene expression. Considering the heterogeneity of cancer cells, the most prominent advantage of Adnatest is that it allows use of a variety of antibody-based selection markers, thereby minimizing false negative and false positive results. Bredemeier et al. enriched CTCs in 62 MBC patients using immunomagnetic beads that target EpCAM, epithelial growth factor receptor (EGFR), and HER2. The enriched CTCs were then characterized by their expression of tumor-related genes using a multiplex qPCR assay (AdnaTest EMT-2/StemCellDetect™). Using this approach, authors of the study established a panel of nine genes able to identify differential expression of each phenotype— epithelial (*EpCAM*), EMT (*PIK3CA, AKT2*), stem cell (*ALDH1*), drug resistant (*ERCC1, AURKA*), receptor positive (*ERBB2, ERBB3,* and *EGFR*), and leukocyte control (*CD45*) [23]. Adnatest is capable of detecting as few as 2 CTCs in 5 ml of blood [20, 22] and in a comparative study showed greater sensitivity than the CellSearch® system in detecting CTCs (53 vs.

A surface coating of anti-EpCAM antibody enables the microfluidic CTC-Chip to capture

cells in its channel while eliminating those that are negative under precisely controlled

CTCs are enriched using magnetized antibodies and further iden-

reproducibility of this study is high with minimal inter- or intrareader variability.

ment is an independent predictor of PFS and OS in patients with MBC [7].

47% CTC positive, respectively, in a sample of 55 MBC patients) [24].

The MagSweeper is another EpCAM-based immunomagnetic cell separator. It uses a round-bottom, magnetic rod covered with an ultrathin (25 μm) nonadherent plastic sheath. This assembly is robotically swept through a well containing a blood sample labeled with anti-EpCAM functionalized paramagnetic beads (CELLection Epithelial Enrich Dynabeads: Invitrogen). The EpCAM+ cells captured on the covered magnetic rod (MagSweeper) are transferred to and washed in a well containing PBS, then released into another well of PBS by removing the magnetic rod from its sheath. Finally, EpCAM+ cells are further confirmed by morphology and gene expression profiles [27, 28]. The gene expression profiles of MCF7 cells incubated with anti-EpCAM magnetic beads before and after MagSweeper isolation were analyzed using microarray analysis and compared with a similar number of MCF7 cells grown in culture media. Statistical analysis indicated that the MagSweeper isolation process does not induce any significant perturbation in the gene expression profile of cells. Additionally, the use of 4.5-μm magnetic beads permits isolation of target EpCAM+ cells, even with single-bead attachment, making the procedure suitable for isolation of CTCs with moderate-to-low EpCAM expression. However, the attachment of large magnetic beads to the cell surface may interfere with certain applications. An additional drawback is that normal EpCAM+ cells present in the circulation are also potentially selected and need to be distinguished from CTCs at a microscopic level. MagSweeper technology succeeded in isolating CTCs from all MBC patients (*n* = 47) at an average of 12 ± 23 CTCs per 9 ml of blood, while no CTCs were found in samples derived from healthy donors [28].

#### *2.1.5. Vita-Assay™ (Vitatex Inc.)*

The Vita-Assay™ is a functional assay-based CTC enrichment method that takes advantage of invasive CTCs preferential adhesion to the cell adhesion matrix (CAM). Viable, invasive CTCs are captured on a plate coated with CAM-mimic, then further identified based on their proclivity to degrade and ingest the extracellular matrix. CTCs adhered to the Vita-Assay™ plate are released by the addition of an enzyme that dissolves the CAM coating, and then concentrated by centrifugation for cytologic analysis. This application's criteria for CTCs include positive CAM uptake (CAM+ ) and negative hematopoietic lineage (HL) marker expression. Enumeration of CTCs by flow cytometry may be further validated by microscopic comparison of CTC and immune cell morphology. The Vita-Assay™ CTC-enrichment platform is capable of enriching rare and invasive CTCs and is not biased by surface markers, morphology, or size. Rather, since it focuses on the adhesive properties of CTCs, it offers a potentially robust capture method for invasive CTCs. Additionally, Vita-Assay™ allows for sensitive multiplex flow cytometric and microscopic detection of CTCs. Vita-Assay™ successfully detected CTCs in all blood samples from MBC patients (n = 10) with a range of 18–256 CTCs per ml. Moreover, CTCs were detected in blood samples of 28 of 54 (52%) stage I–III breast cancer patients with a mean count of 61 CTCs per ml [29].

#### **2.2. Isolation based on physicochemical properties of CTCs**

Several isolation methods take advantage of differences between the physicochemical properties of CTCs and other circulating cells. Enrichment methods have been developed for properties including size, density, and surface charge [30]. For example, the well-documented fact that carcinoma cells have larger overall size and denser nuclei than normal epithelial and immune cells has been adopted for CTC isolation [31, 32]. Similarly, nuclear condensation of carcinoma cells has led to the development of density-based CTC enrichment [33]. Lastly, differential surface charges between carcinoma and normal epithelial or immune cells are also a strategy used for CTC enrichment [34]. Enrichment strategies based on physiochemical properties have emerged in order to minimize bias (i.e., exclusion of non-EpCAM+ cells); thus, the sensitivity of CTC isolation using these methods is high. However, their specificity is not always high due to the difficulty of completely eliminating potential leukocyte contamination during the enrichment step. Most of these methods rely on manual cytopathologic identification of CTCs, a highly laborious process with varied reproducibility depending on the pathologist. Despite this, the potential for versatile applications as well as live CTC collection remains major strengths of this method class.

#### *2.2.1. Size and density*

Since CTCs are larger in size than immune or red blood cells, two commercially available methods have used this principle to enrich CTCs. Using negative pressure or gravity, cells with diameter greater than the 6.5–8.0-μm pores are captured on porous membranes. This results in the acquisition of multiple types of cells, including CTCs, leukocytes, fibroblasts, normal epithelial, and endothelial cells. CTCs are then distinguished from immune cells by immunostaining and morphology. Although, different types of staining methods have been explored for reproducibility, no standard staining method for CTCs has been established thus far.

#### *<IP_ADDRESS>. RareCells® system (Rarecells)*

The RareCells® system allows performance of the Isolation by Size of Epithelial Tumor Cells test (ISET® test). The ISET® test enriches CTCs according to their size and subsequently identifies them based on their cytopathologic features. The RareCells® system is a negative depressionbased filtration device. It consists of a 10-well filtration module that captures CTCs on a polycarbonate Track-Etch-type porous membrane [35–37]. Following red blood cell rupture and mild fixation, circulating cells smaller than 8 μm are filtered through the porous membrane, while those of a greater diameter are enriched on the membrane. The membranes may subsequently be stained for the detection of CTCs or stored for future analysis. The RareCells® system allows for versatile applications, including both fixed and live CTC collection. Using ISET method, Hofman et al. conducted a blinded, multicenter study to assess the feasibility of CTC identification using cytopathologic criteria. CTCs were defined as circulating nonhematologic cells exhibiting at least 4 of the following criteria: irregular nuclei, anisonucleosis (ratio >0.5), high nuclear/ cytoplasmic ratio, nuclei larger than 24 μm, or the presence of tridimensional sheets. Based on these criteria, CTCs were only detected in the blood of patients with malignant disease and were absent in healthy subjects [38]. The RareCells® system has shown successful detection of CTCs in breast cancer, melanoma, and nonsmall cell lung cancer cases [37, 39–41]. In a comparison between the RareCells® system and CellSearch®, the RareCells® system displayed greater sensitivity of detection (93% vs. 40%, respectively) and yielded higher median CTC counts [42].

#### *<IP_ADDRESS>. OncoQuick® tube (Greiner Bio-One)*

This separation device is composed of a centrifugation tube containing a liquid density separation medium and porous barrier membrane optimized for the enrichment of CTCs from blood. During the enrichment step, blood is layered on top of the gradient and then centrifuged. CTCs are enriched in the fluid above the porous barrier and collected in a tube by centrifugation. Following immunocytochemistry, CTCs are identified as cytokeratin-positive (7, 8, and 18) and CD45 negative with intact nuclei and an increased nuclear-cytoplasmic ratio [43, 44]. The purity and efficacy of CTC enrichment using OncoQuick® are higher than that with Ficoll, which traps up to 25 times more blood mononuclear cells [45]. However, detection sensitivity using OncoQuick® was found to be lower than CellSearch® (23% vs. 54%; *p* < 0.001) [43].

#### *2.2.2. Electrical properties*

flow cytometry may be further validated by microscopic comparison of CTC and immune cell morphology. The Vita-Assay™ CTC-enrichment platform is capable of enriching rare and invasive CTCs and is not biased by surface markers, morphology, or size. Rather, since it focuses on the adhesive properties of CTCs, it offers a potentially robust capture method for invasive CTCs. Additionally, Vita-Assay™ allows for sensitive multiplex flow cytometric and microscopic detection of CTCs. Vita-Assay™ successfully detected CTCs in all blood samples from MBC patients (n = 10) with a range of 18–256 CTCs per ml. Moreover, CTCs were detected in blood samples of

28 of 54 (52%) stage I–III breast cancer patients with a mean count of 61 CTCs per ml [29].

Several isolation methods take advantage of differences between the physicochemical properties of CTCs and other circulating cells. Enrichment methods have been developed for properties including size, density, and surface charge [30]. For example, the well-documented fact that carcinoma cells have larger overall size and denser nuclei than normal epithelial and immune cells has been adopted for CTC isolation [31, 32]. Similarly, nuclear condensation of carcinoma cells has led to the development of density-based CTC enrichment [33]. Lastly, differential surface charges between carcinoma and normal epithelial or immune cells are also a strategy used for CTC enrichment [34]. Enrichment strategies based on physiochemical properties have emerged

lation using these methods is high. However, their specificity is not always high due to the difficulty of completely eliminating potential leukocyte contamination during the enrichment step. Most of these methods rely on manual cytopathologic identification of CTCs, a highly laborious process with varied reproducibility depending on the pathologist. Despite this, the potential for versatile applications as well as live CTC collection remains major strengths of this method class.

Since CTCs are larger in size than immune or red blood cells, two commercially available methods have used this principle to enrich CTCs. Using negative pressure or gravity, cells with diameter greater than the 6.5–8.0-μm pores are captured on porous membranes. This results in the acquisition of multiple types of cells, including CTCs, leukocytes, fibroblasts, normal epithelial, and endothelial cells. CTCs are then distinguished from immune cells by immunostaining and morphology. Although, different types of staining methods have been explored for

The RareCells® system allows performance of the Isolation by Size of Epithelial Tumor Cells test (ISET® test). The ISET® test enriches CTCs according to their size and subsequently identifies them based on their cytopathologic features. The RareCells® system is a negative depressionbased filtration device. It consists of a 10-well filtration module that captures CTCs on a polycarbonate Track-Etch-type porous membrane [35–37]. Following red blood cell rupture and mild fixation, circulating cells smaller than 8 μm are filtered through the porous membrane, while those of a greater diameter are enriched on the membrane. The membranes may subsequently be stained for the detection of CTCs or stored for future analysis. The RareCells® system allows

reproducibility, no standard staining method for CTCs has been established thus far.

cells); thus, the sensitivity of CTC iso-

**2.2. Isolation based on physicochemical properties of CTCs**

in order to minimize bias (i.e., exclusion of non-EpCAM+

*2.2.1. Size and density*

124 Breast Cancer - From Biology to Medicine

*<IP_ADDRESS>. RareCells® system (Rarecells)*

CTCs have a unique surface charge that distinguishes them from other cells. Thus, a dielectrophoretic flow field can be used to fractionate CTCs from blood cells based on their differential electrical properties [34].

#### *<IP_ADDRESS>. DEPArray™ technology (Silicon Biosystems)*

Utilizing this principle, DEPArray™ is an automated instrument that can identify, quantify, and recover individual rare cells. It is used as a second purification step after initial EpCAMbased CTC enrichment methods. The individually isolated CTCs using DEPArray™ are then identified based on their morphological and immunocytochemical features. The system includes the DEPArray™ cartridge and DEPArray™ analysis platform. The single-use, microfluidic cartridge contains an array of individually controllable electrodes, each with embedded sensors. This circuitry enables the creation of dielectrophoretic cages around cells. After imaging, individual CTCs are gently moved into the holding chamber for isolation and recovery. The DEPArray™ analysis platform utilizes image-based selection to allow identification and isolation of CTCs on the DEPArray™ cartridge. The system uses a six-channel fluorescent microscope and a CCD camera to capture images and identify cells demonstrating the desired fluorescence labeling and morphological characteristics. The main advantage of DEPArray™ is its ability to eliminate mononuclear cell cross contamination from the preenriched CTC pool. Image-based selection enables the isolation of specific rare cells from other cell types. Moreover, the DEPArray™ system yields high-quality nucleic acids for molecular investigations, since the

**Table 1.** Summary of approaches used for CTC isolation and their relevant devices.

cells receive minimal disturbance during capture or transport. Additionally, the DEPArray™ system allows for the isolation of single cells, making it a promising contributor to the understanding of CTC heterogeneity [46].

 A summary for the CTC methods discussed in this chapter along with other methods is described in **Table 1**.

### **3. Clinical utility of CTCs as a biomarker**

**Detection strategy Detection method Advantages Disadvantages**

IsoFlux NSG applicable

FDA approved

CTC-chip [25] Captured cells are suitable for

isolation

isolation

isolation

ScreenCell® [100] Isolation of live cells and allows

Celsee PREP™ slide Highly efficient CTC detection

specificity

SIM-Chip [99] Single-cell isolation

Quantification of CTCs

Custom design for CTC

Semiautomated system

Allows detection of microclusters of CTCs

Enhanced platform for CTC

Automated immunomagnetic

Allows for gene expression profiling analysis

AdnaTest [20–22] High sensitivity CTCs cannot be

High purity and recovery without cell damage

Single CTC morphological, immunocytological, and genetic analyses

for tissue culture experiments

Immunohistochemistry, DNA, and mRNA analyses Single cell analyses

Automatic imaging system

High quality nucleic acids for molecular investigations (elimination of blood cells cross

contamination)

with high sensitivity and

morphologically Multiplex PCR assays characterized

large monocytes High sensitivity and specificity

molecular analyses High detection rate Quantification of CTCs

Semiautomated system Only epithelial CTCs

are captured and mesenchymal cells are

Possible cell damage

Cross contamination with other rare blood cells such as megakaryocytes and

discarded

CellSearch® System

Herringbone-Chip (HB-Chip) [98]

MagSweeper [27, 28]

[35–37]

Surface charge DEPArray**™** [46] Single cell isolation

[17]

Biological EpCAM

126 Breast Cancer - From Biology to Medicine

expression

EpCAM and MUC1 expression

EpCAM expression and microfluidic

Physicochemical Size RareCells® system

Liquid biopsy is a clinically amenable method to enable real-time and longitudinal disease monitoring. Currently, available serum markers lack the specificity and sensitivity needed for clinical management of breast tumors. Since the detection of CTCs became feasible, a number of clinical studies have undertaken exploration of whether CTCs could provide a new minimally invasive, longitudinal disease monitoring strategy in breast cancer [7, 47]. The ability of CTCs to predict disease progression in both early and MBC as well as in different tumor subtypes is currently under investigation. Moreover, several clinical trials have investigated the use of CTCs as an early therapy response marker. This section discusses the potential use of CTCs as a biomarker for prognosis and therapy response in breast cancer.

#### **3.1. Prediction of prognosis of metastatic breast cancer by CTC**

In a landmark study, Cristofanilli et al. conducted the first large, multi-institutional clinical study to evaluate the utility of CTCs as a predictive biomarker for disease progression in patients with MBC. Using the CellSearch® system, CTCs were enumerated from 177 patients with measurable MBC from 20 clinical centers across the United States. Of these, 49% had ≥5 CTCs per 7.5 ml of blood at baseline prior to treatment. When compared to patients with fewer than 5 CTCs at baseline, these patients had shorter median PFS (2.7 months vs. 7.0 months; *p* < 0.001) and OS (10.1 months vs. >18 months; *p* < 0.001). After 4 months, 10 of the 177 patients had died, each showing an average of 3000 CTCs per 7.5 ml of blood at baseline [7]. This data clearly indicated that the presence of CTCs is strongly associated with poor outcomes in MBC patients. When another study, conducted by Nakamura et al., examined the correlation between CTC count and OS, increased risk was found for patients with higher pretreatment counts Hazard Ratio (HR): 2.4 for patients with 5–10 CTCs; (95% CI, 0.72–8.24, *p* = 0.1481); HR: 13.95 for patients with 21–100 CTCs; (95% CI, 4.57–42.55, *p* < 0.0001). Furthermore, when the OS of patients with ≥5 CTCs was compared to those with <5 CTCs, the HR was 3.1 times higher (95% CI, 1.49–6.29, *p* = 0.002) [47]. This relationship has been further confirmed by both meta- and pooled analyses [48–50]. Comprehensive meta-analysis of 49 studies, including a total of 6825 patients (Early stage [M0, *n* = 2993], metastatic [M1, *n* = 3069], and pooled patients with I–IV stages [*n* = 763]) found that CTC count was significantly associated with shorter PFS and OS in both early stage and MBC patients (HR: 1.78; 95% CI, 1.52–2.09, *p* < 0.001 for PFS; HR: 2.33; 95% CI, 2.09–2.60, *p* < 0.001 for OS in MBC patients) [48]. Additionally, pooled analysis of 20 studies from 17 European breast cancer centers found similar trends. In this cohort of 1944 MBC patients, 46.9% had a CTC count of ≥5 CTCs per 7.5 ml at baseline, which was associated with both decreased PFS (HR: 1.92; 95% CI, 1.73–2.14, *p* < 0.0001) and OS (HR: 2.78; 95%CI, 2.42–3.19, *p* < 0.0001) compared to those with <5 CTCs [49]. In another recent meta-analysis of 24 studies including 3701 MBC patients, in 1 and 2 years PFS and OS rates (respectively), higher CTC counts correlated with shorter PFS [50].

Since the studies included in these analyses varied in CTC detection method and time point of blood draw, Zhang et al. evaluated whether these differences affect the prognostic value of CTC counts. Using a subgroup analysis stratified by detection method and time point of blood sampling, their meta-analysis found that the prognostic value of CTCs in PFS was significant in studies using RT-PCR (HR: 2.58; 95% CI, 1.99–3.35) or CellSearch® methodologies (HR: 1.85; 95% CI, 1.53–2.25). This demonstrated that CTCs are a reliable prognostic marker from predose/preoperative blood in patients with MBC, regardless of other differences in study design [51]. Similarly, CTC counts have also been shown to be a prognostic marker in the postdose blood of MBC patients receiving their first cycle of first-line treatment [7, 52].

Together, these studies lay the foundation for CTCs as a valid and reliable prognostic indicator both before and after breast cancer treatment. Given their promising clinical application, the question of whether CTCs are superior to other prognostic factors has also been addressed. A multivariate, Cox proportional hazards regression analysis showed that CTC count at baseline was the most significant predictor of both PFS and OS, regardless of histology grade, recurrence, de novo stage IV breast cancer, or hormone and HER2 receptor status. Moreover, in a retrospective analysis of MBC patients, comparison of the prognostic significance of CTCs with tumor burden, therapy type, and receptor subtype showed that CTCs were an independent predictor of prognosis [7, 53].

#### **3.2. Prediction of prognosis of nonmetastatic breast cancer by CTCs**

As mediators of metastasis, CTCs' presence and role in advanced breast cancer cases have received much attention. However, their implications in nonmetastatic cases have been a focus of investigation as well. Using mRNA expression of cytokeratin 19 (CK19) to identify CTCs, Stathopoulou et al. detected CTCs in the blood of 148 patients with operable breast cancer prior to initiation of adjuvant therapy. For stage I and II breast cancer, the presence of CK19+ , CTCs was an independent prognostic factor associated with early relapse (*p* < 0.001) and disease-related death (*p* = 0.01) during a median follow-up of 28 months [54]. Using the same approach, Xenedis et al. analyzed 167 node-negative breast cancer patients before the initiation of therapy and found 21.6% of the patients had CK19+ CTCs, which were associated with early relapse (*p* < 0.001) and disease-related death (*p* < 0.008). Multivariate analysis further confirmed the detection of CK19<sup>+</sup> CTCs in the blood of node-negative patients is an independent prognostic factor [55].

counts Hazard Ratio (HR): 2.4 for patients with 5–10 CTCs; (95% CI, 0.72–8.24, *p* = 0.1481); HR: 13.95 for patients with 21–100 CTCs; (95% CI, 4.57–42.55, *p* < 0.0001). Furthermore, when the OS of patients with ≥5 CTCs was compared to those with <5 CTCs, the HR was 3.1 times higher (95% CI, 1.49–6.29, *p* = 0.002) [47]. This relationship has been further confirmed by both meta- and pooled analyses [48–50]. Comprehensive meta-analysis of 49 studies, including a total of 6825 patients (Early stage [M0, *n* = 2993], metastatic [M1, *n* = 3069], and pooled patients with I–IV stages [*n* = 763]) found that CTC count was significantly associated with shorter PFS and OS in both early stage and MBC patients (HR: 1.78; 95% CI, 1.52–2.09, *p* < 0.001 for PFS; HR: 2.33; 95% CI, 2.09–2.60, *p* < 0.001 for OS in MBC patients) [48]. Additionally, pooled analysis of 20 studies from 17 European breast cancer centers found similar trends. In this cohort of 1944 MBC patients, 46.9% had a CTC count of ≥5 CTCs per 7.5 ml at baseline, which was associated with both decreased PFS (HR: 1.92; 95% CI, 1.73–2.14, *p* < 0.0001) and OS (HR: 2.78; 95%CI, 2.42–3.19, *p* < 0.0001) compared to those with <5 CTCs [49]. In another recent meta-analysis of 24 studies including 3701 MBC patients, in 1 and 2 years PFS and OS rates

Since the studies included in these analyses varied in CTC detection method and time point of blood draw, Zhang et al. evaluated whether these differences affect the prognostic value of CTC counts. Using a subgroup analysis stratified by detection method and time point of blood sampling, their meta-analysis found that the prognostic value of CTCs in PFS was significant in studies using RT-PCR (HR: 2.58; 95% CI, 1.99–3.35) or CellSearch® methodologies (HR: 1.85; 95% CI, 1.53–2.25). This demonstrated that CTCs are a reliable prognostic marker from predose/preoperative blood in patients with MBC, regardless of other differences in study design [51]. Similarly, CTC counts have also been shown to be a prognostic marker in the postdose

Together, these studies lay the foundation for CTCs as a valid and reliable prognostic indicator both before and after breast cancer treatment. Given their promising clinical application, the question of whether CTCs are superior to other prognostic factors has also been addressed. A multivariate, Cox proportional hazards regression analysis showed that CTC count at baseline was the most significant predictor of both PFS and OS, regardless of histology grade, recurrence, de novo stage IV breast cancer, or hormone and HER2 receptor status. Moreover, in a retrospective analysis of MBC patients, comparison of the prognostic significance of CTCs with tumor burden, therapy type, and receptor subtype showed that CTCs

As mediators of metastasis, CTCs' presence and role in advanced breast cancer cases have received much attention. However, their implications in nonmetastatic cases have been a focus of investigation as well. Using mRNA expression of cytokeratin 19 (CK19) to identify CTCs, Stathopoulou et al. detected CTCs in the blood of 148 patients with operable breast cancer prior to initiation of adjuvant therapy. For stage I and II breast cancer, the presence of

, CTCs was an independent prognostic factor associated with early relapse (*p* < 0.001) and disease-related death (*p* = 0.01) during a median follow-up of 28 months [54]. Using the same approach, Xenedis et al. analyzed 167 node-negative breast cancer patients before the

(respectively), higher CTC counts correlated with shorter PFS [50].

128 Breast Cancer - From Biology to Medicine

blood of MBC patients receiving their first cycle of first-line treatment [7, 52].

were an independent predictor of prognosis [7, 53].

CK19+

**3.2. Prediction of prognosis of nonmetastatic breast cancer by CTCs**

The largest study on CTCs conducted in an adjuvant setting was the SUCCESS study, which included 2026 patients (median follow-up of 35 months) and collected CTCs after surgery and chemotherapy. The presence of even one CTC before adjuvant systemic treatment was associated with poor disease-free survival and OS. Additionally, node-positive patients were found to have CTCs more frequently than node-negative patients. In a subgroup analysis, the presence of CTCs was not significantly associated with DFS in node-negative patients; however, in node-positive patients, CTC was proportionally associated with number of node involvement and poor prognosis (positive node number of 1–3; *p* = 0.008), 4–9; *p* < 0.001; ≥10; *p* = 0.001). Patients who had CTCs present both before and after systemic chemotherapy had the worst 3 years DFS of any group in this study. Multivariate analysis further confirmed that detection of CTCs prior to chemotherapy was an independent prognostic factor for DFS (HR: 2.11; 95% CI,1.49–2.99, *p* < 0.0001) and OS (HR: 2.18; 95% CI,1.32–3.59, *p* = 0.002) in early breast cancer patients [56]. Although the study provided valuable prognostic data, its clinical significance remains uncertain since it only provided a short follow-up time. Two separate studies have found the presence of one or more CTCs to be of prognostic significance in nonmetastatic, chemotherapy-naïve (HR: 4.62 for PFS; 95% CI, 1.79–11.9, p = 0.005; HR: 4.04 for OS; 95% CI, 1.28–12.8, *p* = 0.011; [57]) and surgery-naïve patients (HR: 2.72 for relapse-free survival (RFS); 95% CI,1.57–4.72, *p* < 0.001; HR: 2.29 for OS; 95% CI, 1.12–4.67, p = 0.02;[58]). Moreover, a meta-analysis of 49 studies (*n* = 6825 of both early breast cancer and MBC patients) showed that in early breast cancer patients, CTC count correlated with both shorter DFS (HR: 2.86; 95% CI, 2.19–3.75, *p* < 0.001) and OS (HR: 2.78; 95% CI, 2.22–3.48, *p* < 0.001) [51]. Similar to MBC patients, posttherapy CTC count was shown to be an independent prognostic factor in non-MBC patients. In a study of stage I–III node-negative breast cancer patients (*n* = 175) who had completed adjuvant chemotherapy, Ignatiadis et al. detected CTCs using a panel of three biomarkers (CK19, mammaglobin (MGB1), and HER2) using RT-PCR. The detection of all three markers was associated with shorter DFS (for CK19+ ; HR: 2.967; 95% CI, 1.64–5.34, *p* < 0.001, for MGB1+; HR: 3.275; 95% CI, 1.58–6.76, *p* = 0.001, and for HER2+; HR: 2.869; 95% CI,1.63–5.02, *p* < 0.001) in univariate analysis [59]. While these studies suggest the independent prognostic significance of CTCs in non-MBC patients both before and after adjuvant chemotherapy, the data remains somewhat controversial.

Kuniyoshi et al. reported no correlation between PFS and the presence of CK19 or HER2 CTCs in non-MBC patients (*n* = 167) at baseline or the first two subsequent follow-ups during chemotherapy [60]. This data was further supported by the recently published results from the SUCCESS-A trial, a randomized, multicenter trial (EudraCT2005000490-21) that evaluated the prognostic value of CTCs in 1221 early-stage (94% of patients were stages I and II) breast cancer patients prior to adjuvant chemotherapy. Using a density gradient followed by labeling with the anticytokeratin antibody, the SUCCESS-A trial detected CTCs in only 20.6% of all patients, and univariate analyses demonstrated that the presence of one or more CTCs had no significant impact on DFS or OS over a median follow-up of 64 months [61]. The inconsistent results regarding the prognostic utility of CTC counts in non-MBC patients may be partially attributable to the use of different CTC detection methods. While studies using the CellSearch® system have consistently reported CTCs as a significant independent prognostic marker in non-MBC patients, those using other methods (such as mRNA or cytokeratin-protein expression) yielded data to the contrary. These conflicting reports underscore the need for standardization of CTC-detection methodologies.

#### **3.3. Subtype-dependent prognostic significance of CTCs**

Breast cancer is a heterogeneous disease, and large-scale gene expression analysis of primary tumors has made it possible to stratify clinical cases into four intrinsic subtypes based on receptor expression—Luminal A, Luminal B, HER2+, and triple negative (TN). These subtypes are significantly associated with differences in clinical outcomes and define a patient's course of therapy. In a large retrospective study using CellSearch®, Giordano et al. addressed subtypespecific differences among CTCs in 517 MBC patients prior to first course of therapy. CTC counts were predictive of prognosis in Luminal and TN breast cancer subtypes but were less so in the HER2-positive subtype. In Luminal A patients, the median OS and PFS of those with ≥5 CTCs (*n* = 292) were significantly shorter than those with <5 CTCs (OS, 18.8 vs. 48.7 months; *p* < 0.001; and PFS, 5.9 vs. 7.1; *p* = 0.004). Within the TN subtype, patients with >5 CTCs (*n* = 124) had a median OS significantly shorter than patients with <5 CTCs (10.4 vs. 17.8 months respectively; *p* = 0.001); however, there was little difference in median PFS for these patients (PFS, 5.1 vs. 4.8, respectively; *p* = 0.274). By contrast, among HER2+ patients, there was no significant association between CTC count and OS or PFS (median OS, 27.2 vs. 21.4 months; *p* = 0.991; median PFS 7.6 vs. 8.6; *p* = 0.458) [62]. Likewise, another retrospective study using CellSearch® found similar trends in the relationship between CTCs and subtype among MBC patients. Patients were stratified into groups based on their CTC count at baseline (0, 1–4, or ≥ 5 CTCs) and subtype. Similar to Giordano's findings, CTCs were predominately found in patients with Luminal-A/ Luminal-B/HER2-negative subtypes. Moreover, patients of all subtypes, except HER2+ , with no CTCs detected in the blood had a better prognosis compared with those with 1–4 or >5 CTCs [63]. However, a large, multicenter study conducted in Germany found that CTC count at baseline was positively associated with shorter OS in all tumor subtypes, including HER2+ patients [64]. Additionally, two recent reports have investigated the prognostic role of CTCs, specifically in TN subtype. A meta-analyses including 10 studies with a total of 642 metastatic and nonmetastatic TN breast cancer patients found the presence of CTCs, predicted aggressive disease progression (HR: 2.18; 95% CI, 1.59–2.99, *p* = 0.010) and reduced OS (HR: 2.02; 95% CI, 1.59–2.57, *p* = 0.169) [65]. Additionally, Karhade et al. evaluated CTCs at baseline in 113 stage I–III nonmetastatic TN patients and found that presence of ≥2 CTCs predicted shorter PFS (HR: 8.30; 95% CI, 2.61–26.37, *p* < 0.001) and OS (HR: 7.19; 95% CI, 1.98–26.06, *p* < 0.0004) [66].

In summary, the prevalence and clinical relevance of CTCs vary by breast cancer subtype. Currently, data indicates that the prevalence of CTCs is high among metastatic Luminal A as well as both metastatic and nonmetastatic TN breast cancers. However, data regarding the prognostic significance in HER2+ tumors remains inconclusive.

#### **3.4. Prediction of therapy response by CTCs**

Neoadjuvant therapy is increasingly popular among patients (e.g., TN, HER2+, or large tumor burden, etc) who would qualify for adjuvant chemotherapy. Several studies have explored the clinical validity of CTCs in this setting, specifically the REMAGUS02, GeparQuattro, and BEVERLY-2 trials. Each of these studies enumerated CTCs before and after neoadjuvant therapy, yet produced contradictory findings. The REMAGUS02 trial found no correlation between the presence of CTCs and pathological complete response, tumor size, grade, or lymph node status. However, multivariate analysis revealed that patients without CTCs before and after neoadjuvant therapy had better distant metastasis-free survival (DMFS) (before; Relative Risk (RR): 2.4; 95% CI,0.9–6, *p* = 0.06), although no difference was noted in DMFS or OS after a median follow-up of 70 months [67]. On the other hand, the GeparQuattro trial failed to show any correlation between the presence of CTCs and, worse, DFS or OS [68]. The BEVERLY-2 study, however, showed that the presence of CTCs at baseline was an independent prognostic factor for poor DFS (HR: 4.75; 95% CI,1.56–14.50, *p* = 0.006) at 3 years of follow-up [69]. Another study compared CTC enumeration with CT scan results in MBC patients following therapy. CTCs were measured at baseline and 4 weeks following therapy, and CT scans were obtained at 9–12-week intervals to assess response to therapy using RECIST criteria. CTC counts were reviewed by a local and central laboratory, while two central radiologists reviewed the CT scans. Superior interreader agreement for CTCs was observed at 0.7% variability, and radiological responses showed 15.2% variability. Patients with <5 CTCs following 4 weeks of therapy who had stable or partial response on the CT scans demonstrated the best median OS of 26.9 months. After these results, however, it is still unclear whether the change in therapy course can be based on CTC detection following chemotherapy in MBC patients [70]. In 2014, the first interventional study based on postchemotherapy CTC detection was launched by the SWOG trial. The main goal of the S0500 SWOG study was to demonstrate an OS benefit in CTC-positive patients who were nonresponsive to therapy by switching them from first to second-line therapy. Patients who had >5 CTCs after 3 weeks of therapy were randomized to ARM 1 (continuation of same therapy) or to ARM 2 (switch to second-line therapy). Disappointingly, no difference in OS or DFS was observed in either arm. There are several ongoing interventional clinical trials that stratify patients based on CTC count for either aggressive chemotherapy or hormonal therapy [52]. Another trial is investigating the change of therapy based on CTC number at the third or subsequent lines of therapy for MBC [71]. In conclusion, the present data is insufficient to recommend the use of CTC enumeration for risk stratification and treatment response. Also, early changes in therapy based on CTC enumeration in MBC patients are not recommended at this time, although ongoing studies may yield more definitive results.

### **4. CTC heterogeneity**

CellSearch® system have consistently reported CTCs as a significant independent prognostic marker in non-MBC patients, those using other methods (such as mRNA or cytokeratin-protein expression) yielded data to the contrary. These conflicting reports underscore the need

Breast cancer is a heterogeneous disease, and large-scale gene expression analysis of primary tumors has made it possible to stratify clinical cases into four intrinsic subtypes based on receptor expression—Luminal A, Luminal B, HER2+, and triple negative (TN). These subtypes are significantly associated with differences in clinical outcomes and define a patient's course of therapy. In a large retrospective study using CellSearch®, Giordano et al. addressed subtypespecific differences among CTCs in 517 MBC patients prior to first course of therapy. CTC counts were predictive of prognosis in Luminal and TN breast cancer subtypes but were less so in the HER2-positive subtype. In Luminal A patients, the median OS and PFS of those with ≥5 CTCs (*n* = 292) were significantly shorter than those with <5 CTCs (OS, 18.8 vs. 48.7 months; *p* < 0.001; and PFS, 5.9 vs. 7.1; *p* = 0.004). Within the TN subtype, patients with >5 CTCs (*n* = 124) had a median OS significantly shorter than patients with <5 CTCs (10.4 vs. 17.8 months respectively; *p* = 0.001); however, there was little difference in median PFS for these patients (PFS, 5.1 vs. 4.8,

between CTC count and OS or PFS (median OS, 27.2 vs. 21.4 months; *p* = 0.991; median PFS 7.6 vs. 8.6; *p* = 0.458) [62]. Likewise, another retrospective study using CellSearch® found similar trends in the relationship between CTCs and subtype among MBC patients. Patients were stratified into groups based on their CTC count at baseline (0, 1–4, or ≥ 5 CTCs) and subtype. Similar to Giordano's findings, CTCs were predominately found in patients with Luminal-A/ Luminal-B/HER2-negative subtypes. Moreover, patients of all subtypes, except HER2+

no CTCs detected in the blood had a better prognosis compared with those with 1–4 or >5 CTCs [63]. However, a large, multicenter study conducted in Germany found that CTC count at baseline was positively associated with shorter OS in all tumor subtypes, including HER2+ patients [64]. Additionally, two recent reports have investigated the prognostic role of CTCs, specifically in TN subtype. A meta-analyses including 10 studies with a total of 642 metastatic and nonmetastatic TN breast cancer patients found the presence of CTCs, predicted aggressive disease progression (HR: 2.18; 95% CI, 1.59–2.99, *p* = 0.010) and reduced OS (HR: 2.02; 95% CI, 1.59–2.57, *p* = 0.169) [65]. Additionally, Karhade et al. evaluated CTCs at baseline in 113 stage I–III nonmetastatic TN patients and found that presence of ≥2 CTCs predicted shorter PFS (HR: 8.30; 95% CI, 2.61–26.37, *p* < 0.001) and OS (HR: 7.19; 95% CI, 1.98–26.06, *p* < 0.0004) [66].

In summary, the prevalence and clinical relevance of CTCs vary by breast cancer subtype. Currently, data indicates that the prevalence of CTCs is high among metastatic Luminal A as well as both metastatic and nonmetastatic TN breast cancers. However, data regarding the

Neoadjuvant therapy is increasingly popular among patients (e.g., TN, HER2+, or large tumor burden, etc) who would qualify for adjuvant chemotherapy. Several studies have explored

prognostic significance in HER2+ tumors remains inconclusive.

**3.4. Prediction of therapy response by CTCs**

patients, there was no significant association

, with

for standardization of CTC-detection methodologies.

130 Breast Cancer - From Biology to Medicine

respectively; *p* = 0.274). By contrast, among HER2+

**3.3. Subtype-dependent prognostic significance of CTCs**

A number of studies have addressed the heterogeneous nature of CTCs with the ultimate goal of understanding what molecular signature is required for successful metastasis. The two main phenomena that orchestrate tumor heterogeneity and metastases are cancer stem cells (CSC) and EMT. CSCs are pluripotent, highly resistant to conventional chemotherapy [72–74] and contribute to the heterogeneous nature of the tumor as well as its ability for self-renewal and metastasis [75]. Notably, not all tumor cells are capable of distant organ metastasis; CSCs seem to have such metastatic potential [76]. Likewise, the process of EMT plays an essential role in invasion and metastasis. At the primary tumor site, a subpopulation of cells loses their epithelial characteristics (such as cell polarity or adhesion to the matrix and other cells) and acquires mesenchymal features (including the ability to invade the basement membrane and surrounding tissues), which in turn supports eventual intravasation into the circulation, the first step in the metastatic cascade [77]. These two processes are interconnected, adding further complexity to our understanding of metastasis. Recent studies have shown a direct link between EMT and CSCs in breast cancer, suggesting that EMT generates cancer cells with stem cell-like traits. Mani et al. showed that the induction of EMT in immortalized human mammary epithelial cells results in *de novo* expression of stem cell markers and the acquisition of functional stem cell properties, including the ability to form mammospheres [78–80]. Both CSC and EMT markers have been identified in CTCs, and while a CTC count itself is an independent prognostic marker, the addition of functional marker expression among CTCs will likely strengthen their prognostic value. This section specifically focuses on CTC heterogeneity of CSC and EMT nature.

#### **4.1. Stem-like CTCs**

CSCs are derived both intrinsically and extrinsically [72–74]; although the mechanism for extrinsic acquisition of CSC properties is not clearly understood, several lines of evidence suggest a close link to EMT [81–84]. CSCs are pluripotent and highly resistant to conventional chemotherapy [72–74]. Currently, there is no therapeutics effective in eradicating CSCs [85]; therefore, CTCs with CSC properties are postulated to be an important subset. In 2010, Theodoropoulos et al. investigated whether bulk CTCs contain a subset of cells with CSC characteristics. The protein expression of CSC markers CD44, CD24, and ALDH1 was assessed in cytokeratin+ CTCs isolated from MBC patients using immunofluorescence microscopy. In approximately 1500 CTCs identified from 20 MBC patients, 35.2% had the stem-like phenotype (CD44+ /CD24−/low), whereas 17.7% of the CTCs were ALDH1−high/CD24−/low [86]. This is in concordance with another study that found 19% of EpCAM+ /Cytokeratin+ CTCs are also CD44+ /CD24−/low cells [87]. Further support came from an experimental model that demonstrated a stem-like CD44+ CTC subset isolated from MBC patient blood having metastatic potential. Interestingly, the six recipient NSG, immunocompromised mice in this study developed multiple bone, lung, and liver metastases within 6–12 months following injection of bulk CTCs into their bone marrow, confirming the existence of metastatic-initiating cells (MICs) among CTCs. To determine the phenotype of the MIC-CTC subpopulation, flow cytometry analyses showed that all analyzed CTCs expressed CD44 and CD47. CD47 has been implicated in facilitating cancer cell evasion of the innate immune system through its inhibitory role in phagocytosis. Around 33% of CD44+ /CD47+ CTCs express the hepatocyte growth factor (HGF) receptor MET, a tyrosine kinase involved in the activation of the migration and putative invasion program in several cancers. To functionally assess the presence of MICs in this cell population, CD44+ /CD47+ /MET+/– CTCs were isolated by FACS and directly transplanted into the femoral medullar cavity of an NSG recipient mouse. After 8 months, bone metastasis developed in the mouse, demonstrating that CD44+ CD47+ MET+/- CTCs contain functional MICs. These markers were further examined in four patients before and after disease progression. An increased frequency of CTCs with CD44+ /CD47+ /MET+ was detected after disease progression (fold increase of 1.78; *p* = 0.019). Additionally, in a total of eight patients, those with >12 CD44+ /CD47+ /MET+ (triple positive) CTCs per 7.5 ml of blood had significantly more metastasis sites than those with <12 triple positive CTCs (mean: 3.25 sites vs. 2.25 sites; *p* = 0.03), and the presence of CD44+ /CD47+ /MET+ CTCs was associated with shorter OS (HR: 7.4; *p* = 0.0246) [88]. The association of CSC-CTCs with advanced disease was further supported by the work of Papadaki *et al*. who found that MBC patients have a higher percentage of ALDH1+ CTCs than those with early breast cancer. ALDH1+ CTCs were observed in 38.7% of CTCs from early-breast cancer patients compared to 83.5% from MBC patients [89]. Together, these data suggest that CTCs with CSC characteristics have more biological relevance for disease development, progression, and outcomes than bulk CTC data. However, the value of CSClike CTCs in prognosis and therapy-response prediction requires further confirmation in a large prospective clinical study.

#### **4.2. Mesenchymal CTCs**

epithelial characteristics (such as cell polarity or adhesion to the matrix and other cells) and acquires mesenchymal features (including the ability to invade the basement membrane and surrounding tissues), which in turn supports eventual intravasation into the circulation, the first step in the metastatic cascade [77]. These two processes are interconnected, adding further complexity to our understanding of metastasis. Recent studies have shown a direct link between EMT and CSCs in breast cancer, suggesting that EMT generates cancer cells with stem cell-like traits. Mani et al. showed that the induction of EMT in immortalized human mammary epithelial cells results in *de novo* expression of stem cell markers and the acquisition of functional stem cell properties, including the ability to form mammospheres [78–80]. Both CSC and EMT markers have been identified in CTCs, and while a CTC count itself is an independent prognostic marker, the addition of functional marker expression among CTCs will likely strengthen their prognostic value. This section specifically focuses on CTC hetero-

CSCs are derived both intrinsically and extrinsically [72–74]; although the mechanism for extrinsic acquisition of CSC properties is not clearly understood, several lines of evidence suggest a close link to EMT [81–84]. CSCs are pluripotent and highly resistant to conventional chemotherapy [72–74]. Currently, there is no therapeutics effective in eradicating CSCs [85]; therefore, CTCs with CSC properties are postulated to be an important subset. In 2010, Theodoropoulos et al. investigated whether bulk CTCs contain a subset of cells with CSC characteristics. The protein expression of CSC markers CD44, CD24, and ALDH1 was assessed in cytokeratin+ CTCs isolated from MBC patients using immunofluorescence microscopy. In approximately 1500 CTCs identified from 20 MBC patients, 35.2% had the stem-like phe-

in concordance with another study that found 19% of EpCAM+

/CD24−/low), whereas 17.7% of the CTCs were ALDH1−high/CD24−/low [86]. This is

CTC subset isolated from MBC patient blood having metastatic

/MET+/– CTCs were isolated by FACS and directly transplanted

CD47+

/CD47+

/MET+

/CD24−/low cells [87]. Further support came from an experimental model that demon-

potential. Interestingly, the six recipient NSG, immunocompromised mice in this study developed multiple bone, lung, and liver metastases within 6–12 months following injection of bulk CTCs into their bone marrow, confirming the existence of metastatic-initiating cells (MICs) among CTCs. To determine the phenotype of the MIC-CTC subpopulation, flow cytometry analyses showed that all analyzed CTCs expressed CD44 and CD47. CD47 has been implicated in facilitating cancer cell evasion of the innate immune system through its inhibitory

/CD47+

(HGF) receptor MET, a tyrosine kinase involved in the activation of the migration and putative invasion program in several cancers. To functionally assess the presence of MICs in this

into the femoral medullar cavity of an NSG recipient mouse. After 8 months, bone metasta-

MICs. These markers were further examined in four patients before and after disease pro-

ease progression (fold increase of 1.78; *p* = 0.019). Additionally, in a total of eight patients,

/Cytokeratin+

CTCs express the hepatocyte growth factor

MET+/- CTCs contain functional

was detected after dis-

CTCs are also

geneity of CSC and EMT nature.

132 Breast Cancer - From Biology to Medicine

**4.1. Stem-like CTCs**

notype (CD44+

strated a stem-like CD44+

cell population, CD44+

role in phagocytosis. Around 33% of CD44+

/CD47+

sis developed in the mouse, demonstrating that CD44+

gression. An increased frequency of CTCs with CD44+

CD44+

EMT contributes to the acquisition of invasiveness in cancer cells, and therefore it is believed that CTCs with mesenchymal features may contribute to metastasis. However, this question has not been extensively addressed due to the use of affinity-based CTC enrichment methods that rely on EpCAM and/or Cytokeratin markers, lacking mesenchymal cell surface marker selection. EMT is a gradual process that yields epithelial cells which have not gone through EMT, intermediate mesenchymal (cells that have partially completed EMT), and exclusively mesenchymal cells (ones that have completed EMT). Yu et al. characterized the EMT status of CTCs captured on the microfluidic herringbone chip with an antibody cocktail directed against EpCAM, EGFR, and HER2. These researchers established a quantifiable, dual-colorimetric RNA-in situ hybridization (ISH) assay to examine tumor cells for expression of seven pooled epithelial transcripts (Cytokeratin 5, 7, 8, 18, and 19, EpCAM, and Cadherin 1) and three mesenchymal transcripts (Fibronectin 1, Cadherin 2, and Serpin peptidase inhibitor/clade E [SERPINE1/PAI1]). Five categories of cells ranging from exclusively epithelial, intermediate (more epithelial, equal, and more mesenchymal), and exclusively mesenchymal were determined [90]. Similarly, a study by Polioudaki et al. used the ratio of Cytokeratin to Vimentin protein expression (measured by immunofluorescence) to study on a single cell basis the EMT status of 110 CTCs detected in 5 MBC patients. This study identified that 46% of CTCs were "epithelial," 5.4% were "mesenchymal," and 48.2% were "intermediate" [91]. The existence of CTCs across the EMT spectrum was further confirmed by another single cell level study. Using DEPArray to select viable CTCs from 56 MBC patients, Bulfoni et al. determined the EMT status of single CTCs by staining with an antibody cocktail that recognized both epithelial (EpCAM, E-Cad) and mesenchymal (CD44, CD146, and N-Cadherin) markers. This study also reported the presence of diverse CTC phenotypes based on their EMT statuses [92]. CTC heterogeneity was further investigated on a genetic level using single CTCs. Powell et al. were the first to perform microfluidic-based single cell transcriptional profiling of 87 cancerassociated and reference genes in CTCs. Their study found that CTCs are heterogeneous and can be separated into two major subgroups based on 31 highly expressed genes including mesenchymal and metastatic associated genes (*VIMENTIN, TGFß1, ZEB2, FOXC1, CXCR4, NPTN, S100A4, and S100A9*) [93].

Several studies have investigated the clinical relevance of CTCs based on their EMT status. Yu et al. reported the CTCs isolated from ER+ /PR+ breast cancer patients were predominantly epithelial, whereas those from the TN and HER2+ subtypes were predominantly mesenchymal in a sample of 41 MBC patients [90]. Similarly, Polioudaki et al. retrospectively analyzed 1000 CTCs isolated from 61 MBC patients at baseline using CellSearch® and investigated the correlation between the level of cytokeratin expression and tumor subtype. Interestingly, CTCs from TN patients showed a lower average cytokeratin expression level compared to those from the remaining patients (122 vs. 175; *p* < 0.001) [91]. Moreover, Kallergi et al. found that the proportion of CTCs coexpressing cytokeratins 7, 8, or 18 together with the mesenchymal marker Twist (measured by Immunofluorescence) is lower in patients with non-MBC than in patients with MBC (53% vs. 97%, respectively; *p* < 0.001). Similarly, the proportion of CTCs coexpressing cytokeratins and Vimentin was lower in patients with non-MBC than in those with MBC (56% vs. 74%; *p* = 0.005) [94]. Likewise, Papadaki et al. found that nuclear Twist localization was detected in the CTCs of 70.3% of MBC patients, whereas it was detected in only 32.3% of CTCs from early breast cancer patients [89]. Moreover, Markiewicz et al. found that CTCs isolated from lymph node-positive breast cancer patients are more frequently Vimentin and Snail mRNA expression positive compared to those from lymph node-negative patients [95]. Polioudaki et al. reported that 1-year OS of patients with high cytokeratin+ CTCs was 73.3%, whereas 1-year OS declined by 46.2% in patients with low cytokeratin+ CTCs (*p* = 0.038) [91].

#### **4.3. CTC with CSC and EMT characteristics**

Given the implications of both CSCs and EMT in metastasis, the existence of CTCs displaying both traits has been investigated. Aktas et al. tested the mRNA expression of three EMT markers (Twist1, Akt2, PI3Kα) and the CSC marker ALDH1 in CTCs from 39 MBC patients and found CTCs expressing at least one EMT marker, ALDH1, or both in 21 patients (81% of CTC-positive patients) [96]. The presence of CTCs coexpressing one of the EMT markers and ALDH1 was further confirmed by Raimondi et al. The mRNA expression of ALDH1 in bulk CTCs is correlated with the mRNA expression of Vimentin and Fibronectin (*p* < 0.001) [97]. Papadaki et al. further investigated the coexpression of ALDH1 and Twist in individual CTCs from both early and MBC patients and found that the prevalence of an ALDH1+ /Twist+ subpopulation was significantly higher in MBC patients compared to those with early disease (76% vs. 15.4%, *p* = 0.001) [89]. Overall, these results indicate that the identification of a subpopulation of CTCs bearing mesenchymal properties, cancer stem cell characteristics, or both may help in discerning which patients are at higher risk for disease progression.

#### **5. Summary**

CTCs have received significant attention as a liquid biopsy to facilitate longitudinal disease monitoring. The current consensus based on large clinical studies is that CTC count is an independent prognostic marker in MBC, yet it is still controversial whether CTC count is predictive of prognosis in non-MBC or could be used for monitoring of therapy response. Several clinical trials are currently ongoing to determine the utility of CTCs in making an early decision to change the course of therapy and spare toxicity. The isolation of CTCs has been a challenging task due to their rarity in the blood; however, a number of new isolation and detection strategies have emerged in the past 10 years, making CTC detection in relatively small amounts of blood feasible. The current challenge in this era is tackling the heterogeneous nature of CTCs and understanding which subpopulations drive metastasis. The count of CTCs with mesenchymal features was shown to be more sensitive in the prediction of prognosis than the number of bulk CTCs. Similarly, CTCs with both stem-like and mesenchymal features sensitively predicted prognosis. Currently, the detection of mesenchymal CTCs that have lost their epithelial markers requires laborious work necessitating either the detection of intracellular markers or mRNA expression. Therefore, discovery of a new CTC-specific functional surface marker that is relevant to metastasis would greatly advance the realistic clinical utility of CTCs. Additionally, in-depth understanding of CTC's heterogeneity utilizing single cell level analysis will improve our knowledge of hematogenous metastasis.

### **Abbreviations**

Several studies have investigated the clinical relevance of CTCs based on their EMT status. Yu

in a sample of 41 MBC patients [90]. Similarly, Polioudaki et al. retrospectively analyzed 1000 CTCs isolated from 61 MBC patients at baseline using CellSearch® and investigated the correlation between the level of cytokeratin expression and tumor subtype. Interestingly, CTCs from TN patients showed a lower average cytokeratin expression level compared to those from the remaining patients (122 vs. 175; *p* < 0.001) [91]. Moreover, Kallergi et al. found that the proportion of CTCs coexpressing cytokeratins 7, 8, or 18 together with the mesenchymal marker Twist (measured by Immunofluorescence) is lower in patients with non-MBC than in patients with MBC (53% vs. 97%, respectively; *p* < 0.001). Similarly, the proportion of CTCs coexpressing cytokeratins and Vimentin was lower in patients with non-MBC than in those with MBC (56% vs. 74%; *p* = 0.005) [94]. Likewise, Papadaki et al. found that nuclear Twist localization was detected in the CTCs of 70.3% of MBC patients, whereas it was detected in only 32.3% of CTCs from early breast cancer patients [89]. Moreover, Markiewicz et al. found that CTCs isolated from lymph node-positive breast cancer patients are more frequently Vimentin and Snail mRNA expression positive compared to those from lymph node-negative patients [95]. Polioudaki et al. reported that 1-year OS of patients with high cytokeratin+

breast cancer patients were predominantly epi-

subtypes were predominantly mesenchymal

CTCs

CTCs (*p* =

/PR+

was 73.3%, whereas 1-year OS declined by 46.2% in patients with low cytokeratin+

Given the implications of both CSCs and EMT in metastasis, the existence of CTCs displaying both traits has been investigated. Aktas et al. tested the mRNA expression of three EMT markers (Twist1, Akt2, PI3Kα) and the CSC marker ALDH1 in CTCs from 39 MBC patients and found CTCs expressing at least one EMT marker, ALDH1, or both in 21 patients (81% of CTC-positive patients) [96]. The presence of CTCs coexpressing one of the EMT markers and ALDH1 was further confirmed by Raimondi et al. The mRNA expression of ALDH1 in bulk CTCs is correlated with the mRNA expression of Vimentin and Fibronectin (*p* < 0.001) [97]. Papadaki et al. further investigated the coexpression of ALDH1 and Twist in individual CTCs from both early and MBC patients and found that the prevalence of an

with early disease (76% vs. 15.4%, *p* = 0.001) [89]. Overall, these results indicate that the identification of a subpopulation of CTCs bearing mesenchymal properties, cancer stem cell characteristics, or both may help in discerning which patients are at higher risk for

CTCs have received significant attention as a liquid biopsy to facilitate longitudinal disease monitoring. The current consensus based on large clinical studies is that CTC count is an independent prognostic marker in MBC, yet it is still controversial whether CTC count is predictive

subpopulation was significantly higher in MBC patients compared to those

et al. reported the CTCs isolated from ER+

134 Breast Cancer - From Biology to Medicine

0.038) [91].

ALDH1+

/Twist+

disease progression.

**5. Summary**

thelial, whereas those from the TN and HER2+

**4.3. CTC with CSC and EMT characteristics**

### **Author details**

DAPI 4',6-Diamidino-2-phenylindole

EGFR Epithelial Growth Factor Receptor EMT Epithelial Mesenchymal Transition EpCAM Epithelial cell adhesion molecule

ERBB2 Receptor tyrosine-protein kinase erbB-2 (Gene) ERBB3 Receptor tyrosine-protein kinase erbB-3 (Gene)

HER2 Receptor tyrosine-protein kinase erbB-2 (protein)

ISET Isolation by size of epithelial tumor cells test

MET The hepatocyte growth factor receptor

ERCC1 DNA excision repair protein ERCC-1

FDA Food and Drug Administration

FOXC1 Forkhead box protein C1

HGF Hepatocyte growth factor

HL Hematopoietic lineage

ISH In situ hybridization

MBC Metastatic breast cancer

MICs Metastatic-initiating cells mRNA Messenger ribonucleic acid

HB Heringbone

HR Hazard ratio

MGB1 Mammaglobin

MUC1 Mucin-1

NPTN Neuroplastin

NSG NOD Scid Gamma

OS Overall Survival

DEP DielEctroPhoretic

136 Breast Cancer - From Biology to Medicine

DFS Disease-Free Survival DNA Deoxyribonucleic acid

EPISPOT EPithelial ImmunoSPOT

ER Estrogen receptor

Mohamed Kamal1 , Wajeeha Razaq2 , Macall Leslie1 , Smita Adhikari1 and Takemi Tanaka1,3\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Stephenson Cancer Centre, Oklahoma University Health Science Center, OK, USA

2 Department of Hematology and Oncology, Stephenson Cancer Centre, School of Medicine, OK, USA

3 Department of Pathology, Stephenson Cancer Centre, School of Medicine, Oklahoma University Health Science Center, OK, USA

### **References**

[17] Kagan MH, Bendele MS, Mayes BS, Silvia BS, Repollet BS, Doyle DDS, Allard J, Tu N, Bui T, Russell T, Rao C, Hermann BS, Rutner MS, Terstappen LWMM. A sample preparation and analysis system for identification of circulating tumor cells. Journal of Clinical Ligand Assay. 2002;25:104 –10.

**References**

138 Breast Cancer - From Biology to Medicine

1961;21(1):38–9.

Journal of Medicine. 2004;351.

[1] Ashworth TR. A case of cancer in which cells similar to those in the tumors were seen in

[2] Jonescu P. Über das Vorkommen von Geschwulstzellen im strömenden Blut von Tieren

[4] Alexander RF, Spriggs AI. The differential diagnosis of tumour cells in circulating blood.

[5] Zeidman I. The fate of circulating tumor cells. I Passage of cells through capillaries.

[6] Romsdahl MD, Chu EW, Hume R, Smith RR. The time of metastasis and release of circulating tumor cells as determined in an experimental system. Cancer. 1961;14(4):883–8.

[7] Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. The New England

[8] Nguyen DX, Bos PD, Massague J. Metastasis: from dissemination to organ-specific colo-

[9] Joosse SA, Gorges TM, Pantel K. Biology, detection, and clinical implications of circulat-

[10] Miller MC, Doyle GV, Terstappen LW. Significance of circulating tumor cells detected by the CellSearch system in patients with metastatic breast colorectal and prostate cancer.

[11] Alix-Panabieres C, Schwarzenbach H, Pantel K. Circulating tumor cells and circulating

[12] Hu XC, Chow LW. Detection of circulating breast cancer cells with multiple-marker

[13] Hawes D, Neville AM, Cote RJ. Occult metastasis. Biomedicine & Pharmacotherapy =

[14] Witzig TE, Bossy B, Kimlinger T, Roche PC, Ingle JN, Grant C, et al. Detection of circulating cytokeratin-positive cells in the blood of breast cancer patients using immunomagnetic enrichment and digital microscopy. Clinical Cancer Research. 2002;8(5):1085–91.

[15] Bednarz-Knoll N, Alix-Panabieres C, Pantel K. Plasticity of disseminating cancer cells in patients with epithelial malignancies. Cancer Metastasis Reviews. 2012;31(3–4):673–87.

[16] Mostert B, Kraan J, Sieuwerts AM, van der Spoel P, Bolt-de Vries J, Prager-van der Smissen WJ, et al. CD49f-based selection of circulating tumor cells (CTCs) improves

detection across breast cancer subtypes. Cancer Letters. 2012;319(1):49–55.

the blood after death. Medical Journal of Australia. 1869;14:146–9.

[3] Marcus H. Krebszellen im strömenden Blut. Z Krebsforsch. 1919;16.

mit Impftumoren. Z Krebsforsch. 1931;33:264.

Journal of Clinical Pathology. 1960;13:414s24.

nization. Nature Reviews Cancer. 2009;9(4):274–84.

ing tumor cells. EMBO Molecular Medicine. 2015;7.

tumor DNA. Annual Review of Medicine. 2012;63:199–215.

RT-PCR assay. Anticancer Research. 2001;21(1A):421–4.

Biomedecine & Pharmacotherapie. 2001;55(4):229–42.

Journal of Oncology. 2010;2010:617421.

[42] Khoja L, Backen A, Sloane R, Menasce L, Ryder D, Krebs M, et al. A pilot study to explore circulating tumour cells in pancreatic cancer as a novel biomarker. British Journal of Cancer. 2012;106(3):508–16.

[29] Lu J, Fan T, Zhao Q, Zeng W, Zaslavsky E, Chen JJ, et al. Isolation of circulating epithelial and tumor progenitor cells with an invasive phenotype from breast cancer patients.

[30] den Toonder J. Circulating tumor cells: the Grand Challenge. Lab on a Chip.

[31] Kim MS, Sim TS, Kim YJ, Kim SS, Jeong H, Park J-M, et al. SSA-MOA: a novel CTC isolation platform using selective size amplification (SSA) and a multi-obstacle architecture

[32] Coumans FAW, van Dalum G, Beck M, Terstappen LWMM. Filter characteristics influencing circulating tumor cell enrichment from whole blood. PloS One. 2013;8(4):e61770.

[33] Paterlini-Brechot P, Benali NL. Circulating tumor cells (CTC) detection: Clinical impact

[34] Moon H-S, Kwon K, Kim S-I, Han H, Sohn J, Lee S, et al. Continuous separation of breast cancer cells from blood samples using multi-orifice flow fractionation (MOFF)

[35] Rostagno P, Moll JL, Bisconte JC, Caldani C. Detection of rare circulating breast cancer cells by filtration cytometry and identification by DNA content: sensitivity in an experi-

[36] Vona G, Sabile A, Louha M, Sitruk V, Romana S, Schutze K, et al. Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulatingtumor cells. The American Journal of Pathology. 2000;156(1):57–63.

[37] Dong X, Alpaugh KR, Cristofanilli M. Circulating tumor cells (CTCs) in breast cancer: a diagnostic tool for prognosis and molecular analysis. Chinese Journal of Cancer

[38] Hofman VJ, Ilie MI, Bonnetaud C, Selva E, Long E, Molina T, et al. Cytopathologic detection of circulating tumor cells using the isolation by size of epithelial tumor cell method: promises and pitfalls. American Journal of Clinical Pathology. 2011;135(1):146–56. [39] Pinzani P, Salvadori B, Simi L, Bianchi S, Distante V, Cataliotti L, et al. Isolation by size of epithelial tumor cells in peripheral blood of patients with breast cancer: correlation with real-time reverse transcriptase-polymerase chain reaction results and feasibility of molecular analysis by laser microdissection. Human Pathology. 2006;37(6):711–8. [40] De Giorgi V, Pinzani P, Salvianti F, Panelos J, Paglierani M, Janowska A, et al. Application of a filtration- and isolation-by-size technique for the detection of circulating tumor cells in cutaneous melanoma. The Journal of Investigative Dermatology. 2010;130(10):2440–7.

[41] Hofman V, Ilie MI, Long E, Selva E, Bonnetaud C, Molina T, et al. Detection of circulating tumor cells as a prognostic factor in patients undergoing radical surgery for non-small-cell lung carcinoma: comparison of the efficacy of the CellSearch Assay and the isolation by size of epithelial tumor cell method. International Journal of Cancer.

International Journal of Cancer. 2010;126(3):669–83.

(MOA) filter. Lab on a Chip. 2012;12(16):2874–80.

and future directions. Cancer Letters. 2007;253(2):180–204.

mental model. Anticancer Research. 1997;17(4A):2481–5.

Research. 2012;24(4):388–98.

2011;129(7):1651–60.

and dielectrophoresis (DEP). Lab on a Chip. 2011;11(6):1118–25.

2011;11(3):375–7.

140 Breast Cancer - From Biology to Medicine

detected by real-time polymerase chain reaction in node-negative breast cancer patients. Journal of Clinical Oncology. 2006;24(23):3756–62.

[68] Riethdorf S, Müller V, Zhang L, Rau T, Loibl S, Komor M, et al. Detection and HER2 expression of circulating tumor cells: prospective monitoring in breast cancer patients treated in the neoadjuvant GeparQuattro Trial. Clinical Cancer Research. 2010;16(9):2634–45.

detected by real-time polymerase chain reaction in node-negative breast cancer patients.

[56] Rack B, Schindlbeck C, Juckstock J, Andergassen U, Hepp P, Zwingers T, et al. Circulating tumor cells predict survival in early average-to-high risk breast cancer patients. Journal

[57] Lucci A, Hall CS, Lodhi AK, Bhattacharyya A, Anderson AE, Xiao L, et al. Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncology. 2012;13.

[58] Hall C, Valad L, Lucci A. Circulating tumor cells in breast cancer patients. Critical

[59] Ignatiadis M, Kallergi G, Ntoulia M, Perraki M, Apostolaki S, Kafousi M. Prognostic value of the molecular detection of circulating tumor cells using a multimarker reverse transcription-PCR assay for cytokeratin 19, mammaglobin A, and HER2 in early breast

[60] Kuniyoshi RK, Gehrke Fde S, Alves BC, Vilas-Boas V, Colo AE, Sousa N, et al. Gene profiling and circulating tumor cells as biomarker to prognostic of patients with locore-

[61] Jueckstock J, Rack B, Friedl TW, Scholz C, Steidl J, Trapp E, et al. Detection of circulating tumor cells using manually performed immunocytochemistry (MICC) does not correlate with outcome in patients with early breast cancer—results of the German SUCCESS-

[62] Giordano A, Giuliano M, Laurentiis M, Arpino G, Jackson S, Handy BC. Circulating tumor cells in immunohistochemical subtypes of metastatic breast cancer: lack of prediction in HER2-positive disease treated with targeted therapy. Annals of Oncology :

[63] Munzone E, Botteri E, Sandri MT, Esposito A, Adamoli L, Zorzino L, et al. Prognostic value of circulating tumor cells according to immunohistochemically defined molecular

[64] Wallwiener M, Hartkopf AD, Baccelli I, Riethdorf S, Schott S, Pantel K. The prognostic impact of circulating tumor cells in subtypes of metastatic breast cancer. Breast Cancer

[65] Lu YJ, Wang P, Wang X, Peng J, Zhu YW, Shen N. The significant prognostic value of circulating tumor cells in triple-negative breast cancer: a meta-analysis. Oncotarget. 2016;

[66] Karhade M, Hall C, Mishra P, Anderson A, Kuerer H, Bedrosian I, et al. Circulating tumor cells in non-metastatic triple-negative breast cancer. Breast Cancer Research and

[67] Bidard FC, Belin L, Delaloge S, Lerebours F, Ngo C, Reyal F, et al. Time-dependent prognostic impact of circulating tumor cells detection in non-metastatic breast cancer: 70-month analysis of the REMAGUS02 study. International Journal of Breast Cancer. 2013;2013:130470.

Official Journal of the European Society for Medical Oncology/ESMO. 2012;23.

subtypes in advanced breast cancer. Clinical Breast Cancer. 2012;12(5):340–6.

Journal of Clinical Oncology. 2006;24(23):3756–62.

of the National Cancer Institute. 2014;106(5).

142 Breast Cancer - From Biology to Medicine

Reviews in Oncogenesis. 2016;21(1-2):125–39.

cancer. Clinical Cancer Research. 2008;14.

A-trial. BMC Cancer. 2016;16:401.

Research and Treatment. 2013;137.

Treatment. 2014;147(2):325–33.

7(24): 37361–9.

gional breast cancer. Tumour Biology. 2015;36(10):8075–83.

[95] Markiewicz A, Ksiazkiewicz M, Welnicka-Jaskiewicz M, Seroczynska B, Skokowski J, Szade J, et al. Mesenchymal phenotype of CTC-enriched blood fraction and lymph node metastasis formation potential. PLoS One. 2014;9(4):e93901.

[82] Tsai JH, Yang J. Epithelial-mesenchymal plasticity in carcinoma metastasis. Genes &

[83] Puisieux A, Brabletz T, Caramel J. Oncogenic roles of EMT-inducing transcription fac-

[84] Kalluri R. EMT: when epithelial cells decide to become mesenchymal-like cells. Journal

[85] Kano Y, Konno M, Kawamoto K, Tamari K, Hayashi K, Fukusumi T, et al. Novel drug discovery system for cancer stem cells in human squamous cell carcinoma of the esopha-

[86] Theodoropoulos PA, Polioudaki H, Agelaki S, Kallergi G, Saridaki Z, Mavroudis D. Circulating tumor cells with a putative stem cell phenotype in peripheral blood of

[87] Wang J, Cao MG, You CZ, Wang CL, Liu SL, Kai C, et al. A preliminary investigation of the relationship between circulating tumor cells and cancer stem cells in patients with

[88] Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates

[89] Papadaki MA, Kallergi G, Zafeiriou Z, Manouras L, Theodoropoulos PA, Mavroudis D, et al. Co-expression of putative stemness and epithelial-to-mesenchymal transition markers on single circulating tumour cells from patients with early and metastatic breast

[90] Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013;339.

[91] Polioudaki H, Agelaki S, Chiotaki R, Politaki E, Mavroudis D, Matikas A, et al. Variable expression levels of keratin and vimentin reveal differential EMT status of circulating tumor cells and correlation with clinical characteristics and outcome of patients with

[92] Bulfoni M, Gerratana L, Del Ben F, Marzinotto S, Sorrentino M, Turetta M, et al. In patients with metastatic breast cancer the identification of circulating tumor cells in epithelial-to-mesenchymal transition is associated with a poor prognosis. Breast Cancer

[93] Powell AA, Talasaz AH, Zhang H, Coram MA, Reddy A, Deng G, et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast

[94] Kallergi G, Papadaki MA, Politaki E, Mavroudis D, Georgoulias V, Agelaki S. Epithelial to mesenchymal transition markers expressed in circulating tumour cells of early and

metastatic breast cancer patients. Breast Cancer Research. 2011;13(3):R59.

breast cancer. Cellular and Molecular Biology. 2012;58 Suppl:OL1641–5.

metastasis in a xenograft assay. Nature Biotechnology. 2013;31.

metastatic breast cancer. BMC Cancer. 2015;15(1):1–10.

cancer cell lines. PLoS One. 2012;7(5):e33788.

Development. 2013;27(20):2192–206.

144 Breast Cancer - From Biology to Medicine

gus. Oncology Reports. 2013.

cancer. BMC Cancer. 2014;14:651.

Research. 2016;18(1):1–15.

tors. Nature Cell Biology. 2014;16(6):488–94.

of Clinical Investigation. 2009;119(6):1417–9.

patients with breast cancer. Cancer Letters. 2010;288.

**Provisional chapter**

### **DNA Hypermethylation in Breast Cancer DNA Hypermethylation in Breast Cancer**

Le Huyen Ai Thuy, Lao Duc Thuan and Truong Kim Phuong Le Huyen Ai Thuy, Lao Duc Thuan and Truong Kim Phuong

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66900

#### **Abstract**

Cancer development is a complex process with multiple steps. Many factors, including radiation, chemicals, viruses, genetic and epigenetic changes, lead to abnormal proliferation of a single cell, which results in the outgrowth of a population of clonal-derived tumour cells. It has established that DNA hypermethylation, an epigenetic mechanism that occurred by the addition of a methyl group at 5′ position of the pyrimidine ring of cytosine residues at CpG islands through the action of DNA methyltransferase enzymes, has been considered as the cause of human tumorigenesis, including breast cancer development. Moreover, DNA hypermethylation holds a promising application as a potential biomarker for the early detection, prognosis and prediction of drug sensitivity in cancer. Therefore, this chapter focuses on the description and exemplification of the DNA hypermethylation changes, particularly, highlight the DNA hypermethylation as a potential biomarker applied in predictive, diagnostic, prognostic and therapeutic monitoring of breast cancer.

**Keywords:** breast cancer, epigenetics, hypermethylation, tumour suppressor gene

### **1. Introduction**

Epigenetics, which was first coined by Waddington in 1942, literally means as 'outside conventional genetics', refers to the heritable, reversible changes in gene expression that occur without alteration DNA sequence [1]. Epigenetic modifications are natural processes and essential for mammalian development and cell proliferation. These epigenetic changes could also be affected by many random factors or environmental influences. Disruption of epigenetic modification resulting in regulating patterns of gene expression is the feature of a number of severe human diseases, including malignant cellular transformation [2–4]. Three main epigenetic modification systems, including DNA methylation, histone covalent modification, and non-coding RNA modification, leading to associated-gene silencing, have been observed [5, 6]. This chapter aims

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

to introduce the reader to the concept of DNA methylation, especially DNA hypermethylation, with examples of its involvement in human breast cancer.

### **2. DNA hypermethylation: a kind of epigenetic modification that plays a key role in silencing tumour suppressor genes**

DNA methylation is one of the epigenetic mechanisms that is closely associated with normal cell development and a number of key processes including imprinting, X-chromosome inactivation, repression of repetitive element transcription, chromatin organization, etc. [7–9]. Aberrant methylation patterns are known to be presented in the genomes of cancer cells. Two patterns of aberrant methylation have been observed, including global hypomethylation along the genome and hypermethylation at the specific sites, namely the CpG islands (CGIs) within the promoter regions, according to the decreased and increased the level of methyl group modification, respectively [4, 8, 10–12]. Disordered DNA methylation contributes to a number of human diseases, including breast cancer. Increased level of genome-wide hypomethylation results in increased chromosomal instability and activation of regulatory DNA sequences, including transcription of oncogenes, retrotransposons as well as genes encoding proteins involved in malignant cell development. DNA methylation refers to a covalent modification of cytosine ring at the 5′ position of a CpG dinucleotide by adding a methyl group in the 5th carbon of the ring using S-adenosyl methionine as a methyl donor (**Figure 1**) [8, 12].

This methylation process is catalysed by DNA (cytosine-5) methyltransferases (DNMTs). In mammalian, DNMTs are a highly conversed family protein encompassing DNMT1, DNMT2, DNMT3A, DNMT3B and DNMT3L, which could be distinguished by their function [13–15] (**Figure 2**). DNMT1 was the first methyltransferase to be discovered [1], then DNMT3 was discovered and characterized. Regarding to DNMTs function, DNMT3A and DNMT3B perform de novo methylation by adding the methyl groups to unmethylated CpG, which is responsible for the establishment of new methylation pattern in genomic DNA, whereas DNMT1, which has a high preference for hemi-methylated DNA, maintains the existence of methylation patterns following DNA replication on the newly synthesized strand [3, 4, 13, 14, 16, 17]. DNMT3L (DNA (cytosine-5-)-methyltransferase 3-Like) has no catalytic activity, DNMT3L has been shown to act as a general stimulatory factor for *de novo* methylation and facilitate methylation of DNMT3A and DNMT3B [2, 18].

The term CpG refers to the base cytosine (C) linked by a phosphate bond to the base Guanine (G) in the DNA nucleotide sequence, which usually cluster together in 'CpG islands (CGIs)' and typically locate at or near the promoters and transcription sites of genes. The molecular mechanisms underlying CpG island hypermethylation in many human cancers, including breast cancer, have been explored. The hypermethylation of CGIs located at tumour suppressor genes can result in transcriptional silencing of genes through a number of mechanisms, including (i) DNA hypermethylation directly affects the RNA polymerase II and DNA interactions by inhibiting the binding of transcriptional factors on specific sequences, such as AP-2, c-Myc/Myn, E2F, NF-*κ*B, etc. and (ii) hypermethylated DNA recruits methyl-CpG binding proteins (MeCP1 and MeCP2), and methyl-CpG binding domain protein (MBD1, MBD2, MBD3 and MBD4) [4].

**Figure 1.** (A) The DNMTs catalyse the methyl cytosine modification. (B) The structure of SAM and SAH.

**Figure 2.** The roles of DNMTs.

to introduce the reader to the concept of DNA methylation, especially DNA hypermethylation,

**2. DNA hypermethylation: a kind of epigenetic modification that plays a**

DNA methylation is one of the epigenetic mechanisms that is closely associated with normal cell development and a number of key processes including imprinting, X-chromosome inactivation, repression of repetitive element transcription, chromatin organization, etc. [7–9]. Aberrant methylation patterns are known to be presented in the genomes of cancer cells. Two patterns of aberrant methylation have been observed, including global hypomethylation along the genome and hypermethylation at the specific sites, namely the CpG islands (CGIs) within the promoter regions, according to the decreased and increased the level of methyl group modification, respectively [4, 8, 10–12]. Disordered DNA methylation contributes to a number of human diseases, including breast cancer. Increased level of genome-wide hypomethylation results in increased chromosomal instability and activation of regulatory DNA sequences, including transcription of oncogenes, retrotransposons as well as genes encoding proteins involved in malignant cell development. DNA methylation refers to a covalent modification of cytosine ring at the 5′ position of a CpG dinucleotide by adding a methyl group in the 5th carbon of the ring

This methylation process is catalysed by DNA (cytosine-5) methyltransferases (DNMTs). In mammalian, DNMTs are a highly conversed family protein encompassing DNMT1, DNMT2, DNMT3A, DNMT3B and DNMT3L, which could be distinguished by their function [13–15] (**Figure 2**). DNMT1 was the first methyltransferase to be discovered [1], then DNMT3 was discovered and characterized. Regarding to DNMTs function, DNMT3A and DNMT3B perform de novo methylation by adding the methyl groups to unmethylated CpG, which is responsible for the establishment of new methylation pattern in genomic DNA, whereas DNMT1, which has a high preference for hemi-methylated DNA, maintains the existence of methylation patterns following DNA replication on the newly synthesized strand [3, 4, 13, 14, 16, 17]. DNMT3L (DNA (cytosine-5-)-methyltransferase 3-Like) has no catalytic activity, DNMT3L has been shown to act as a general stimulatory factor for *de novo* methylation and

The term CpG refers to the base cytosine (C) linked by a phosphate bond to the base Guanine (G) in the DNA nucleotide sequence, which usually cluster together in 'CpG islands (CGIs)' and typically locate at or near the promoters and transcription sites of genes. The molecular mechanisms underlying CpG island hypermethylation in many human cancers, including breast cancer, have been explored. The hypermethylation of CGIs located at tumour suppressor genes can result in transcriptional silencing of genes through a number of mechanisms, including (i) DNA hypermethylation directly affects the RNA polymerase II and DNA interactions by inhibiting the binding of transcriptional factors on specific sequences, such as AP-2, c-Myc/Myn, E2F, NF-*κ*B, etc. and (ii) hypermethylated DNA recruits methyl-CpG binding proteins (MeCP1 and MeCP2), and methyl-CpG binding domain protein (MBD1, MBD2, MBD3 and MBD4) [4].

with examples of its involvement in human breast cancer.

148 Breast Cancer - From Biology to Medicine

**key role in silencing tumour suppressor genes**

using S-adenosyl methionine as a methyl donor (**Figure 1**) [8, 12].

facilitate methylation of DNMT3A and DNMT3B [2, 18].

Tumour suppressor genes (TSGs) normally suppress or negatively regulate cell proliferation by encoding proteins that block the action of growth-promoting proteins. A hallmark of cancer involves the loss of function of TSGs through the silencing genetic information. The silencing of TSGs by the high levels of 5-methylcytosine in their CpG island promoter regions, considered as the 'first and second hit', is equivalent to mutations and translocations, in Knudson's two-hit model of tumorigenesis [19, 20]. Here, the methyl groups become chemically bonded to the cytosine in CGIs, leading to disruption of the normally controlled cell proliferation and drive it to malignancy (**Figure 3**). Thus, the presence of m5CpG dinucleotide in tumour suppressor gene promoters is recognized as an important event in many human tumour types.

**Figure 3.** The typical CpG island of a tumour suppressor gene is represented in a normal and a tumour cell. White dots: unmethylated CpG; black dots: methylated CpG.

### **3. DNA methylation in circulation as a cancer biomarker**

The high presence of cell-free circulating tumour DNA (ctDNA), which is derived from primary tumour cells, can be found in blood and non-invasive samples of patients with cancer, such as urine, brochoalveolar lavage, mammary aspiration fluids, saliva, sputum, etc. makes an ideal candidate biomarker for prognosis and early diagnosis of breast cancer. ctDNA can be distinguished from circulating DNA derived from healthy cells by the presence of genomic aberrant modifications. For example, upon the tumour development, ctDNA carries tumour specific epigenetic modifications, i.e. DNA hypermethylation, is released due to the lysis of circulating cancer cells or micro-metastases. Therefore, the detection of genetic and epigenetic alterations in ctDNA offers a potential source of development of prognostic and predictive biomarkers for cancer. Quantitative evaluation of ctDNA can reflect tumour burden relevant to provide information on genetic and epigenetic profiles associated with human cancer development. The concentration of methylated ctDNA is presented in an even smaller portion of this amount, thus, presenting a challenging substrate to work with. Fortunately, even in the low concentration, ongoing technical developments and much of the progress in molecular biological techniques have provided a chance that they can be directly applied in ctDNA collection and validation even smaller amounts of ctDNA [10, 21, 22].

### **4. Hypermethylation of TSGs in breast cancer: a prognostic and early diagnostic indicator**

Tumour suppressor genes (TSGs) normally suppress or negatively regulate cell proliferation by encoding proteins that block the action of growth-promoting proteins. A hallmark of cancer involves the loss of function of TSGs through the silencing genetic information. The silencing of TSGs by the high levels of 5-methylcytosine in their CpG island promoter regions, considered as the 'first and second hit', is equivalent to mutations and translocations, in Knudson's two-hit model of tumorigenesis [19, 20]. Here, the methyl groups become chemically bonded to the cytosine in CGIs, leading to disruption of the normally controlled cell proliferation and drive it to malignancy (**Figure 3**). Thus, the presence of m5CpG dinucleotide in tumour suppressor gene promoters is recognized as an important event in many human tumour types.

**3. DNA methylation in circulation as a cancer biomarker**

unmethylated CpG; black dots: methylated CpG.

150 Breast Cancer - From Biology to Medicine

The high presence of cell-free circulating tumour DNA (ctDNA), which is derived from primary tumour cells, can be found in blood and non-invasive samples of patients with cancer, such as urine, brochoalveolar lavage, mammary aspiration fluids, saliva, sputum, etc. makes an ideal candidate biomarker for prognosis and early diagnosis of breast cancer. ctDNA can be distinguished from circulating DNA derived from healthy cells by the presence of genomic aberrant modifications. For example, upon the tumour development, ctDNA carries tumour specific epigenetic modifications, i.e. DNA hypermethylation, is released due to the lysis of circulating cancer cells or micro-metastases. Therefore, the detection of genetic and epigenetic alterations in ctDNA offers a potential source of development of prognostic and predictive biomarkers for cancer. Quantitative evaluation of ctDNA can reflect tumour burden relevant to provide information on genetic and epigenetic profiles associated with human cancer development. The concentration of methylated ctDNA is presented in an even smaller portion of this amount, thus, presenting a challenging substrate to work with. Fortunately, even in the low concentration, ongoing technical developments and much of the progress in

**Figure 3.** The typical CpG island of a tumour suppressor gene is represented in a normal and a tumour cell. White dots:

DNA aberrant methylation patterns, like hypermethylation of TSGs, global hypomethylation, etc. have been observed in human breast cancer. Silencing of TSGs expression by DNA hypermethylation provides a molecular mechanism by which DNA hypermethylation could trigger tumour development by interfering with the binding of transcription factors located at TSG gene's promoter. Thus, numerous studies have been attempted to focus on the role of hypermethylation of the TSG genes' promoter in breast cancer as well as the correlation between methylation of specific CGIs in TSGs and many breast cancer clinical states. **Table 1** shows the most relevant hypermethylated genes involve in various functions in breast cancer reported so far. Methylation of these TSG promoters is associated with the complete loss of TSG protein products in cancer cells and development of malignant phenotype.

**Table 1.** Examples of TSGs that undergo CpG island hypermethylation in breast cancer.

This DNA hypermethylation is a reversible signal, maybe due to the activity of Demethylase, which performs the reverse reaction to DNA methyltransferase and is an excellent candidate to be one of its important partners in shaping the methylation pattern of genomes [23, 24]. Thus, nowadays, many studies have been focused on an innovative approach in cancer treatments in which aimed to inhibit DNA hypermethylation and/or re-expression of silenced TSGs.

Therein, the hypermethylation of the CGIs promoter of *BRCA1* gene is now recognized as one of the most common molecular abnormalities associated with breast cancer development and is quoted as a significant example. *BRCA1* (Breast cancer 1) gene (HGNC: 1100; Entrez Gene: 672; OMIM: 113705; UniProtKB: P38398), which locates at 17q12-21, also known by many other names such as *IRIS*, *PSCP*, *BRCAI*, *BRCC1*, *RNF53*, *BROVCA1*, etc. is a tumour suppressor gene that conferred genetic pre-disposition to early onset of human breast and ovarian cancer [25–27]. This gene encodes a nuclear phosphoprotein that plays a role in maintaining genomic stability. The encoded protein combines with many other tumour suppressor, DNA damage sensors, and signal transducers to form a large multi-subunit protein complex that is called as BRCA1-associated genome surveillance complex (BASC). Therefore, the BRCA1 protein is involved in multifunction, such as repairing damaged DNA of doublestranded break, transcriptional regulation, ubiquitinylation, recombination and controlling the cell cycle check points as well as other functions. The hypermethylation of the *BRCA1* promoter has been considered as an inactivating mechanism of *BRCA1* expression, leading to breast tumourigenesis. In addition, some evidences have shown the significant association between the inactivation or low expression of BRCA1 protein expression and the aberrant methylation status of CGIs in the *BRCA1* promoter in breast cancer tumorigenesis.

It is well known that breast cancer constitutes a heterogeneous complex of diseases characterized by different distinct morphologies, biological behaviours and clinical outcomes. The classification and diagnosis of breast cancer have been based on the expression of different proteins, including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) [28, 29]. An example of such a target molecular therapy is Trastuzumab (Herceptin®), which has been approved to directly against HER2-expressing tumours. Among the variety of breast cancer types, a subtype called triple-negative breast cancer (TNBC), which is clinically defined by the lack of expression of ER, PR and HER2, presents a challenge for effective clinical management [28]. Therefore, it is essential to find a reliable biomarker, which are not only useful for the screening, early diagnosis and prognosis prediction for breast cancer, but also provide insight into the mechanisms driving tumourigenesis as well as an innovative approach in breast cancer treatments.

Over the past few years, a considerable amount of studies has been conducted to evaluate the association between *BRCA1* promoter methylation and many clinicopathological characteristics of breast cancer. Therefore, tentatively, a meta-analysis was carried out, a total of 44 studies including 25 case-control studies and 19 cohort studies were eligible, enrolled into the metaanalysis research. According to our research, the prevalence of the hypermethylated *BRCA1* promoter has been reported to fall in the range from 9.1 to 59.2%, which was statistically significant higher in breast cancers than non-cancerous controls (*OR* = 4.00, 95% CI= 2.336–6.878, *P* < 0.001, **Figure 4**). Because of large heterogeneity (*PH* ≤ <0.0001, *I* <sup>2</sup> = 73.82%), we continued to clarify the potential source of heterogeneity via stratified analysis based on sample materials, methods for identifying methylation and ethnicity; with the detailed results were summarized in **Table 2**.

As shown in **Table 2**, the pooled OR for *BRCA1* promoter hypermethylation in breast cancer tissues was 4.312 (95% CI = 2.395–7.765, *P* < 0.001) compared with normal or benign tissues, and was higher than the pooled OR in peripheral blood of breast cancer patients (*OR* = 2.485, 95% CI = 1.433–4.310, *P* = 0.001) compared with non-cancer controls. In addition, the pooled OR for *BRCA1* promoter hypermethylation detected by MSP was 5.059 (95% CI = 2.214–11.561, *P* < 0.001), significant higher than other methods (*OR* = 2.506; 95% CI = 1.409–4.457, *P* = 0.002). Meanwhile, the frequency of *BRCA1* promoter hypermethylation in Asians (*OR* = 4.006, 95% CI = 2.122–7.560; *P* < 0.001) was higher than in Caucasians (*OR* = 2.291, 95% CI = 1.147– 4.576, *P* = 0.006). Furthermore, our studies also demonstrated that the *BRCA1* promoter hypermethylation was significant correlated with the clinicopathological characteristics which included ages, meant that the prevalence of hypermethylation status was higher in the group of age under 55 (*OR* = 1.227, 95% CI = 1.604–1.414, *P* = 0.05) (**Figure 5**); histological grade, meant that the hypermethylated *BRCA1* in the case of histological grade 3 and 4 was higher than in the histological grade 1 and 2 (*OR* = 1.858, 95% CI = 1.499–2.301, *P* < 0.001) (**Figure 6**); disease stages, meant that the prevalence of the hypermethylation of *BRCA1* gene in the case of late stages was higher than in early stages (*OR* = 1.339, 95% CI = 1.023–1.752, *P* = 0.033) (**Figure 7**). Additionally, the hypermethylation status of *BRCA1* gene's promoter was correlated with the ER(−) (*OR*= 2.02, 95% CI = 1.525–2.675, *P* < 0.001), PR(−) (*OR* = 1.823, 95% CI = 1.374–2.41, *P* < 0.001) and especially with triple-negative phenotype (*OR* = 2.814, 95% CI = 1.811–4.371, *P* < 0.001) under fixed or random effect mode (**Figure 8**). Thus, those meta-analysis results confirmed that the *BRCA1* promoter hypermethylation was significant correlated with the increased risk of breast cancer, associated with several specific clinicopathological characteristics of breast cancer, which indicated that *BRCA1* promoter hypermethylation could be utilized as an effective biomarker in predictive and diagnostic breast cancer.

suppressor gene that conferred genetic pre-disposition to early onset of human breast and ovarian cancer [25–27]. This gene encodes a nuclear phosphoprotein that plays a role in maintaining genomic stability. The encoded protein combines with many other tumour suppressor, DNA damage sensors, and signal transducers to form a large multi-subunit protein complex that is called as BRCA1-associated genome surveillance complex (BASC). Therefore, the BRCA1 protein is involved in multifunction, such as repairing damaged DNA of doublestranded break, transcriptional regulation, ubiquitinylation, recombination and controlling the cell cycle check points as well as other functions. The hypermethylation of the *BRCA1* promoter has been considered as an inactivating mechanism of *BRCA1* expression, leading to breast tumourigenesis. In addition, some evidences have shown the significant association between the inactivation or low expression of BRCA1 protein expression and the aberrant

152 Breast Cancer - From Biology to Medicine

methylation status of CGIs in the *BRCA1* promoter in breast cancer tumorigenesis.

genesis as well as an innovative approach in breast cancer treatments.

**Figure 4**). Because of large heterogeneity (*PH* ≤ <0.0001, *I*

It is well known that breast cancer constitutes a heterogeneous complex of diseases characterized by different distinct morphologies, biological behaviours and clinical outcomes. The classification and diagnosis of breast cancer have been based on the expression of different proteins, including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) [28, 29]. An example of such a target molecular therapy is Trastuzumab (Herceptin®), which has been approved to directly against HER2-expressing tumours. Among the variety of breast cancer types, a subtype called triple-negative breast cancer (TNBC), which is clinically defined by the lack of expression of ER, PR and HER2, presents a challenge for effective clinical management [28]. Therefore, it is essential to find a reliable biomarker, which are not only useful for the screening, early diagnosis and prognosis prediction for breast cancer, but also provide insight into the mechanisms driving tumouri-

Over the past few years, a considerable amount of studies has been conducted to evaluate the association between *BRCA1* promoter methylation and many clinicopathological characteristics of breast cancer. Therefore, tentatively, a meta-analysis was carried out, a total of 44 studies including 25 case-control studies and 19 cohort studies were eligible, enrolled into the metaanalysis research. According to our research, the prevalence of the hypermethylated *BRCA1* promoter has been reported to fall in the range from 9.1 to 59.2%, which was statistically significant higher in breast cancers than non-cancerous controls (*OR* = 4.00, 95% CI= 2.336–6.878, *P* < 0.001,

potential source of heterogeneity via stratified analysis based on sample materials, methods for identifying methylation and ethnicity; with the detailed results were summarized in **Table 2**.

As shown in **Table 2**, the pooled OR for *BRCA1* promoter hypermethylation in breast cancer tissues was 4.312 (95% CI = 2.395–7.765, *P* < 0.001) compared with normal or benign tissues, and was higher than the pooled OR in peripheral blood of breast cancer patients (*OR* = 2.485, 95% CI = 1.433–4.310, *P* = 0.001) compared with non-cancer controls. In addition, the pooled OR for *BRCA1* promoter hypermethylation detected by MSP was 5.059 (95% CI = 2.214–11.561, *P* < 0.001), significant higher than other methods (*OR* = 2.506; 95% CI = 1.409–4.457, *P* = 0.002). Meanwhile, the frequency of *BRCA1* promoter hypermethylation in Asians (*OR* = 4.006, 95% CI = 2.122–7.560; *P* < 0.001) was higher than in Caucasians (*OR* = 2.291, 95% CI = 1.147– 4.576, *P* = 0.006). Furthermore, our studies also demonstrated that the *BRCA1* promoter hypermethylation was significant

<sup>2</sup> = 73.82%), we continued to clarify the

**Figure 4.** Forest plot for evaluating the association between *BRCA1* promoter methylation and breast cancer under fixed or random effect mode.

 This needs to find a more sensitive and specific biomarker, which can be a powerful prognostic indicator and help make therapeutic decisions to prolong the survival time of patients. Then, we included 10 articles provide disease-free survival (DFS) and/or overall survival (OS) to evaluate the role of the *BRCA1* promoter hypermethylation in the prognosis of breast cancer. Overall survival (OS), which was defined as the length of time from either the date of diagnosis or the start of treatment for breast cancer, that patients diagnosed with the disease are still alive, and disease-free survival (DFS), which was defined that the length of time after primary treatment for a cancer ends that the patient survives without any signs or symptoms of that cancer. In detail, in the Asian population, the OS and DFS were 2.163 (95% CI = 1.212–3.858, *P* < 0.001) and 2.47 (95% CI = 1.331–4.584, *P* = 0.004), respectively, using single variable analysis. In the case of using multiple variables analysis, the OS and DFS were 1.611 (95% CI = 1.116–2.324, *P* = 0.011), and 2.872 (95% CI = 1.389– 5.937, *P* = 0.004), respectively. Those analytic results indicated that hypermethylated *BRCA1* gene's promoter was significant associated with OS, DFS, meant that it was poor prognosis to breast cancer patients, in both single and multiple variables analysis. Hence, *BRCA1* promoter hypermethylation is suggested to be a potential biomarker for prognostic assessment.

*Note*: *N*: the total number of eligible studies; Caucasians included: American and Europeans, Australians. *P*H: the *P*-value of *Q* test for heterogeneity among studies; F: fixed-effects model; R: random-effects model; NA: non-analysis.

#### DNA Hypermethylation in Breast Cancer http://dx.doi.org/10.5772/66900 155

without any signs or symptoms of that cancer. In detail, in the Asian population, the OS and DFS were 2.163 (95% CI = 1.212–3.858, *P* < 0.001) and 2.47 (95% CI = 1.331–4.584, *P* = 0.004), respectively, using single variable analysis. In the case of using multiple variables analysis, the OS and DFS were 1.611 (95% CI = 1.116–2.324, *P* = 0.011), and 2.872 (95% CI = 1.389– 5.937, *P* = 0.004), respectively. Those analytic results indicated that hypermethylated *BRCA1* gene's promoter was significant associated with OS, DFS, meant that it was poor prognosis to breast cancer patients, in both single and multiple variables analysis. Hence, *BRCA1* promoter hypermethylation is suggested to be a potential biomarker for prognostic assessment.

**Test of association Test of heterogeneity**

**Variables** *N* **OR (95% CI)** *Z P***-value Model Variables** *N* Total 25 4.00 (2.336–6.878) 5.04 <0.001 *R* <0.0001 73.82%

Tissue 22 4.312 (2.395–7.765) 4.87 <0.001 *R* 0.0003 58.32% Blood 10 2.485 (1.433–4.310) 3.24 0.001 *R* 0.0045 60.78%

MSP 15 5.059 (2.214–11.561) 3.845 <0.001 *R* 0.0001 67.89% Others 10 2.506 (1.409–4.457) 3.126 0.002 *R* 0.0049 61.97%

Caucasian 10 2.291 (1.147–4.576) 2.349 0.006 *R* 0.0375 49.25% Asian 14 4.006 (2.122–7560) 4.282 <0.001 *R* 0.0060 55.60% Africa 1 18.5217 (6.917–49.59) 5.809 <0,001 NA NA NA *Note*: *N*: the total number of eligible studies; Caucasians included: American and Europeans, Australians. *P*H: the *P*-value of *Q* test for heterogeneity among studies; F: fixed-effects model; R: random-effects model; NA: non-analysis.

**Table 2.** Overall and subgroups analyses of *BRCA1* methylation and breast cancer risk in 25 cases control studies.

**Figure 5.** Forest plot for evaluating the association between *BRCA1* promoter methylation and ages under fixed or

random effect mode.

Material

154 Breast Cancer - From Biology to Medicine

Methods

Ethnicity

**Figure 6.** Forest plot for evaluating the association between *BRCA1* promoter methylation and histological tumour grades under fixed or random effect mode.

**Figure 7.** Forest plot for evaluating the association between *BRCA1* promoter methylation and disease stages under fixed or random effect mode.

**Figure 8.** Forest plot for evaluating the association between *BRCA*1 promoter methylation and triple negative phenotype under fixed or random effect mode.

### **5. DNA hypermethylation-targeted drug in cancer therapy**

The process of DNA methylation is catalysed by DNMTs which typically occurs at CpG dinucleotides. As mentioned earlier, it is also a reversible process. Removal of a methyl group from DNA must involve a cleavage of a carbon-carbon bond, which is carried out by DNA demethylase (dMTase). In addition, the methylation reaction can be blocked by the inhibitors of DNA methylation drugs, such as 5-azacytidine, 5-aza-2′-deoxycytidine, etc. which contains a nitrogen in the place of carbon at 5′ position of cytosine ring (**Figure 9**) [30]. This drug is cooperated into DNA, then, replaces the natural base cytosine and acts as a potent inhibitor of the DNMTs, inducing the DNA demethylation [31]. Since DNA methylation is reversible, an aberrant hypermethylation of tumour suppression genes can be reverted. This consequently supports DNA methyltransferases (DNMTs) as attractive therapeutic targets. Indeed, epigenetic drugs (epi-drug)—methylation inhibitors through DNMT inactivation, used alone or in combination with other biomarkers, including by dietary agents, for targeted preventive and therapeutic interventions, have attracted attention recently.

**Figure 9.** Inhibition of DNMTs by 5-azacytidine.

DNMT inhibitors (DNMTi), such as 5-azacytidine (azacitidine) and 5-aza-2′-deoxycytidine (decitabine) (**Figure 10**), are epi-drugs which are first announced and currently marketed as hypomethylation therapeutics. They are nucleoside analogues, derivatives of cytidine that work by incorporating into the DNA sequence at cytosine positions during DNA replication to be active and then form a suicidal covalent complex with the DNMTs. These drugs have been approved by Food and Drug Administration (FDA) for clinical tests on the myelodysplastic syndrome, malignant mesothelioma, pre-leukemic disease, breast cancer, nasopharyngeal carcinoma and some other diseases.

**Figure 10.** The structure of 5-azacytidine and 5-aza-2′-deoxycytidine.

**5. DNA hypermethylation-targeted drug in cancer therapy**

156 Breast Cancer - From Biology to Medicine

The process of DNA methylation is catalysed by DNMTs which typically occurs at CpG dinucleotides. As mentioned earlier, it is also a reversible process. Removal of a methyl group from DNA must involve a cleavage of a carbon-carbon bond, which is carried out by DNA demethylase (dMTase). In addition, the methylation reaction can be blocked by the inhibitors of DNA methylation drugs, such as 5-azacytidine, 5-aza-2′-deoxycytidine, etc. which contains a nitrogen in the place of carbon at 5′ position of cytosine ring (**Figure 9**) [30]. This drug is cooperated into DNA, then, replaces the natural base cytosine and acts as a potent inhibitor of the DNMTs, inducing the DNA demethylation [31]. Since DNA methylation is reversible, an aberrant hypermethylation of tumour suppression genes can be reverted. This consequently supports DNA methyltransferases (DNMTs) as attractive therapeutic targets. Indeed, epigenetic drugs (epi-drug)—methylation inhibitors through DNMT inactivation, used alone or in combination with other biomarkers, including by dietary agents, for tar-

geted preventive and therapeutic interventions, have attracted attention recently.

DNMT inhibitors (DNMTi), such as 5-azacytidine (azacitidine) and 5-aza-2′-deoxycytidine (decitabine) (**Figure 10**), are epi-drugs which are first announced and currently marketed as hypomethylation therapeutics. They are nucleoside analogues, derivatives of cytidine that work by incorporating into the DNA sequence at cytosine positions during DNA replication to be active and then form a suicidal covalent complex with the DNMTs. These drugs have been approved by Food and Drug Administration (FDA) for clinical tests on the myelodysplastic syndrome, malignant mesothelioma, pre-leukemic disease, breast cancer, nasopharyn-

geal carcinoma and some other diseases.

**Figure 9.** Inhibition of DNMTs by 5-azacytidine.

Zebularine is another cytidine analog that has a mechanism similar to 5-azacytidine, integrating into DNA and forming a covalent bond with DNMT1, resulting in inhibition of methylation reaction. Moreover, Zebularine is reported that it is a DNMT1 inhibitor with low toxicity and has a high sensitivity in selective cancer cells. Particularly, this drug showed the reactivated functions on some important tumour suppressor genes that were disrupted in breast cancer cell lines, even at low concentrations. Although the drug is not yet FDA approved, a preclinical study on mouse models showed that Zebularine can inhibit DNA methylation and induce re-expression-silenced gene, even given orally.

Other trends related to DNA methylation including the inhibition of DNMTs through siRNA, ribozymes, antisense oligonucleotides have also been considered. Some drugs have proven effective impact on cell cultures, animal models and clinical trials as well such asMG98, a 20 bp anti-sense oligonucleotide that directly prevents the translation of DNMT1 or RG108—a new small molecule that can act on active site of DNMT1. Unlike the nucleoside analogs, RG108 did not demonstrate cytotoxic or genotoxic effects on cells even at high concentrations.

The combination of the histone deacetylase inhibitors (HDACi), such as Trichostatin A (TSA) and phenylbutyrate, with DNMTi is a new trend giving promising efficacy in the treatment of cancer. In breast cancer, triple negative metastatic patients that do not express estrogen receptor (ER), progesterone receptor (PR) and HER, do not respond to agents such as trastuzumab (Herceptin) and tamoxifen. Particularly, the loss of ER in some triple negative breast cancers is epigenetically silenced by abnormal methylation and histone modifications. Consequently, the patients express the resistance of anti-estrogen. Triple negative metastatic breast cancer patients were pre-treated with decitabine—a DNMTi and LBH 589—an HDACi, to restore the ER and then treated with tamoxifen. This combination can remove the epigenetic modifications including DNA methylation and histone deacetylation and reactivate ER. Thus, this reactivated ER cells become sensitive to agents like tamoxifen. Similarly, the combination of azacitidine with TSA also induces the re-expression of ER function to increase the sensitivity of breast cancer cell lines that previously show negative expression with ER in tamoxifen therapy or the combination of HDACi and trastuzumab has taken to effectively suppression of the development and apoptosis induction into breast cancer cells lines.

In addition, the combination of epi-drugs with chemotherapeutic agents or natural dietary ingredients also increases the effectiveness of treatment. A pre-clinical study has shown that the combination of decitabine and docetaxel (an anti-mitotic drug) can increase treatment outcomes against cancer cells in experiments conducted on breast cancer cell lines [32, 33]. Decitabine in combination with another substance, amsacrine or idarubicin, also shows therapeutic effect. Green tea polyphenol, (-)-epigallocatechin-3-gallate (EGCG), may cause re-modelling of chromatin structure and the ERα promoter by histone acetylation and DNA methylation mechanisms, and consequently reactivating ERα. The combination of TSA and EGCG leads to reactivation of numerous tumour suppressor genes by inhibiting directly or indirectly DNMTs. Dietary sulforaphane—an inhibitor of histone acetylation also shows very effective activity in the inhibition of proliferation and survival of breast cancer cells without affecting normal cells.

Therefore, methylation combined therapy is very promising in the treatment of breast cancer. Clinical trials in the combination of trastuzumab with HDACi for the treatment of breast cancer, and a phase II trial in breast cancer—valproic acid combined with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) also are being investigated. Up to date, several other classes of epi-drug have been studied, developed with new drugs, which based on the DNMT inhibitors, HDAC inhibitors, HMT inhibitors, etc. in early preclinical trial development.

### **6. Conclusion**

DNA hypermethylation has become established in recent years as being one of the important causes of breast tumorigenesis and potential biomarkers in clinical applications, prognosis and early diagnosis of breast cancer. As the release of tumour-associated DNA into body fluids, thus the screening of plasma or serum DNA may provide information on epigenetic profiles which are tightly associated with breast cancer development, progression and response to therapies. This is the real advantage of an aberrant DNA methylation property as a great versatility, promising biomarker for the molecular monitoring of cancer patients, and applied in early detection, prognosis and predicting drug sensitivity in cancer.

### **Abbreviations**

### **Author details**

ment outcomes against cancer cells in experiments conducted on breast cancer cell lines [32, 33]. Decitabine in combination with another substance, amsacrine or idarubicin, also shows therapeutic effect. Green tea polyphenol, (-)-epigallocatechin-3-gallate (EGCG), may cause re-modelling of chromatin structure and the ERα promoter by histone acetylation and DNA methylation mechanisms, and consequently reactivating ERα. The combination of TSA and EGCG leads to reactivation of numerous tumour suppressor genes by inhibiting directly or indirectly DNMTs. Dietary sulforaphane—an inhibitor of histone acetylation also shows very effective activity in the inhibition of proliferation and survival of breast cancer cells without

Therefore, methylation combined therapy is very promising in the treatment of breast cancer. Clinical trials in the combination of trastuzumab with HDACi for the treatment of breast cancer, and a phase II trial in breast cancer—valproic acid combined with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) also are being investigated. Up to date, several other classes of epi-drug have been studied, developed with new drugs, which based on the DNMT inhibitors, HDAC inhibitors, HMT inhibitors, etc. in early preclinical trial

DNA hypermethylation has become established in recent years as being one of the important causes of breast tumorigenesis and potential biomarkers in clinical applications, prognosis and early diagnosis of breast cancer. As the release of tumour-associated DNA into body fluids, thus the screening of plasma or serum DNA may provide information on epigenetic profiles which are tightly associated with breast cancer development, progression and response to therapies. This is the real advantage of an aberrant DNA methylation property as a great versatility, promising biomarker for the molecular monitoring of cancer patients, and applied

in early detection, prognosis and predicting drug sensitivity in cancer.

BASC BRCA1-associated genome surveillance complex

*APC* Adenomatous Polyposis Coli

ctDNA Cell-free circulating tumour DNA

DNMTi DNA methyltransferase inhibitors

*BRCA1* Breast cancer 1 CGIs CpG islands

CI Confidence interval

DFS Disease free survival

affecting normal cells.

158 Breast Cancer - From Biology to Medicine

development.

**6. Conclusion**

**Abbreviations**

Le Huyen Ai Thuy\*, Lao Duc Thuan and Truong Kim Phuong

\*Address all correspondence to<EMAIL_ADDRESS>
Faculty of Biotechnology, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam

### **References**

[19] Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet. 1999;**21**(2):163–7. DOI: 10.1038/5947.

[4] Luczak MW, Jagodziński PP. The role of DNA methylation in cancer development. Folia

[5] Chuang JC, Jones PA. Epigenetics and microRNAs. Pediatr Res. 2007;**61**(5 Pt 2):24R–29R.

[6] Muntean AG, Hess JL. Epigenetic dysregulation in cancer. Am J Pathol. 2009;**175**(4):1353–

[7] Jones M, Goodman SJ, Kobor MS. DNA methylation and healthy human aging. Aging

[8] Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;**6**(8):297–610.

[9] Sadikovic B, Al-Romaih K, Squire JA, Zielenska M. Cause and consequences of genetic and epigenetic alterations in human cancer. Curr Genomics. 2008;**9**(6):394–408. DOI:

[10] Diaz LA Jr, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol.

[11] Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene. 2002;**21**(35):5427–40. DOI: 10.1038/sj.onc.1205600. [12] Yuanyuan D, Haiyang Z, Haiyan L, Xiaokun L, Shulin Y. DNA methylation as an early diagnostic marker of cancer (Review). Biomed Rep. 2014;**2**(3):326–30. DOI: 10.3892/

[13] Bestor T, Laudano A, Mattaliano R, Ingram V. Cloning and sequencing of a cDNA encoding DNA methyltransferase of mouse cells. The carboxyl-terminal domain of the mammalian enzymes is related to bacterial restriction methyltransferases. J Mol Biol.

[14] Kafri T, Ariel M, Brandeis M, Shemer R, Urven L, McCarrey J, et al. Developmental pattern of gene-specific DNA methylation in the mouse embryo and germ line. Genes Dev.

[15] Zhang L, Long X. Association of BRCA1 promoter methylation with sporadic breast cancers: Evidence from 40 studies. Sci Rep. 2015;**5**:17869. DOI: 10.1038/srep17869. [16] Bestor TH. The DNA methyltransferases of mammals. Hum Mol Genet. 2000;**9**(16):2395–

[17] Okano M, Xie S, Li E. Cloning and characterization of a family of novel mammalian DNA (cytosine-5) methyltransferases. Nat Genet. 1998;**19**(3):219–20. DOI: 10.1038/890.

[18] Chen ZX, Mann JR, Hsieh CL, Riggs AD, Chédin F. Physical and functional interactions between the human DNMT3L protein and members of the de novo methyltransferase

family. J Cell Biochem. 2005;9**5**(5):90–17. DOI: 10.1002/jcb.20447.

Histochem Cytobiol. 2006;**44**(3):143–54.

DOI: 10.1203/pdr.0b013e3180457684.

61. DOI: 10.2353/ajpath.2009.081142.

DOI: 10.1038/nrg1655.

160 Breast Cancer - From Biology to Medicine

br.2014.237.

10.2174/138920208785699580.

Cell. 2015;**14**(6):924–32. DOI: 10.1111/acel.12349.

2014;**32**(6):579–86. DOI: 10.1200/JCO.2012.45.2011.

1988;**203**(4):971–83. DOI: 10.1016/0022-2836(88)90122-2.

1992;**6**(5):705–14. DOI: 10.1101/gad.6.5.705.

402. DOI: 10.1093/hmg/9.16.2395.

### **ErbB2 Receptor in Breast Cancer: Implications in Cancer Cell Migration, Invasion and Resistance to Targeted Therapy Provisional chapter ErbB2 Receptor in Breast Cancer: Implications in Cancer Cell Migration, Invasion and Resistance to Targeted**

Maria del Pilar Camacho-Leal, Maria del Pilar Camacho-Leal,

**Therapy**

Marianna Sciortino and Sara Cabodi Marianna Sciortino and Sara Cabodi

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66902

#### **Abstract**

Overexpression of ErbB2 is found in several types of human carcinomas. In breast tumors, ErbB2 overexpression is detected in up to 20% of patients. Breast cancers in with amplification of ErbB2 are characterized by rapid tumor growth, lower survival rate and increased disease progression. The molecular mechanisms underlying the oncogenic action of ErbB2 involve a complex signaling network that tightly regulates malignant cell migration and invasion and hence metastatic potential. Recent efforts have been made to identify gene expression signatures of ErbB2-positive invasive breast cancers that may represent important mediators of ErbB2-induced tumorigenesis and metastatic progression.

In this chapter, we will discuss the canonical ErbB2 signaling pathways responsible for tumor growth and dissemination along with newly identified mediators such as adaptor protein p130Cas and miRNAs. From a therapeutic point of view, the treatment with anti-ErbB2 monoclonal antibody trastuzumab has greatly improved the outcomes of patients with ErbB2 aggressive cancer. Nevertheless, *de novo* and acquired resistance to trastuzumab therapy still represent a major clinical problem. In the second part of the chapter, we will provide an overview of the mechanisms so far implicated in the onset of resistance to targeted therapy and of the new strategies to overcome resistance.

and reproduction in any medium, provided the original work is properly cited.

**Keywords:** ErbB2, breast cancer, molecular mechanisms, treatment

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution

### **1. Introduction**

Breast cancer is the leading cause of cancer-related death in women worldwide [1]. Despite significant advances in breast cancer diagnosis and treatment, several major unresolved clinical and scientific problems still remain, such as the understanding of the causes of tumor progression and resistance and how to predict them.

ErbB2 is a well-known oncoprotein that belongs to the epidermal growth factor receptor epidermal growth factor receptor (EGFR) family. It is overexpressed approximately in 20% of invasive breast cancers [2]. In particular, overexpression of ErbB2 has been demonstrated to promote breast cancer invasion and metastasis and to correlate with poor patient survival [3–6]. ErbB2 is also overexpressed in noninvasive mammary ductal carcinomas *in situ* (DCIS) [7]. Indeed, ErbB2 amplification or overexpression seems to be crucial but not sufficient for the transition from *in situ* to invasive cancer and additional hits are required for the progression of ErbB2-positive tumors. Although the molecular and genetic events underlying ErbB2-positive tumor invasion and metastasis are still not fully understood, intense investigation has led to the notion that molecules involved in cell adhesion and migration are critical in this process [8].