Sage Journals: Discover world-class research

Abstract

Circulating tumor cells are low-frequency cells that are shed into the peripheral bloodstream from a primary solid tumor and/or metastasis. Although these cells were recognized initially in 1869, it is only in the past 2 decades that they have been isolated for use as a surrogate biomarker to monitor response to therapy, evaluate prognosis, detect tumor mutations, assist in selecting personalized medicine, and enable earlier cancer diagnosis.

Keywords

circulating tumor cells enrichment response to therapy prognostic evaluation heterogeneity personalized treatment early diagnosis

Introduction

The occurrence of distant metastases is the cause of nearly all cancer deaths. Circulating tumor cells (CTCs) are more likely to be detected in patients with metastatic cancer, and the number of CTCs significantly increases with tumor progression.¹ The number of CTCs also changes in response to anticancer treatment, and therefore, CTCs are a promising marker for evaluating curative effects. So CTCs represent an opportunity for clinicians to identify the best therapy for individuals with cancer over the course of the disease.² The presence of CTCs has also been associated with overall survival (OS) in several tumor types, such as colorectal, prostate and breast cancer. The CTCs have potential as a biomarker for evaluating prognosis, which is independent of the traditional “gold-standard” pathologic Tumor Node Metastasis (TNM) staging. In the clinic, targeted therapy decisions for cancer are generally guided by the primary tumor genotype. However, tumor genotypes change over time, and we are not able to monitor this with tumor biopsies or surgical specimens.³ The isolation of CTCs from blood, which is a minimally invasive procedure, is performed to investigate the ability of CTCs to represent the full spectrum of mutations in a primary tumor and metastastic sites as a minimally invasive procedure. Here, we review the progress regarding CTCs as a biomarker and focus on isolation and enrichment methods, response to therapy, prognostic evaluation, anticancer treatment guidance, early diagnostic value, and areas for future improvement.

Detection and Enrichment Techniques

The fundamental challenge in analyzing CTCs is its extremely low concentration in blood, occurring at rates as low as 1 cell per 10⁶ or 10⁷ leukocytes.⁴ Efficient techniques are required to distinguish CTCs from peripheral blood cells for enrichment. Techniques for CTC enrichment can be classified as positive enrichment (capturing target cells and eluting nontarget cells) or negative enrichment (removing nontarget cells), which are performed based on biological or physical properties.

Positive enrichment is the most commonly employed strategy for separating CTCs, and this method is mainly based on an affinity-binding system. This system distinguishes target cells that express certain CTC-specific antigens that are not expressed by blood cells. For example, CTCs can be positively enriched using an anti-epithelial cell-adhesion molecule (EpCAM) antibody combined with magnetic particles, and the mixture is captured in a magnetic field and collected on a glass slide. CellSearch (Veridex; Warren, NJ, USA) is the only platform approved by the Food and Drug Administration (FDA) for detecting CTCs by targeting cell markers, such as EpCAM and cytokeratins 8, 18, and 19, in metastatic breast, colon, and prostate cancer.⁴ Although positive enrichment methods are able to isolate CTCs at a high purity, they have significant limitations. The EpCAM is not expressed in nonepithelial cancers (eg, ovarian cancer) and certain epithelial cancers (eg, renal cell cancer). Furthermore, circulating epithelial cells have been observed in patients with benign colon diseases.⁵ In addition, some epithelial antigens on CTCs will be downregulated or lost because of epithelial–mesenchymal transition (EMT).⁶ A prospective study demonstrated that only 32% (19 of 60) of patients with distant metastatic non-small-cell lung cancer (NSCLC) had positive CTCs at baseline using the CellSearch System.⁷ To resolve this problem, new biomarkers that cover the complex heterogeneity of tumor cells are required. For example, protein plastin 3 was discovered as a novel CTC marker that is not downregulated during EMT and is not expressed in blood cells.⁸ It is promising to realize the broad-spectrum enrichment of all CTCs.

Negative enrichment is another efficient strategy for capturing CTCs by depleting normal hematopoietic cells based on their physical properties, such as size, density, and deformability. Tumor cells are generally bigger (>8 µm in size) and stiffer than normal blood cells, which can be filtered by a membrane- and filtration-based system. Thus, different devices based on cell filtration and centrifugal force have been recently developed.^9
–11 Membrane-based filter systems, such as isolation by size of epithelial tumor cells (ISET), have demonstrated to be an efficient, inexpensive, and quick method for enriching CTCs.¹² A direct comparison of CellSearch and ISET for CTCs isolation demonstrated that ISET performed better than CellSearch in detecting CTCs in patients with metastatic prostate carcinoma and metastatic lung carcinoma.¹³ However, this method should be further developed to overcome the inherent insufficiency that filter pores are likely to be blocked by CTCs or leukocytes. Ficoll-Hypaque centrifugation is an example of an approach that addresses this problem, but a substantial loss of CTCs is unavoidable. Considering these issues, a centrifugal force-based size-selective platform was proposed to isolate and enrich CTCs with a high purity. The capture efficiency for whole blood samples varied from 44% to 84%.¹⁴ The CTCs can also be captured based on biological characteristics. For example, the characteristics of normal blood cells, such as CD45 expression on leukocytes, were used to isolate heterogenetic CTCs. Using a bead-conjugated anti-CD45⁺ antibody, leukocytes are negatively depleted in a special magnetic field. The CTCs can also be enriched based on electric charge. Under a particular medium conductivity, different types of cells exhibit different dielectrophoresis (DEP) behavior. A study demonstrated that DEP using a DEP-based microfluidic chip attained enrichment efficiencies as high as 93% with high specificity based on the electric charge of breast cancer CTCs.¹⁵

It is controversial whether positive or negative enrichment is more efficient. Positive enrichment can be used to isolate CTCs with high purity, but there are significant limitations; namely, CTCs do not all express the same specific antigens because of heterogeneity. Thus, systems that target different epithelial antigens or novel biomarkers that are expressed by all CTCs might be advantageous. Generally, negative enrichment approaches are easy and highly sensitive but have low specificity. Undoubtedly, the latter is more advantageous in that the target cells remain intact and heterogeneous. This provides researchers with the opportunity for downstream molecular analysis. In addition, positive and negative enrichment can be integrated based on physical and biological characteristics. For example, CTCs can be first selected based on their larger size, which discards the smaller leukocytes; CTCs can be subsequently immunostained with bead-conjugated antibodies against EpCAMs and then captured in a magnetic field.¹⁶ Basically, various platforms are being developed to overcome the problem of the sensitivity/specificity trade-off.

Recently, a new microfluidics platform called “CTC-chip” was developed rapidly. Current microfluidics technology-based methods for separating CTCs can also be classified as positive enrichment (ie, using anti-EpCAM-coated microfluidic channels or affinity-based magnetophoresis) or negative enrichment (ie, using size-based filtration, size-based hydrophoresis, or normal cells depleting using an anti-CD45 antibody-coated microfluidic channel). Several microfluidics-based platforms have been described.¹⁷ This CTC-chip enables the detection of CTCs in 1 mL of blood from almost all patients with metastatic cancer; this method has a high detection efficiency (approximately 100%), even in patients without metastatic disease, and further investigations on assay specificity are therefore warranted.^18
–20 It also has the advantages of being low cost and high-throughput as well as providing the ability to perform downstream molecular analyses. This microfluidic platform is believed to have laid a solid foundation for clinical practice.

Monitoring Therapeutic Response

Changes in the number or genotype of CTCs can provide a reliable marker of clinical response to anticancer treatment by sensitive detection methods such as CTC-chip.^20,21 Various studies have been published supporting this as a powerful tool for monitoring therapeutic response, predicting clinical outcome, and realizing accurate, early decision making.

Genotypic changes may occur during disease progression and may predict a lack of response to targeted therapy. Tyrosine kinase inhibitors (TKIs) are effective in patients with NSCLC with an EGFR mutation but are limited by mutations that confer resistance to anti-EGFR therapies, such as the T790M mutation.²² Maheswaran et al used CTCs as a noninvasive strategy to monitor tumor genotypic changes during personalized targeted treatment.²³ The CTCs were enriched from 27 patients with NSCLC, and an Epidermal Growth Factor Receptor (EGFR) mutation analysis was performed on DNA from CTCs and tumor-biopsy specimens using polymerase chain reaction; T790M mutation analysis was also performed on CTCs from patients with EGFR mutations who had received TKIs. The results showed that EGFR mutations were detected in 92% of patients and that the presence of T790M in CTCs correlated with worse progression-free survival (PFS). These data suggested the possibility of monitoring genotypic changes during the course of treatment and investigating the reasons for drug resistance. Furthermore, serial analysis of CTCs demonstrated that decreasing CTC numbers were associated with a radiographic tumor response; increasing CTC numbers were associated with tumor progression. These data suggested that the CTC number is promising as a predictor of tumor progression and treatment response. Similarly, the Kirsten rat sarcoma viral oncogene (KRAS) mutation has been successfully detected in CTCs from patients with metastatic colorectal cancer (mCRC), and the KRAS mutation status in CTCs may differ from that of the corresponding primary tumor over the course of targeted therapy,²⁴ which supports the use of CTCs in monitoring mutation status and predicting the response to targeted therapy in real time over the course of treatment.

The number of CTCs, which is influenced by chemotherapy, is a well-developed indicator of therapeutic response and provides valuable information regarding the effect of anticancer treatment. Changes in the number of CTCs at baseline and after treatment are significant in terms of clinical outcomes. In the clinical trial registration number (IMMC38) study,²⁵ serial monitoring of CTC count demonstrated that patients with castration-resistant prostate cancer (CRPC) had significantly better outcomes when an unfavorable baseline CTC count converted to a favorable CTC count after treatment and worse outcomes when favorable baseline counts converted to unfavorable counts after treatment. This finding was confirmed by other studies in CRPC.^26,27 The value of CTC count as an indicator of treatment response is also well established in metastatic and local breast cancer,^28,29 mCRC,³⁰ advanced lung cancer,^31,32 metastatic melanoma,³³ and other tumor types.

Prognosis Evaluation

The CTC count has been shown to be affected by systemic chemotherapy and to be an independent prognostic factor (pretreatment or posttreatment) for survival in patients with metastatic carcinoma and in several other tumor types, such as breast, prostate, and colorectal cancer.

In a prospective, multicenter study, the CTC count in 177 patients with measurable metastatic breast cancer (MBC) was determined before a new line of treatment and at the first follow-up visit. Compared to the group with <5 CTCs per 7.5 mL, patients with ≥5 CTCs per 7.5 mL had a shorter median PFS (2.7 months vs 7.0 months, P < .001) and a shorter OS (10.1 months vs >18 months, P < .001). This difference between the groups persisted at the first follow-up visit (PFS, 2.1 months vs 7.0 months; P < 0.001; OS, 8.2 months vs >18 months; P < 0.001). A multivariate Cox regression analysis that included treatment methods, Eastern Cooperative Oncology Group (ECOG) score, and estrogen receptor/progesterone receptor (ER/PR) status demonstrated that the number of CTCs was the strongest predictor of PFS and OS.³⁴ In a retrospective study, the individual CTC data for 1944 patients with MBC also confirmed this finding.³⁵

In a prospective multicenter study by Cohen et al, the CTC count at baseline or during treatment was confirmed to be an independent predictor of PFS and OS in patients with mCRC. In this study, 430 patients were divided into 2 groups based on CTC count (≥3 CTCs or <3 CTCs per 7.5 mL). Compared to the group with <3 CTCs per 7.5 mL at baseline, the other group had a shorter median PFS (4.5 vs 7.9 months; P = .0002) and OS (9.4 vs 18.5 months; P < .0001). Moreover, differences persisted at different follow-up times after therapy, which demonstrated that the number of CTCs can also predict PFS and OS during treatment.³⁶ The CEA is the most widely used tumor marker for monitoring therapy in patients with mCRC, although its prognostic value has not been confirmed.³⁷ Subsequently, a secondary study examined the relationship between CTCs and CEA in this patient cohort. In a multivariate analysis, CTCs at baseline, but not CEA, independently predicted survival, and only CEA at a threshold of ≥50 ng/mL or a ≤50% reduction provided independent prognostic information 6 to 12 weeks after initiating treatment.³⁸

In the IMMC38 trial,³⁹ mentioned earlier, the role of CTC count in prognostic evaluations was first assessed in CRPC. Patients with an unfavorable pretreatment and posttreatment CTC count (≥5 per 7.5 mL) had a shorter OS. The prognostic value of CTCs was also validated in a phase III trial (SWOG S0421) of docetaxel-based treatment of CRPC.⁴⁰ Notably, CTC count predicted OS better than PSA decrement algorithms at all time points.²⁵ A similar conclusion was reached in a reanalysis of the IMMC38 trial: Changes in prostate specific antigen (PSA) titer were weakly associated or not associated with risk (P < 0.04), and CTC count was the most predictive factor for OS.⁴¹ Based on these results, CTC count was approved by the FDA as a supplementary means for assessing the prognosis of patients with advanced prostate cancer. Hence, CTC count can also be used as an independent prognostic factor for patients with metastatic hormone-sensitive prostate cancer (mHSPC). In a study by Goodman et al, the CTC count was determined in 33 consecutive patients undergoing androgen deprivation therapy (ADT). A multivariate Cox regression showed that baseline CTC count had independent predictive value and that it might identify those patients with mHSPC who are unlikely to benefit from ADT.⁴²

Promising results have also demonstrated that CTC is an independent prognostic factor for relapse-free survival (RFS). In a phase II trial (remaGus 02), Pierga et al determined CTC count in 118 patients with large operable or locally advanced breast cancer before and after neoadjuvant chemotherapy prior to surgery. The results revealed that the presence of CTCs after a short follow-up of 18 months was an independent prognostic factor for early relapse.⁴³ These results were confirmed by Pachmann et al. The number of CTCs was quantified in 91 patients with nonmetastatic primary breast cancer before and during adjuvant chemotherapy. Compared to marginal changes, a 10-fold or greater increase in CTC count was significantly correlated with reduced RFS and decreased CTC count after adjuvant chemotherapy was associated with a favorable RFS.⁴⁴ These preliminary results supported CTCs as a strong predictor for RFS but require further verification.

However, CTCs are promising as a prognostic factor in patients with advanced breast, colorectal, and prostate cancer and other tumor types, such as lung cancer,^45,46 melanoma,⁴⁷ head and neck cancer,⁴⁸ and ovarian cancer.⁴⁹ Even in the early stage of certain cancers without clinical or imaging signs of overt metastases, CTCs have significant prognostic relevance, particularly in breast cancer⁵⁰ but also in other cancer types.⁵¹ These publications suggest that CTCs are an independent prognostic surrogate for some tumor types. Although the FDA has approved CellSearch for the prognostic evaluation of patients with metastatic breast, prostate, and colon cancer, the clinical prognostic value for other tumor types must be further validated. Other important issues remain to be conclusively addressed; for example, the optimized threshold for CTC count in different types of tumors (eg, 3 per 7.5 mL or 5 per 7.5 mL), and the quantitative relationship between CTC count and OS, PFS and RFS must be determined. Further investigations are needed to solve the problem.

CTC Count and Treatment Guidance

As stated earlier, CTC count can be used to predict survival before and after treatment, and the idea that the baseline or posttreatment CTC count may significantly contribute to guiding subsequent treatment is undoubtedly appealing.

The IMMC38 trial laid the foundation for CTCs as a marker for treatment guidance; the primary objective of this prospective study was to correlate changes in CTC count with OS in CRPC.³² The CTC count was determined before first-line chemotherapy and monthly thereafter. The results showed that the posttreatment CTC count had significant relevance for OS. In other words, unfavorable posttreatment CTC count, which predict worse OS, provided the clinician with the chance to evaluate the effectiveness of the neoadjuvant therapy and to consider whether an alternative chemotherapy regimen should be utilized in the next round of treatment. However, the ability of CTCs to facilitate a decision to adopt or reject the original chemotherapy regimen is unclear. A randomized phase III trial, SWOG S0500,⁵² of patients with MBC with a persistent increase in CTCs was conducted to determine whether changing to an alternative chemotherapy regiment (standard second-line treatment) after 3 weeks of first-line chemotherapy would improve OS compared to waiting for clinical or imaging evidence of progression. The results showed no difference in median OS between arm C1 (patients with persistently increased CTCs after first-line chemotherapy who continued the initial therapy) and C2 (patients with persistently increased CTCs after first-line chemotherapy who switched to an alternative chemotherapy). These data suggested that persistently increasing CTCs indicated worse OS but that early switching to an alternate cytotoxic therapy did not effectively prolong OS. However, there is the possibility that increasing CTC count is an indicator of worse prognosis but that standard chemotherapy may not change the course of the disease. As indicated by the results of the SWOG S0500 trial, there is a need for more effective treatment strategies than standard second-line chemotherapy.

Baseline CTC count has already demonstrated its prognostic value in MBC. Multivariate analyses showed that another independent prognosis factor is hormone receptor (HR) status, and HR status has been evaluated to identify individual therapeutic strategies that include chemotherapy and hormone therapy for patients with MBC.^34,53 The other criteria used to determine therapeutic strategies are not independent prognostic factors (eg, number of metastatic sites, visceral disease, and metastasis-free interval). As CTC count is highly correlated with PFS and OS, it has been proposed that CTC count may be a better criterion for guiding this important choice than the current empiric criteria. STIC clinical trial registration number (CTC) METABREAST,⁵⁴ a large pivotal phase III trial, was designed in 2012 and included more than 15 French centers to evaluate the use of CTCs to determine the choice of first-line therapeutic regimens of chemotherapy and hormone therapy in potentially hormone-sensitive MBC. The HR-positive patients with MBC with no previous treatment were randomized into the clinician choice group or the CTC count group. In the CTC arm, patients with a CTC count of ≥5 per 7.5 mL received chemotherapy, whereas patients with a CTC count of <5 per 7.5 mL received endocrine therapy as first-line treatment. The trial is expected to be completed in 2 to 3 years, and we anxiously await the results.

In conclusion, CTC count is mostly used to evaluate the effectiveness of antitumor treatment because of its close relationship with survival, but whether an alternative treatment regimen should be chosen based on CTC count must be validated in large phase III clinical trials that include different tumor types.

Heterogeneity Analysis of CTCs for Personalized Medicine

The utility of CTCs is not simply limited to evaluating their numbers; molecular characterization, such as of CTC genotypes, may be useful. Currently, personalized medicine depends on an analysis of the primary tumor biopsy and/or the status of a specific molecular target. However, a research team performed a careful genetic analysis of multiple spatially separated samples obtained from a primary tumor and associated metastatic sites from 4 patients with renal cell carcinoma. They used exome sequencing, chromosome aberration analysis, and DNA ploidy profiling to study these biopsies. The results revealed that 63% to –69% of the mutations present in a single biopsy were not shared in all biopsies from the same tumor, suggesting that no 2 samples from the same patient are genetically identical.⁵⁵ Similar results have recently been reported in melanoma, wherein 18 of 22 patients exhibited genetic divergence across different metastatic sites.⁵⁶ Tumor biopsy samples from primary tumors or metastases may present a serious challenge to personalized medicine and biomarker development because intratumoral heterogeneity creates a heterogeneous tumor genomic landscape. Heterogeneous protein function may foster tumor adaptation and may lead to drug resistance.^57,58

Thus, it is important to analyze tumor cells to obtain significant information. Considering the limitations of tumor biopsies, CTCs are expected to represent the full spectrum of the primary tumor and metastases and to play a role in guiding real-time drug selection. Single-cell mutational analysis of phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) has been investigated in CTCs and MBC to evaluate the possibility that CTCs represent the full spectrum of phenotypic and genetic heterogeneity of metastases.⁵⁹ In this study, 242 individual tumor cells were isolated from 17 patients and tested for mutations, and single-cell analyses showed that CTCs can reveal the genotypic heterogeneity that may change with disease progression. Therefore, the characterization of CTCs may provide a noninvasive method for monitoring the changing patterns of drug susceptibility in individual patients as their tumors acquire new mutations. For example, in a proof-of-concept study,⁶⁰ newly acquired genetic mutations in estrogen receptor 1, fibroblast growth factor receptor 2, and PIK3CA were discovered in CTC cultures from 6 patients with estrogen receptor-positive breast cancer, and potential new therapeutic targets were identified by determining the drug sensitivity of CTC lines with multiple mutations.

However, in reality, the situation is never quite so simple. Mutations were analyzed in 100 patients with breast cancer, and the results revealed that driver mutations were found in at least 40 cancer genes and 73 different combinations of mutated cancer genes, with the number of driver mutations ranging from 1 to 6 per patient.⁶¹ Such tumors characterized by high mutational rates demonstrate the endless heterogeneity. Even if we realize the full heterogeneity using CTCs, it remains to be seen whether we will be able to identify a targeted treatment plan for a patient with 6 driver mutations. The conventional kinase-based approaches are unlikely to alter the outcome of such highly heterogeneous tumors. Certain approaches, such as strengthening the immune system, may be a reasonable choice for these patients. For less heterogeneous tumors, the conventional truncal mutations model is potentially valuable, with the possibility that such attacks will be thwarted by clonal evolution. Therefore, intratumoral heterogeneity is a potential disaster for personalized treatment, and CTCs are promising candidates for guiding future targeted therapy, but there remains a long way to go.

Identifying CTCs for Early Diagnosis

Most patients with cancer have progressed to advanced stage disease, and thus have an extremely poor prognosis, when they see a doctor for the first time; these patients have missed the opportunity to undergo a radical resection. Early detection and diagnosis are crucial for patients with cancer. However, in early-stage NSCLC (T1a-T2bN0M0) when diagnosed using TNM staging, the recurrence rates are still high, even after curative surgery.⁶² Thus, certain limitations exist in traditional TNM staging: this system fails to evaluate tumor staging accurately. Veronique Hofman et al evaluated the presence of CTCs in patients with NSCLC undergoing surgery by ISET and discovered that the presence of CTCs in resectable patients with NSCLC was independent of TNM stage and was associated with worse prognosis.⁶³ Increasing evidences has strongly suggested that CTCs are a promising surrogate for early cancer diagnosis, even with a clinically detectable tumor. Chronic obstructive pulmonary disease (COPD) has demonstrated a close association with lung cancer and an independent risk factor for the development of lung cancer.⁶⁴ Ilie et al attempted to examine the presence of CTCs in conjunction with CT scans in patients with COPD as a first step in early lung cancer diagnosis.⁶⁵ A cohort of 168 patients with COPD was monitored annually by low-dose spiral CT and simultaneous enrichment of CTCs by ISET. CTC-positive patients with COPD (5 of 168), who were monitored annually by CT scan, developed lung nodules 1 to 4 years after CTC detection, leading to aggressive surgical resection and histopathological diagnosis of early-stage lung cancer. Similarly, the value of early diagnosis is apparent in pancreatic cancer,⁶⁶ gastric cancer,⁶⁷ bladder transitional cell cancer,⁶⁸ and breast cancer.⁶⁹

Conclusion and Perspectives

Detection and enrichment of CTC is the foundation of further studying tumor characteristics and improving clinical decision making with this minimally invasive approach. Series of platforms, such as CellSearch and CTC-chip, have been designed to isolate and enrich CTCs with relatively high sensitivity and purity. However, the CellSearch system and certain other platforms based on EpCAM might fail to identify tumor cells from nonepithelial cancers, such as melanomas or sarcomas, or cells undergoing EMT.⁷⁰ In the future, searching new biomarkers that encompass the complex heterogeneity of tumors or developing a new platform that integrates physical and biological characteristics of CTCs will provide a good solution to this problem.

The quantitative analysis of CTCs has been proven to deliver prognostic value at different stages, including pretreatment and posttreatment, and to be a reliable marker of clinical response to anticancer treatment. However, the role of CTC count in clinical scheme choice remains to be defined. The molecular analysis of CTCs to guide personalized treatment selection has been applied to clinical practice to overcome intratumoral and metastatic disease heterogeneity. Although driver mutations may number beyond our imagination,⁶¹ CTCs point clinicians in the right direction for personalized medicine.

Overall, the quantitative and qualitative analysis of CTCs represents a promising approach for individualized treatment. In the future, high-efficiency and low-cost CTC platforms will be widely used in the clinic.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Abbreviations

References

Tanaka

Yoneda

Kondo

. Circulating tumor cell as a diagnostic marker in primary lung cancer. Clin Cancer Res. 2009;15(22):6980–6986. doi:10.1158/1078-0432.

Bardia

Aceto

. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science. 2014;345(6193):216–220. doi:10.1126/science.1253533.

Janku

. Tumor heterogeneity in the clinic: is it a real problem. Ther Adv Med Oncol. 2014;6(2):43–51. doi:10.1177/1758834013517414.

Farace

Massard

Vimond

. A direct comparison of CellSearch and ISET for circulating tumour-cell detection in patients with metastatic carcinomas. Br J Cancer. 2011;105(6):847–853. doi:10.1038/bjc.2011.294.

Pantel

Denève

Nocca

. Circulating epithelial cells in patients with benign colon diseases. Clin Chem. 2012;58(5):936–940. doi:10.1373/clinchem.2011.175570.

Bardia

Wittner

. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013,339(6119):580–584. doi:10.1126/science.1228522.

Krebs

Sloane

Priest

. Evaluation and prognostic significance of circulating tumor cells in patients with non-small-cell lung cancer. J Clin Oncol. 2011;29(12):1556–1563. doi:10.1200/JCO.2010.28.7045.

Yokobori

Iinuma

Shimamura . Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial mesenchymal transition and is associated with colorectal. Cancer Res. 2013;73(7):2059–2069. doi:10.1158/0008-5472.

Lin

Zheng

Williams

. Portable filter-based microdevice for detection and characterization of circulating tumor cells. Clin Cancer Res. 2010;16(20):5011–5018. doi:10.1158/1078-0432.

10.

Hou

Warkiani

Khoo

. Isolation and retrieval of circulating tumor cells using centrifugal forces. Sci Rep. 2013;3:1259. doi:10.1038/srep01259.

11.

Sollier

Che

. Size-selective collection of circulating tumor cells using Vortex technology. Lab Chip. 2014;14(1):63–77. doi:10.1039/c3lc50689d.

12.

Hosokawa

Hayata

Fukuda

. Size-selective microcavity array for rapid and efficient detection of circulating tumor cells. Anal Chem. 2010,82(15):6629–6635. doi:10.1021/ac101222x.

13.

Farace

Massard

Vimond

. A direct comparison of CellSearch and ISET for circulating tumour-cell detection in patients with metastatic carcinomas. Br J Cancer. 2011;105(6):847–853. doi:10.1038/bjc.2011.294.

14.

Lee

Park

Lim

. All-in-one centrifugal microfluidic device for size-selective circulating tumor cell isolation with high purity. Anal Chem. 2014;86(22):11349–11356. doi:10.1021/ac5035049.

15.

Alshareef

Metrakos

Juarez Perez

. Separation of tumor cells with dielectrophoresis-based microfluidic chip. Biomicrofluidics. 2013,7(1):11803. doi:10.1063/1.4774312.

16.

Alix-Panabieres

Pantel

. Challenges in circulating tumour cell research. Nat Rev Cancer. 2014;14(9):623–631. doi:10.1038/nrc3820.

17.

Hyun

Jung

. Advances and critical concerns with the microfluidic enrichments of circulating tumor cells. Lab Chip. 2014;14(1):45–56. doi:10.1039/c3lc50582k.

18.

Pantel

Alix-Panabières

Riethdorf

. Cancer micrometastases. Nat Rev Clin Oncol. 2009;6(6):339–351. doi:10.1038/nrclinonc.2009.44.

19.

Nagrath

Sequist

Maheswaran

. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235–1239.

20.

Sequist

Nagrath

Toner

Haber

Lynch

. The CTC-chip: an exciting new tool to detect circulating tumor cells in lung cancer patients. J Thorac Oncol. 2009;4(3):281–283. doi:10.1097/JTO.0b013e3181989565.

21.

Dago

Stepansky

Carlsson

. Rapid phenotypic and genomic change in response to therapeutic pressure in prostate cancer inferred by high content analysis of single circulating tumor cells. PLOS ONE. 2014;9(8):e101777. doi:10.1371/journal.pone.0101777.

22.

Finlay

Anderton

Ashton

. Discovery of a potent and selective EGFR inhibitor (AZD9291) of both sensitizing and T790M resistance mutations that spares the wild type form of the receptor. J Med Chem. 2014;57(20):8249–8267. doi:10.1021/jm500973a.

23.

Maheswaran

Sequist

Nagrath

. Detection of mutations in EGFR in circulating lung-cancer cells. N Engl J Med. 2008;359(4):366–377. doi:10.1056/NEJMoa0800668.

24.

Kalikaki

Politaki

Souglakos

. KRAS genotypic changes of circulating tumor cells during treatment of patients with metastatic colorectal cancer. PLOS ONE. 2014;9(8):e104902. doi:10.1371/journal.pone.0104902.

25.

de Bono

Scher

Montgomery

. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin Cancer Res. 2008;14(19):6302–6309. doi:10.1158/1078-0432.CCR-08-0872.

26.

Coumans

Doggen

Attard

de Bono

Terstappen

. All circulating EpCAM+ CK + CD45- objects predict overall survival in castration-resistant prostate cancer. Ann Oncol. 2010;21(9):1851–1857. doi:10.1093/annonc/mdq030.

27.

Danila

Fleisher

Scher

. Circulating tumor cells as biomarkers in prostate cancer. Clin Cancer Res. 2011,17(12):3903–39012. doi:10.1158/1078-0432.CCR-10-2650.

28.

Punnoose

Atwal

Liu

. Evaluation of circulating tumor cells and circulating tumor DNA in non-small cell lung cancer: association with clinical endpoints in a phase II clinical trial of pertuzumab and erlotinib. Clin Cancer Res. 2012;18(8):2391–23401. doi:10.1158/1078-0432.

29.

Huang

Wick

Sittampalam

. A multicenter pilot study examining the role of circulating tumor cells as a blood-based tumor marker in patients with extensive small-cell lung cancer. Front Oncol. 2014;4:271. doi:10.3389/fonc.2014.00271.

30.

Barbazan

Muinelo-Romay

Vieito

. A multimarker panel for circulating tumor cells detection predicts patient outcome and therapy response in metastatic colorectal cancer. Int J Cancer. 2014;135(11):2633–2643. doi:10.1002/ijc.28910.

31.

Punnoose

Atwal

Liu

32.

Huang

Wick

Sittampalam

33.

Koyanagi

O’Day

Boasberg

. Serial monitoring of circulating tumor cells predicts outcome of induction biochemotherapy plus maintenance biotherapy for metastatic melanoma. Clin Cancer Res. 2010;16(8):2402–24028. doi:10.1158/1078-0432.

34.

Cristofanilli

Budd

Ellis

. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004;351(8):781–791.

35.

Bidard

Peeters

Fehm

. Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data. Lancet Oncol. 2014,15(4):406–414. doi:10.1016/S1470-2045(14)70069-5.

36.

Cohen

Punt

Iannotti

. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(19):3213–3221. doi:10.1200/JCO.2007.15.8923.

37.

Aldulaymi

Bystrom

Berglund

. High plasma TIMP-1 and serum CEA levels during combination chemotherapy for metastatic colorectal cancer are significantly associated with poor outcome. Oncology. 2010;79(1-2):144–149. doi:10.1159/000320686.

38.

Aggarwal

Meropol

Punt

. Relationship among circulating tumor cells, CEA and overall survival in patients with metastatic colorectal cancer. Ann Oncol. 2013;24(2):420–428. doi:10.1093/annonc/mds336.

39.

de Bono

Scher

Montgomery

. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin Cancer Res. 2008;14(19):6302–6309. doi:10.1158/1078-0432.

40.

Goldkorn

Ely

Quinn

. Circulating tumor cell counts are prognostic of overall survival in SWOG S0421: a phase III trial of docetaxel with or without atrasentan for metastatic castration-resistant prostate cancer. J Clin Oncol. 2014;32(11):1136–1142. doi:10.1200/JCO.2013.51.7417.

41.

Scher

Jia

de Bono

. Circulating tumour cells as prognostic markers in progressive, castration-resistant prostate cancer: a reanalysis of IMMC38 trial data. Lancet Oncol. 2009;10(3):233–239. doi:10.1016/S1470-2045(08)70340-1.

42.

Goodman

Jr Symanowski

Loudyi

Fink

Ward

Vogelzang

. Circulating tumor cells as a predictive biomarker in patients with hormone-sensitive prostate cancer. Clin Genitourin Cancer. 2011;9(1):31–38. doi:10.1016/j.clgc.2011.04.001.

43.

Pierga

Bidard

Mathiot

. Circulating tumor cell detection predicts early metastatic relapse after neoadjuvant chemotherapy in large operable and locally advanced breast cancer in a phase II randomized trial. Clin Cancer Res. 2008;14(21):7004–7010.

44.

Lucci

Hall

Lodhi

. Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncol. 2012;13(7):688–695. doi:10.1016/S1470-2045(12)70209-7.

45.

Krebs

Sloane

Priest

. Evaluation and prognostic significance of circulating tumor cells in patients with non-small-cell lung cancer. J Clin Oncol. 2011;29(12):1556–1563. doi:10.1200/JCO.2010.28.7045.

46.

Hiltermann

Pore

van den Berg

. Circulating tumor cells in small-cell lung cancer: a predictive and prognostic factor. Ann Oncol. 2012;23(11):2937–2942. doi:10.1093/annonc/mds138.

47.

Rao

Bui

Connelly

. Circulating melanoma cells and survival in metastatic melanoma. Int J Oncol. 2011;38(3):755–760.

48.

Nichols

Lowes

Szeto

. Detection of circulating tumor cells in advanced head and neck cancer using the CellSearch system. Head Neck. 2012;34(10):1440–1444. doi:10.1002/hed.21941.

49.

Pearl

Zhao

Yang

. Prognostic analysis of invasive circulating tumor cells (iCTCs) in epithelial ovarian cancer. Gynecol Oncol. 2014;134(3):581–590. doi:10.1016/j.ygyno.2014.06.013.

50.

Lucci

Hall

Lodhi

. Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncol. 2012;13(7):688–695. doi:10.1016/S1470-2045(12)70209-7.

51.

Pantel

Alix-Panabieres

. The potential of circulating tumor cells as a liquid biopsy to guide therapy in prostate cancer. Cancer Discov. 2012;2(11):974–975. doi:10.1158/2159-8290.

52.

Smerage

Barlow

Hortobagyi

. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J Clin Oncol. 2014;32(31):3483–3489. doi:10.1200/JCO.2014.56.2561.

53.

Kwast

Voogd

Menke-Pluijmers

. Prognostic factors for survival in metastatic breast cancer by hormone receptor status. Breast Cancer Res Treat. 2014;145(2):503–511. doi:10.1007/s10549-014-2964-0.

54.

Bidard

F-C

Baffert

Hajage

. Circulating tumor cells to guide the choice between chemotherapy and hormone therapy as first line treatment for hormone receptors positive metastatic breast cancer patients: the STIC CTC METABREAST trial. Cancer Res. 2012;72(24 suppl 3).

55.

Gerlinger

Rowan

Horswell

. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–892. doi:10.1056/NEJMoa1113205.

56.

Harbst

Lauss

Cirenajwis

. Molecular and genetic diversity in the metastatic process of melanoma. J Pathol. 2014;233(1):39–50. doi:10.1002/path.4318.

57.

Barone

Brusco

Fuqua

. Estrogen receptor mutations and changes in downstream gene expression and signaling. Clin Cancer Res. 2010;16(10):2702–2708. doi:10.1158/1078-0432..

58.

Fuqua

Rechoum

. Estrogen receptor (ER) alpha mutations in breast cancer: hidden in plain sight. Breast Cancer Res Treat. 2014;144(1):11–19. doi:10.1007/s10549-014-2847-4.

59.

Deng

Krishnakumar

Powell

. Single cell mutational analysis of PIK3CA in circulating tumor cells and metastases in breast cancer reveals heterogeneity, discordance, and mutation persistence in cultured disseminated tumor cells from bone marrow. BMC Cancer. 2014;14:456. doi:10.1186/1471-2407-14-456.

60.

Bardia

Aceto

. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science. 2014;345(6193):216–220. doi:10.1126/science.1253533.

61.

Stephens

Tarpey

Davies

. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486(7403):400–404. doi:10.1038/nature11017.

62.

Zhu

Feng

Zhang

. Time-varying pattern of postoperative recurrence risk of early-stage (T1a-T2bN0M0) non-small cell lung cancer (NSCLC): results of a single-center study of 994 Chinese patients. PLOS ONE. 2014;9(9):e106668. doi:10.1371/journal.pone.0106668.

63.

Hofman

Bonnetaud

Ilie

. Preoperative circulating tumor cell detection using the isolation by size of epithelial tumor cell method for patients with lung cancer is a new prognostic biomarker. Clin Cancer Res. 2011;17(4):827–835. doi:10.1158/1078-0432.

64.

Houghton

. Mechanistic links between COPD and lung cancer. Nat Rev Cancer. 2013;13(4):233–245. doi:10.1038/nrc3477.

65.

Ilie

Hofman

Long-Mira

. “Sentinel” circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease. PLOS ONE. 2014;9(10):e111597. doi:10.1371/journal.pone.0111597.

66.

Yuan

. Advances in pancreatic cancer research: moving towards early detection. World J Gastroenterol. 2014;20(32):11241–11248. doi:10.3748/wjg.v20.i32.11241.

67.

Bayat

Mokhtari

Rezaei-Tavirani

. Absolute quantification of free tumor cells in the peripheral blood of gastric cancer patients. Genet Mol Res. 2014;13(2):4425–4432. doi:10.4238/2014.June.16.1.

68.

Liu

Zhao

. Quantitation of rare circulating tumor cells by folate receptor alpha ligand-targeted PCR in bladder transitional cell carcinoma and its potential diagnostic significance. Tumour Biol. 2014;35(7):7217–7223. doi:10.1007/s13277-014-1894-0.

69.

Barriere

Riouallon

Renaudie

Tartary

Rigaud

. Mesenchymal and stemness circulating tumor cells in early breast cancer diagnosis. BMC Cancer. 2012;12:114. doi:10.1186/1471-2407-12-114.

70.

Coumans

Ligthart

Uhr

Terstappen

. Challenges in the enumeration and phenotyping of CTC. Clin Cancer Res. 2012;18(20):5711–5718. doi:10.1021/ac401720k.

Progress of Circulating Tumor Cells in Cancer Management

Abstract

Keywords

Introduction

Detection and Enrichment Techniques

Monitoring Therapeutic Response

Prognosis Evaluation

CTC Count and Treatment Guidance

Heterogeneity Analysis of CTCs for Personalized Medicine

Identifying CTCs for Early Diagnosis

Conclusion and Perspectives

Footnotes

Declaration of Conflicting Interests

Funding

Abbreviations

References