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Review

Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility

by
A. M. Sofie Berghuis
1,*,
Hendrik Koffijberg
1,
Jai Prakash
2,
Leon W. M. M. Terstappen
3 and
Maarten J. IJzerman
1
1
Health Technology and Services Research, University of Twente, Enschede 7500 AE, The Netherlands
2
Biomaterials Science and Technology, University of Twente, Enschede 7500 AE, The Netherlands
3
Medical Cell Biophysics, University of Twente, Enschede 7500 AE, The Netherlands
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2017, 18(2), 363; https://doi.org/10.3390/ijms18020363
Received: 20 December 2016 / Revised: 27 January 2017 / Accepted: 3 February 2017 / Published: 9 February 2017
(This article belongs to the Special Issue Precision Medicine—From Bench to Bedside)
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Versions Notes

Abstract

:
Reviews on circulating biomarkers in breast cancer usually focus on one single biomarker or a selective group of biomarkers. An overview summarizing the discovery and evaluation of all blood-based biomarkers in metastatic breast cancer is lacking. This systematic review aims to identify the available evidence of known blood-based biomarkers in metastatic breast cancer, regarding their clinical utility and state-of-the-art position in the validation process. The initial search yielded 1078 original studies, of which 420 were assessed for eligibility. A total of 320 studies were included in the final synthesis. A Development, Evaluation and Application Chart (DEAC) of all biomarkers was developed. Most studies focus on identifying new biomarkers and search for relations between these biomarkers and traditional molecular characteristics. Biomarkers are usually investigated in only one study (68.8%). Only 9.8% of all biomarkers was investigated in more than five studies. Circulating tumor cells, gene expression within tumor cells and the concentration of secreted proteins are the most frequently investigated biomarkers in liquid biopsies. However, there is a lack of studies focusing on identifying the clinical utility of these biomarkers, by which the additional value still seems to be limited according to the investigated evidence.

1. Introduction

1.1. Breast Cancer Survival

Globally, breast cancer is the most commonly diagnosed form of cancer among women. Clinical management has improved over the last years, and the development of genetic tests such as Mammaprint and OncoTypeDX have proven to guide treatment in early stage breast cancer. Although the current 5-year survival for primary breast cancer is relatively high (ranging from 80% to 92% in different populations) [1], survival rates decrease to less than 25% when the disease becomes metastatic [1,2]. The most important factor to increase survival for those suffering from metastatic breast cancer, is to prescribe a treatment that has the most likelihood of being effective, guided by the tumor cell characteristics [3,4]. To select the most effective treatment once the metastatic lesions have been detected, it is essential to obtain accurate information on the characteristics of the tumor cells at the time therapy is to be initiated [5].

1.2. Detection and Treatment of Metastatic Lesions

Technical advances in the molecular characterization of cells has already lead to accurate predictions of survival and treatment efficacy. However, these molecular characterizations require high-quality biopsies, which cannot always be obtained from the primary tumor [6]. Alternatively, taking a biopsy of the metastatic lesion is either difficult or even impossible, for example, due to its location, or the inability to visualize that location with the currently used imaging techniques [6,7,8]. Furthermore, previous research has shown that molecular aberrations of the primary tumor may differ from that of the metastatic lesion and different metastatic lesions can have different characteristics [9]. Therefore, there remains a need for new tests which are sufficiently sensitive and reflect the composition of the tumor at all sites to guide treatment of metastatic disease.

1.3. The Use of Blood-Based Biomarkers

A possible way of enabling better treatment response monitoring or treatment guidance is the use of blood-based biomarkers or liquid biopsies [10]. A large number of single blood-based biomarkers can be distinguished in the blood, of which the most commonly known soluble proteins are Human Epidermal Growth Factor Receptor 2 (HER2), Cancer Antigen 15-3 (CA 15-3), Carcinoembryonic Antigen (CEA) and MUC1 [11]. Furthermore, all kinds of gene expression patterns or mutations can be extracted from circulating mRNA or circulating free DNA [8,12]. However, not only proteins or gene expression patterns yield prognostic or predictive information, even complete cells found in the blood—such as Circulating Tumor Cells (CTCs) or Cancer Associated Fibroblasts (CAF)—provide this type of information.
Although a range of different biomarkers is known, it is far more difficult to evaluate their usefulness for treatment targeting or prognosis of disease. It therefore is required to develop a classification, both to determine biomarkers with clinical utility and to prioritize future research. For clinical decision making, there are different ways of classifying diagnostic information [10]. Classifications focus, for example, on prognostic or predictive ability, or on a classification according to specific hallmarks of cancer [12].

1.4. Evidence on the Utility of Biomarkers

Up to now, the literature is not clear about the clinical utility of biomarkers in breast cancer. Several systematic reviews on blood-based biomarkers have been published yet [10,13]. However, these studies usually focus on one single biomarker or a selective group of biomarkers. These reviews are helpful to understand specific molecular pathways of oncogenesis, on specific prognostic information and all other outcomes they are related to, or on both.
An overview summarizing the discovery and evaluation of blood-based biomarkers for metastatic disease, in terms of their current status and future potential for clinical application, is still lacking. Therefore, this systematic review focusses on identifying known biomarkers, the available evidence regarding their clinical utility and exploring the current state-of-the-art in the validation process of all blood-based biomarkers in metastatic breast cancer. The review aims to identify a set of blood-based biomarkers that may have substantial future potential. Whereas it is common to focus on outcomes in terms of effectiveness, this review instead focusses on the developmental stage as the primary outcome measure of the included studies. First, all blood-based biomarkers will be identified and classified according to their developmental stage (e.g., from discovery to clinical utility). Second, the set of biomarkers with the highest future potential for clinical application will be identified by the number of studies that have been performed in each of the developmental stages.

1.5. Conclusions

The main aim of research on blood-based biomarkers in metastatic breast cancer is the identification of new biomarkers or relations of these biomarkers with other original molecular tumor characteristics. Especially gene expression within CTCs is investigated frequently. However, there still is a lack of studies identifying the clinical utility of these biomarkers. Thereby, the additional value for these biomarkers seems to be still limited according to the investigated evidence.

2. Results

2.1. Search Results

The initial search resulted in a total of 1249 studies from all databases searched. After screening all abstracts, 410 studies were further assessed for eligibility. During the assessment for eligibility, 91 studies were excluded. A total of 320 studies were included in this review. The full list of all studies that were included is presented in Appendix C. Most studies were excluded because the biomarkers investigated were not extracted from metastatic breast cancer patients (n = 22; 24.4%), because the blood used in the detection of the biomarker was non-human or was injected with a cell line that had just metastatic potential (n = 19; 21.1%) or because the study investigated multiple stages of breast cancer but had not reported conclusions for metastatic breast cancer separately (n = 17; 18.9%). The flow diagram of the search is presented in Figure 1.

2.2. Study Characteristics

For each study, the data were extracted and two classifications were made. First, the biomarkers were classified in one of the four general categories. Second, studies were classified in one of the pre-defined developmental stage categories, as defined in Figure 2. To illustrate the classification more clearly, citations of those studies which were classified as being in one of these phases are given in the right column of Figure 2.

2.3. Results According to Developmental Phase

Figure 3 presents the DEAC with the distribution of studies over developmental phases. From this figure it is apparent that most studies focused on the identification phase. This means that most studies focus on finding relationships between the concentration of the biomarker, in relation to a new or existing threshold and furthermore, try to evaluate this against an outcome measure in terms of survival (e.g., Overall Survival (OS), Progression Free Survival (PFS) or survival in months). This phase is split up over two sub phases, namely basic predictive and basic prognostic research. For predictive research, only the concentrations in a subgroup of metastatic breast cancer patients were reported. For prognostic research, these concentrations were linked to an outcome measure related to survival (OS or PFS).

2.4. Results per Biomarker

The general biomarker category in which most studies were performed on blood-based biomarkers in metastatic breast cancer, concerned whole cells in the blood (n = 181; 56.6%). CTCs made up a large part of this. In 85.1% (n = 154) of all included studies CTC enumeration was performed. In 42.5% of all included studies (n = 136), also genetic profiling for these cells had been done. The markers most frequently investigated are presented in Table 1.
Only those biomarkers for which 5 or more studies have been performed are included in the table. This cut-off had been chosen because these markers represent the most frequently investigated biomarkers. The frequency by which biomarkers are investigated is presented in Table 2, which presents that only 9.8% of all biomarkers is investigated in more than 5 studies. A detailed overview presenting the amount of studies performed for each single biomarker, including an overview of the amount of studies in each developmental stage is presented in Appendix D.
The percentages shown in Table 1 present the percentage of total studies that investigated that single marker. The second general biomarker category on which a relatively large amount of studies have been performed (n = 107; 33.4%) are proteins. Within this category most research has been focusing on 4 proteins, which are: CA15-3 (n = 22; 20.5%), soluble HER2 (n = 19; 17.8%), Vascular Endothelial Growth Factor (VEGF) (n = 18; 16.8%) and Vascular Endothelial Growth Factor Receptor (VEGFR) (n = 14; 13.1%). As discussed before, frequently studies are focusing on investigating multiple biomarkers instead of single biomarkers. As presented in Table 1, a total of 51 studies have investigated CA15-3. This means that also research which mainly focuses on one of the other biomarker categories investigates CA15-3. The same differences in the amount of studies performed were seen for HER2 and VEGF.

2.5. Results on the Number of Studies Performed

Summarized over all general biomarker categories, the total amount of studies included in the results synthesis is 320 as presented in Figure 2. In these studies a total of 275 single biomarkers have been investigated. The average number of studies performed on one single biomarker is 2.6 (range 1–154 studies). In Table 2 results are presented for frequency by which the study investigated a number of biomarkers. Table 2 shows that for 68.8% of all the biomarkers only one study has investigated that particular biomarker. For 13.8% of all biomarkers two studies have investigated that biomarker.

3. Discussion

In this paper we present a broad overview of research on blood-based biomarkers in metastatic breast cancer, performed since 2006. Of the included studies, most focused on detecting whole cells in the blood, with a focus on the enumeration or genetic characterization of circulating tumor cells. Considering the classification into developmental stages, the identification stage is the stage during which most research has been performed. Most studies focus on the identification phase, in which they investigate the ability to detect particular biomarkers in the blood and are trying to find connections between these concentrations and potential outcome measures in terms of survival. For proteins CA15-3, soluble HER2, VEGF and VEGFR have been investigated most frequently. However, for CTCs there have been clinical trials, but not for one of these proteins since 2006.
In terms of developmental stages, we expected that the amount of research performed would follow some kind of trend over time. It was expected that per biomarker there would be a substantial amount of studies focusing on the early developmental stages (technical validation), with decreasing numbers of studies the further the research for that particular biomarker proceeded in the developmental process. However, the DEAC shows that this trend does not exist for blood-based biomarkers in metastatic breast cancer. The DEAC shows that the number of studies performed increase until they reach the identification phase, and decrease afterwards. Therefore, it seems that the technical validation and clinical validation phase are currently less performed than research in the identification phase. Another observation from the DEAC is the low amount of research performed in the prognostic validation phase, suggesting this phase is not receiving sufficient attention. However, this may well be due to the fact that the initial search was limited to articles published since 2006, so that a limited amount of studies concerning some of the developmental phases were found. It might have been that specific phases which seemed to have had insufficient attention for several biomarkers were investigated before 2006. In addition, some information might have been missed, as publication bias may have occurred due to excluding non-English studies.
Furthermore, biomarker research may have been performed in a commercial setting or for stakeholders intent to guide internal research and development decisions. As such, selective reporting may occur by which not all findings might have been published. The same holds for studies with negative findings on (some subset) of investigated biomarkers, as it is known that such results are harder to publish than positive findings. As we did not investigate a single outcome measure, no standard methods are available to assess the ensuing risk of bias in our results. Even though the intention of reports might be to inform about recent developments, other stakeholders might use this information differently. Therefore, it seems valuable for future research to be able to have access to all information that was, or can possibly be extracted from the blood samples. Future research should pay attention to selective reporting before publishing, or ensure that samples are publicly available via biobanks.

4. Materials and Methods

This systematic review of blood-based biomarkers in metastatic breast cancer was performed according to the PRISMA guidelines [22]. A review protocol was used and is presented in Appendix A. This review was not registered in the PROSPERO database. All types of studies were included in the initial review, as the aim of this review is to identify the best available evidence exploring the position in the development process of all blood-based biomarkers in metastatic breast cancer. Since all types of primary research studies were included, it was not required that the intervention, control or specific outcome measure was reported in the initial search. Therefore, no specific study characteristics or PICO-statement for inclusion criteria was used. The only restriction applied to the search concerned a time constraint, as studies published since 1 January 2006 were included. Databases that were searched are PubMed, Scopus and OVID. Additionally, articles found by cross-referencing or hand search were included in the initial search. The initial search was performed in June 2016 and updated on 1 December 2016. The detailed search terms applied are presented in Appendix B.
After the initial search and removal of duplicate papers, abstracts were scanned for relevance. Abstracts of articles that either did not present non-primary research data or concerned topics not of interest here (such as, other cancer types, only other stages of breast cancer, and non-blood-based biomarkers—e.g., biomarkers that can be found in other body fluids) were excluded from the full-text review. All abstracts were processed by one reviewer (A. M. Sofie Berghuis) and were discussedwith a second reviewer (Hendrik Koffijberg) if necessary.
Full texts of all included papers were assessed for eligibility by one reviewer (A. M. Sofie Berghuis). All studies were then categorized according to the 10 pre-defined developmental stage and per general biomarker type. Four general developmental stages were identified, namely technical validation, identification, clinical validation and clinical utility. A full description of all pre-defined developmental stages is presented in Figure 1. Data was then classified in four general types of biomarkers, namely cells, proteins, circulating DNA and circulating RNA. Final classification of studies was discussed with a second reviewer (Hendrik Koffijberg) if classification in either one of the categories was unclear to the first reviewer (A. M. Sofie Berghuis). For studies on which there was no consensus between these two reviewers, a third reviewer reclassified the study (Maarten J. IJzerman).

4.1. Article Processing

Quantitative and qualitative data was manually extracted from the included studies and structured in Excel (version 2013) in pre-defined and labeled columns. The following information was extracted from all the included studies:
-
General biomarker classification (classification in one of the four categories: cells, proteins, circulating DNA or circulating RNA)
-
Developmental stage (classification according to the stages and general descriptions of these stages shown in Figure 1)
-
Specific biomarker name
-
Type of test used to quantify or detect biomarker (e.g., ELISA, CellSearch, etc.)
-
Whether—and if so, which—survival data was presented (Overall Survival, Progression Free Survival, survival in months)
Given the focus on the translation of biomarkers to clinical practice, the results of all included studies were summarized according to the number of studies performed per developmental stage for each general biomarker category. Results for all single biomarkers were summarized per general biomarker category as studies might investigate more than one biomarker. For all single biomarkers it was determined how many studies investigated that biomarker and in which stage of translation the biomarker was identified. For each general biomarker category it was investigated how many studies presented results on the full range of single biomarkers found.

4.2. Synthesis of Results

Results were presented in a Development, Evaluation and Application Chart (DEAC) that was developed specifically for this review. This figure gives a broad overview of the development of biomarkers in each of the predefined stages of clinical translation. Specifically, the figure shows four bar diagrams above each other, one diagram for each of the general biomarker categories. Each vertical bar, per diagram, represents a developmental stage. The bars are displayed to represent the different stages in the translation, starting with the most basic (developmental) research on the left side and more advanced (evaluation) research (such as clinical trials or health economic evaluations) presented on the right side. The height of the bars reflects the number of included studies. This figure therefore gives an overview of the number of studies published on each of the general biomarker categories according to the developmental stage timeline.

5. Conclusions

Since 2006, a substantial amount of research has been done to investigate the potential role of blood-based biomarkers in metastatic breast cancer. There seems to be a focus on research toward the use of CTCs, as most studies investigate these, whether in combination with other markers or as a single marker. The current emphasis of investigating these biomarkers seems to be on developing new techniques or finding new biomarkers that might have predictive or prognostic value, as most studies focus on the identification phase. There is a lack of studies focusing on clinical utility of these biomarkers. This might be because these studies have not yet been performed or suffer from publication bias. However, the lack of studies investigating the utility of blood-based biomarkers causes the additional value in terms of clinical utility, health outcomes or health care efficiency to still be limited according to the investigated evidence.

Author Contributions

The initial search was performed by A. M. Sofie Berghuis. Classification into developmental stages of the articles was discussed with Hendrik Koffijberg and Maarten J. IJzerman. Classification of the general biomarker categories was discussed with Jai Prakash and Leon W. M. M. Terstappen. All authors were involved in writing and revising the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

CA15-3Cancer Antigen 15-3
CTCCirculating Tumor Cell
DEACDevelopment, Evaluation and Application Chart
PFSProgression Free Survival
HER2Human Epidermal Growth Factor Receptor 2
OSOverall Survival
VEGFVascular Endothelial Growth Factor
VEGFRVascular Endothelial Growth Factor Receptor

Appendix A. Review Protocol

The steps described in Figure A1 were used as a review protocol.
In the abstract, screening the abstracts were only excluded when they explicitly presented that they contained information on one of the five exclusion criteria. If it was doubted whether results for blood-based biomarkers could have been presented in the text due to vague descriptions, the article was included for full text review. An example of this is the use of “Advanced breast cancer” as study population. For advanced often stage IIIB/IIIC and stage IV is meant. However, several abstracts did not present which stages they exactly investigated. These abstracts therefore were included.
Non-primary research that was excluded from further synthesis was saved in a separate folder, so these can be easily accessed after the complete search. Thereby, comparisons between the results found and the results that were already presented could easily be made.
Ijms 18 00363 g004 550
Figure A1. Review Protocol.
Figure A1. Review Protocol.
Ijms 18 00363 g004

Appendix B. Search Terms

Below the full electronic search strategy is presented, i updated until 1 December 2016.

Appendix B.1. Final Search Term PubMed (n = 994)

(((Metastatic[tiab] OR Advanced [tiab] OR Late stage[tiab] OR Malign*[tiab] OR Stage IV[tiab] OR Secondary[tiab]) AND (Breast cancer[majr] OR Breast neoplasm[majr] OR Breast carcinoma[majr] OR Mamma carcinoma[majr] OR Breast tumor[majr] OR Breast tumour[majr] OR “breast neoplasms”[Mesh])) AND ((Blood based[tw] OR Circulating[tw] OR Plasma[tw] OR Liquid[tw] OR Serum[tw] OR Serum based[tw] OR Extracellular[tw]) AND (blood[tw] AND (Marker*[tw] OR Biomarker*[tw] OR Biops*[tw] OR Tumor Cell*[tw] OR Tumor micro particle*[tw] OR Tumor particle*[tw] OR Tumor vesicle*[tw] OR Tumour Cell*[tw] OR Tumour micro particle*[tw] OR Tumour particle*[tw] OR Tumour vesicle*[tw] OR Tumor marker*[tw] OR Tumour marker*[tw] OR Tumor cell cluster*[tw] OR Tumour cell cluster*))))

Appendix B.2. Final Search Term Scopus (n = 6)

(((ALL(blood based OR circulating OR plasma OR liquid OR serum OR serum based OR extracellular)) AND (ALL (marker OR biomarker OR biops OR tumor cell OR tumor micro particle OR tumor particle OR tumor vesicle OR tumour cell OR tumour micro particle OR tumour particle OR tumour vesicle OR tumor marker OR tumour marker OR tumor cell cluster OR tumour cell cluster))) AND (ALL (blood))) AND ((ABS (metastatic OR advanced OR late stage OR malign OR stage iv OR secondary)) AND (ALL (breast cancer OR breast neoplasm OR breast carcinoma OR mamma carcinoma OR breast tumor OR breast tumour)))

Appendix B.3. Final Search Terms OVID (n = 90)

(((ALL (blood based OR circulating OR plasma OR liquid OR serum OR serum based OR extracellular)) AND (ALL (marker OR biomarker OR biops OR tumor cell OR tumor micro particle OR tumor particle OR tumor vesicle OR tumour cell OR tumour micro particle OR tumour particle OR tumour vesicle OR tumor marker OR tumour marker OR tumor cell cluster OR tumour cell cluster))) AND (ALL (blood))) AND ((ABS (metastatic OR advanced OR late stage OR malign OR stage iv OR secondary)) AND (ALL (breast cancer OR breast neoplasm OR breast carcinoma OR mamma carcinoma OR breast tumor OR breast tumour)))

Appendix B.4. Final Search Terms Medline (n = 159)

(((TI = (metastatic OR advanced OR late stage OR malign OR stage iv OR secondary)) AND (TI = (breast cancer OR breast neoplasm OR breast carcinoma OR mamma carcinoma OR breast tumor OR breast tumour))) AND ((TI = (blood based OR circulating OR plasma OR liquid OR serum OR serum based OR extracellular)) AND (TI = (marker OR biomarker OR biops OR tumor cell OR tumor micro particle OR tumor particle OR tumor vesicle OR tumour cell OR tumour micro particle OR tumour particle OR tumour vesicle OR tumor marker OR tumour marker OR tumor cell cluster OR tumour cell cluster)))) AND LANGUAGE: (English) AND DOCUMENT TYPES: (Article)
Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI Timespan = 2006–2016

Appendix C. Overview of All Included Studies

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  • Perroud HA, Alasino CM, Rico MJ, Mainetti LE, Queralt F, Pezzotto SM, et al. Metastatic breast cancer patients treated with low-dose metronomic chemotherapy with cyclophosphamide and celecoxib: clinical outcomes and biomarkers of response. Cancer chemotherapy and pharmacology. 2016;77(2):365-74.
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Appendix D. Detailed Overview of All Biomarkers per Developmental Stage

For all biomarkers an overview was created of the number of studies that investigated the biomarker and the translational stages in which these studies have been performed. Some studies focused on biomarkers from multiple categories (e.g., they focused on cells and proteins at the same time). All biomarkers investigated in these studies were categorized according to their main research subject. If the article for example mainly studied CTCs but also measured the concentration of CA15-3, then this study was classified in the cells category. In the following sections an overview of all biomarkers that were classified in the four general biomarker categories is presented.

Appendix D.1. Cells

Several types of research on cells as blood-based biomarkers have been performed. Studies enumerated cells, investigated the expression of proteins on their membrane or investigated the expression of genes that extracted from DNA or mRNA of the cell. The first type of studies are those investigating the enumeration of whole cells or cell clusters. All cells or cell clusters that have been investigated are presented in Table A1.
The second type of studies that investigated cells are those studies that investigate the proteins that are expressed on the membranes of the cells. Table A2 presents the proteins that have been investigated on the membranes of cells. The fourth column “Gene” presents the abbreviations of the genes that encode for these proteins. The fifth column “Gene ID” presents the gene ID of the gene that encodes for the membrane protein.
The third type of studies included in the cells category presented research on the gene expression within these cells. Table A3 presents the genes have been investigated, after DNA or mRNA was extracted from cells.
Studies that mainly focus on cells have also investigated a couple of proteins parallel to their research on the enumeration of cells, membrane expression of proteins on those cells or gene expression within those cells. Table A4 presents the proteins that have been investigated in studies that mainly focused on cells.
Besides the above presented blood-based biomarkers, in one study microRNA 10b had been investigated in parallel with research on CTCs (basic predictive research). In five other studies DNA from cells was extracted and whole genome amplification was performed (all 5 studies were categorized as basic predictive research).

Appendix D.2. Proteins

Several types of proteins have been investigated. Table A5 presents an overview of all proteins that have been investigated in studies which mainly focus on proteins as blood-based biomarkers.
In these studies the main research aim is on proteins as blood-based biomarkers. However, also a couple of other biomarkers have been investigated. In two studies they simultaneously investigated the enumeration of CTCs. In eight studies they looked at gene expression patterns within DNA or mRNA. The genes investigated in these studies were OPG, RANKL, eNOS, and THBS-1.

Appendix D.3. DNA

Table A6 presents al genes that have been investigated in studies that mainly focus on circulating DNA.
Besides DNA investigated, several articles investigated proteins or cells in parallel. Two studies enumerated CTCs in parallel (basic prognostic research). In 13 studies proteins were investigated in parallel. Seven studies focused on CA15-3, five on CEA and 1 on the plasminogen activator.

Appendix D.4. RNA

Table A7 presents al circulating micro RNAs that have been studied.
Studies that mainly focused on microRNAs investigated the enumeration of CTCs in parallel in two studies, and the concentration of secreted Mammaglobin in one study.
Table
Table A1. Translational stages of research on the enumeration of whole cells or cell clusters.
Table A1. Translational stages of research on the enumeration of whole cells or cell clusters.
Biomarker AbbreviationBiomarkerTotal Number of ArticlesBasic ResearchProof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservationalTrialsHealth Economic Analyses
CAMLsCancer associated macrophage like cells1 1
CAFsCancer associated fibroblasts1 1
Monocyte CD63Type of white blood cell CD631 1
Monocyte CD64Type of white blood cell CD641 1
CECsCirculating epithelial cells2 2
CEPsCirculating epithelial progenitor cells2 2
CETCCirculating epithelial tumor cells2 1 1
aCTCsApoptotic circulating tumor cells1 1
CSCsCancer Stem Cells2 1 1
CTCCirculating tumor cells1564192046351292
CTC ClusterCirculating tumor cell clusters3 1 1 1
NKsNatural Killer Cells2 2
Table
Table A2. Translational stages of research on the membrane expression of proteins.
Table A2. Translational stages of research on the membrane expression of proteins.
Biomarker AbbreviationBiomarkerCell *GeneGene IDTotal Number of ArticlesBasic Research Proof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservationalTrialsHealth Economic Analyses
Akt2AKT serine theronine kinase 2CTCAKT22084 111 1
CD133Prominin 1CTCPROM188421 1
CD44CD44 molecule CTCCD449601 1
EREstrogen CTCESR120991312163
FibronectinFibronectinCTCFN123351 1
HER 2Human epidermal growth factor receptor 2CTCERBB22064251138408
N-CadherinCadherin 2CTCCDH210003 12
PRProgesteronCTCPGR524171 42
VEGFVascular endothelial growth factorCTCVEGFA74224 2 2
VEGFR2Vascular endothelial growth factor receptor 2CTCKDR37911 1
VimentinVimentinCTCVIM74316 141
CD24CD24 moleculeGranulocytesCD241001339411 1
TLR2Toll Like Receptor 2LymphocytesTLR270971 1
TLR4Toll Like Receptor 4LymphocytesTLR470991 1
* Cells given in this column represent the cells in which these biomarkers were found.
Table
Table A3. Translational stages of research on gene expression within cell DNA or mRNA.
Table A3. Translational stages of research on gene expression within cell DNA or mRNA.
Biomarker AbbreviationBiomarkerCell *GeneGene IDTotal Number of ArticlesBasic ResearchProof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservationalTrialsHealth Economic Analyses
CD34CD34 MoleculeCSCsCD349471 1
NanogNANOGCSCsNANOG799231 1
NestinNestinCSCsNES107631 1
Oct3/4POU class 5 homeobox 1CSCsPOU5F154601 1
Sox2SRY Box 2CSCsSOX266571 1
ACTA1Actin/α 1, skeletal muscleCTCACTA1581 1
AGR2Anterior gradient 2 protein disulphide isomerase family memberCTCAGR2105511 1
ALDH1Aldehyde dehydrogenase 1 family member A1CTCALDH1A12165 11 2 1
AURKAAurora Kinase ACTCAURKA67901 1
BCL2BCL2 apoptosis regulatorCTCBCL25961 1
BIRC5baculoviral IAP repeat containing 5CTCBIRC53321 1
CCND1Cyclin D1CTCCCND15951 1
CDC6Cell Division Cycle 6CTCCDC69901 1
CENPFCentromere protein FCTCCENPF10631 1
CEP55Centrosomal protein 55CTCCEP55551652 1 1
CK19Keratin type I cytoskeletal 19CTCKRT1938806 41 1
CK8Cytokeratin 8CTCKRT838561 1
CRABP2Cellular retinoic acid binding protein 2CTCCRABP213821 1
CSt6 promotorCystatin E/MCTCCST614741 1
CXCL14CXC motif chemokine ligand 14CTCCXCL1495472 2
CXXC5CXXC finger protein 5CTCCXXC5515231 1
DTX3Deltex E3 ubiquitin ligase 3CTCDTX31964031 1
DUSP4Dual specificity phosphatase 4CTCDUSP418461 1
EEF1A2Eukaryotic translation elongation factor 1 α 2CTCEEF1A219172 2
EGFREpidermal growth factor receptorCTCEGFR19566 1 21 2
ERBB3ERB-b2 receptor tyrosine kinase 3 CTCERBB320652 11
ERBB4ERB-b2 receptor tyrosine kinase 4CTCERBB420661 1
ERCC1ERCC excision repair 1, endonuclease non-catalytic subunitCTCERCC120671 1
ESR1Estrogen Receptor 1CTCESR120993 1 2
FGFR4Fibroblast gorwth factor receptor 4CTCFGFR422642 1 1
FKBP10FK506 binding protein 10CTCFKBP10606811 1
FOX A1Forkhead box A1CTCFOX A131691 1
FOXC1Forkhead box C1CTCFOXC122961 1
GAPDHGlyceraldehyde-3-phosphate dehydrogenaseCTCGAPDH25071 1
HER2Human epidermal growth factor receptor 2CTCERBB220641713291 1
HIF-1ahypoxia inducible factor-1 αCTCHIF1A30911 1
IGFBP2Insulin like growth factor binding protein 2CTCIGFBP234851 1
IGFBP4Insulin like growth factor binding protein 4CTCIGFBP434871 1
IL17 BRInterleukin 17 receptor BCTCIL17RB555401 1
ITGA6Integrin subunit αCTCITGA636551 1
Ki67(proliferation marker)CTCMKI6742881 1
KRT14Keratin 14CTCKRT1438611 1
KRT17Keratin 17CTCKRT1738721 1
KRT19Keratin 19CTCKRT19 38802 2
KRT20Keratin type I cytoskeletal 20CTCKRT20544741 1
KRT7Keratin 7CTCKRT738551 1
KRT81Keratin 81CTCKRT8138871 1
LAD1Ladinin 1CTCLAD138981 1
Mamma-globinSecretoglobin family 2a member 2CTCSCGB2A242503 1 2
MELKMaternal embryonic leucine zipper kinaseCTCMELK98331 1
MUC1CA15-3CTCMUC1 4582511 2 1
MYBL2MYB proto-oncogene like 2CTCMYBL246051 1
NDC80NDC80 Kinetochore complex componentCTCNDC80104031 1
NUF2NUF2, NDC80 kinetochore complex componentCTCNUF2835401 1
PIK3CAPhosphalidylinositol-4,5-bisphosphate 3 kinase catalytic subunit αCTCPIK3CA52907 1 41 1
PIPProlactin induced proteinCTCPIP53041 1
PKP3Plakophilin 3CTCPKP3111871 1
PTPRKProtein tyrosine phosphatase, receptor type KCTCPTPRK57961 1
PTRFPolymerase I and transcript release factorCTCPTRF2841192 2
PTTG1Pituitary tumor transforming 1CTCPTTG192321 1
RRM2Ribonucleotide reductase regulatory subunit M2CTCRRM262411 1
S100A7S100 calcium binding protein A7CTCS100A762781 1
SCGB1D2Secretoglobin family 1D member 2CTCSCGB1D2106471 1
SLUGSnail family transcriptional repressor 2CTCSNAI265911 1
SNAIL1Snail family transcriptional repressor 1CTCSNAI166151 1
SPDEFSAM Pointed domain containing ETS transcription factorCTCSPDEF258031 1
TFF3Trefoil factor 3CTCTFF370332 1 1
TMEM45BTransmembrane protein 45BCTCTMEM45B1202241 1
TSPAN13Tetraspanin 13CTCTSPAN13270751 1
TWISTTWISTCTCTWIST172917 32 2
TYMSThymidylate synthesaseCTCTYMS72981 1
UBE2CUbiquitin conjugating enzyme E2 CCTCUBE2C110651 1
UBE2TUbiquitin conjugating enzyme E2 TCTCUBE2T290891 1
uPARTyrokinase plasminogen activator receptorCTCPLAU53282 2
TP53Tumor Protein P53CTCTP5371571 1
MRP1ATP binding cassette subfamily C member 1CTCABCC143631 1
MRP2ATP binding cassette subfamily C member 2CTCABCC212441 1
CK18Cytokeratin 18 CTCKRT1838751 1
TFF1Trefoil factor 1CTCTFF170312 1 1
BMS1BMS1 ribosome biogenesis factorCTCBMS197901 1
SOX 17 SRY Box 17CTCSOX17643211 1
* Cells given in this column represent the cells in which these biomarkers were found.
Table
Table A4. Translational stages of research on proteins investigated in parallel in studies which mainly focus on cells.
Table A4. Translational stages of research on proteins investigated in parallel in studies which mainly focus on cells.
Biomarker AbbreviationBiomarkerMembrane or Secreted ProteinGeneGene IDTotal Number of ArticlesBasic Research Proof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservationalTrialsHealth Economic Analyses
HER2Human epidermal growth factor receptor 2MembraneMUC145823 21
pFAK phosphorylated-focal adhesion kinaseMembranePTK257471 1
CA15-3CA15-3 , produced by MUC1 SecretedMUC14582171 24415
CAIXCarbonic anhydrase IXSecretedCA97681 1
CEACarcinoembryonic antigen related cell adhesion moleculeSecretedCEACAM5104861 11111
CXCL1chemokine (C-X-C Motif) Ligand-1SecretedCXCL129191 1
FibrinogenFibrinogenSecretedFGA22431 1
LDHALactate dehydrogenaseSecretedLDHA39392 1 1
M30Cytokeratin 18 fragmentsSecretedKRT1838753 1 2
P53Tumor Protein P53SecretedTP5371571 1
TGF-BTranscription Growth Factor Beta 1SecretedTGFB170401 1
TIMP-1TIMP metallopeptidase inhibitor 1SecretedTIMP170761 1
Bcl-2BCL2 apoptosis regulatorSecretedBCL25961 1
Table
Table A5. Translational stages of proteins.
Table A5. Translational stages of proteins.
Biomarker AbbreviationBiomarkerType of BiomarkerGeneGene IDTotal Number of ArticlesBasic Research Proof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservationalTrials Health Economic Analyses
EGFREpidermal growth factor receptorMembraneEGFR19569 122 4
ENDO180Mannose Receptor, C type 2 (Endo 180)MembraneMRC299021 1
EndoglinGlycoprotein co-receptor for peptides in the TGF familyMembraneENG20221 1
E-selectinSelectin E (SELE)MembraneSELE64012 1 1
HER2Soluble Human epidermal growth factor receptor 2MembraneERBB2206419 58 6
Jagged 1Jagged 1MembraneNOTCH148511 1
PDGFR-αPlatelet derived growth factor receptor αMembranePDGFRA51561 1
RAGEsoluble receptor for advanced glycation end products (sRAGE)MembraneAGER1771 1
RCFRed Cell FolateMembraneFOLR123481 1
sKITStem cell factor receptor/KIT proto-oncogene receptor tyrosine kinaseMembraneKIT38151 1
VEGFR-1Vascular Endothelial Growth Factor Receptor 1MembraneFLT123211 1
VEGFR-1Vascular Endothelial Growth Factor Receptor 1MembraneFLT123212 2
VEGFR-1Vascular Endothelial Growth Factor Receptor 1MembraneFLT123212 2
VEGFR-2Vascular Endothelial Growth Factor Receptor 2MembraneKDR37911 1
VEGFR-2Vascular Endothelial Growth Factor Receptor 2MembraneKDR37913 3
VEGFR-2Vascular Endothelial Growth Factor Receptor 2MembraneKDR37914 1 3
VEGFR-3Vascular endothelial growth factor receptor 3MembraneFLT423241 1
a2-HS-glyco-proteinfetuin-ASecretedAHSG1971 1
Activin AInhibin beta A subunitSecretedINHBA36241 1
ALPserum Alkaline PhosphataseSecretedALPL2494 12 1
bFGFFibroblast growth factor 2SecretedFGF222471 1
big ET-1big endothelin 1-growth factorSecretedEDN119061 1
CA19-9Carbohydrate antigenSecreted 2 11
CAIXCarbonic anhydrase IXSecretedCA97682 1 1
CEACarcinoembryonic antigen (CEA) SecretedCEACAM510848 17
CMLCarboxymethyllysineSecretedBCR6131 1
CRPC-reactive proteinSecretedCRP14011 1
CTXC-terminal telopeptidesecretedCYP27A115933 1 2
CYFRA21-1Cytokeratin 19 FragmentSecretedKRT19 38801 1
Cyst Cinhibitor of IL-6Secreted 1 1
DKK-1Dickkopf-1 (DKK1)SecretedDKK1229432 11
ENO1 ABSAntibodies against human α enolaseSecretedENO120231 1
ESEndostatinSecretedCOL18A1807811 1
FASNFatty acid synthaseSecretedFASN21942 1 1
FSIP1Fibrous sheath interacting proteinSecretedFSIP11618352 2
Galectin-1Galectin-1SecretedLGALS139561 1
Galectin-8Galectin-8SecretedLGALS839641 1
GASP-1G-protein coupled receptor associated sorting protein 1 SecretedGPRASP197371 1
G-CSFGranulyte colony stimulating factorSecretedCSF3R14411 1
GDF15Growth factor differentiation factor 15SecretedGDF1595181 1
GGTGamma-GlutamyltransferaseSecretedGGT126781 1
HMGB1soluble high mobility group box 1 (HMGB1) SecretedHMGB131461 1
HSP70Heat shock protein 70SecretedHSPA433081 1
IFN-yInterferon gammaSecretedIFNG34581 1
IL-10Interleukin 10SecretedIL1035861 1
IL-18Interleukin 18SecretedIL1836061 1
IL-2Interleukin 2SecretedIL235581 1
IL-4Interleukin 4SecretedIL435051 1
IL-6Interleukin 6 SecretedIL635694 22
IL-7Interleukin 7SecretedIL735741 1
IL-8CXC motif chemokine ligand 8SecretedCXCL835761 1
LDHALactate dehydrogenase SecretedLDHA39391 1
M30Cytokeratin 18 fragmentsSecretedKRT1838751 1
MidkineGrowth factor SecretedMDK41921 1
MMP2Matrix Metallopeptidase 2SecretedMMP243131 1
MMP7Matrix Metallopeptidase 7SecretedMMP743161 1
MMP9Matrix Metalloproteinase 9SecretedMMP943182 1 1
MUC1Cancer Antigen 15-3SecretedMUC1 458221 14132 1
MUC1AntigenSecretedMUC1 45821 1
MUC16Mucin 16 or Cancer antigen 125 orSecretedMUC16940253 3
Nectin-4Nectin Cell Adhesion molecule 4SecretedNECTIN4816071 1
NSENeuron specific enolaseSecretedENO220261 1
OCOsteocalcin (bone gamma-carboxylglutamate) proteinSecretedBGLAP6323 2 1
OPGOsteoprotegerin SecretedTNFRSF11B49821 1
PAI-1Plasminogen activator inhibitor 1SecretedSERPINE150541 1
Pik3CAphosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit αSecretedPIK3CA52901 1
PLGPlasminogen SecretedPLG53401 1
ProthrombinCoagulation factor IISecretedF221471 1
PTHParathyroid hormoneSecretedPTH57411 1
RANKLReceptor activator of nuclear factor kappa-B ligandSecretedTNFSF1186001 1
Survivinbaculoviral IAP repeat containing 5SecretedBIRC53321 1
TATITumor associated trypsin inhibitor SecretedSPINK66901 1
TGF-B1Transcription Growth Factor Beta 1SecretedTGFB170404 11 2
THBS-1Thrombospondin (TSP-1)SecretedTHBS170575 2 3
TIMP1Tissue inhibitor of metalloproteinase 1 SecretedTIMP170763 2 1
TK1Thymidine kinase1SecretedTK170832 11
TNCTenascin-C SecretedTNC33711 1
TPATissue polypeptide antigen Plasminogen activator, tissue typeSecretedPLAT53271 1
TRACP5aTartrate resistant acid phosphatase 5aSecretedACP5542 11
TSHThyroid stimulating hormoneSecretedTSHB72521 1
TWEAKTumor necrosis factor related weak inducer of apoptosis SecretedTNFSF1287421 1
u-PARUrokinase-type plasminogen activatorSecretedPLAU53281 1
VEGFVascular Endothelial Growth FactorSecretedVEGFA742211 4 7
VEGF-AVascular Endothelial Growth Factor ASecretedVEGFA74225 5
VEGF-CVascular Endothelial Growth Factor CSecretedVEGFC74242 1 1
YKL-40Chitinase-3-like protein 1SecretedCHI3L111162 2
1CTP C-terminal telopeptide of collagen type I Secreted CYP27A115931 1
Fibrin αfibrin alfaSecreted FGA22431 1
OPNOsteopontin (Secreted phosphoprotein 1)Secreted SPP166962 11
OsteonectinOsteonectinSecreted SPARC66781 1
PeriostinPeriostinSecreted POSTN106311 1
PhosphocholinelipoproteinSecreted PCYT1A51301 1
VCAM1Vascular endothelial adhesion moleculeSecreted VCAM174121 1
Table
Table A6. Translational stage of genes investigated in studies that focus on circulating DNA.
Table A6. Translational stage of genes investigated in studies that focus on circulating DNA.
Biomarker AbbreviationBiomarkerGeneGene IDTotal Number of ArticlesBasic Research Proof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservational Trials Health Economic Analyses
AKR1B1Aldo-keto reductase family 1AKR1B12311 1
AKT1AKT serine threonine kinase 1AKT12071 1
APCAPC, WNT signaling pathwayAPC3241 1
ARHGEF7Rho guanine nucleotide exchange factor 7ARHGEF788741 1
COL6A2Collagen type VI α 2 chainCOL6A212921 1
ESR1 Estrogen Receptor 1ESR120994 4
FGFR1 Fibroblast growth factor receptor 1FGFR122601 1
FGFR2Fibroblast growth factor receptor 2FGFR222631 1
GPX7Glutathione peroxidase 7GPX728821 1
GSTP1Glutathione S-transferase pi1GSTP129501 1
HER2Human epidermal growth factor receptor 2ERBB220642 2
HIST1H3CHistone cluster 1 H3 family member CHIST1H3C83521 1
HOXB4Homeobox B4HOXB432141 1
IDH2Isocitrate dehydrogenase 2IDH234181 1
KU86X-ray repair cross complementin 5XRCC575201 1
PIK3CAPhosphalidylinositol-4,5-bisphosphate 3 kinase catalytic subunit αPIK3CA52906 6
PTENPhosphatase and tensin homologPTEN57281 1
RARβ2 geneRetinoic acid receoptor betaRARB59151 1
RASGRF2Ras protein specific guanine nucleotide-releasing factor 2RASGRF259241 1
RASSF1ARas association domain family member 1RASSF1111866 15
StratifinStratifinSFN28101 1
TM6SF1Transmembrane 6 superfamily member 1TM6F1533461 1
TMEFF2Transmembrane protein with EGF like and two follistatin like domains 2TMEFF2236711 1
TP53Tumor Protein P53TP5371575 41
WGAWhole genome amplification**1 1
* Whole genome amplification has been performed once. No specific gene or gene ID has been reported as multiple genes can be identified. However, in the study that presented whole genome amplification, no further gene information has been presented.
Table
Table A7. Translational stages of microRNAs.
Table A7. Translational stages of microRNAs.
BiomarkerTotal Number of ArticlesBasic Research Proof of ConceptComparison of MethodsBasic Predictive ResearchBasic Prognostic ResearchPrognostic ValidationObservational Trials Health Economic Analyses
miR-10b4 31
miR-12601 1
miR-12801 1
miR-1412 11
miR-1553 3
miR-162 2
miR-171 1
miR-1971 1
miR-19a1 1
miR-200a1 1
miR-200b1 1
miR-200c2 2
miR-20151 1
miR-2031 1
miR-211 1
miR-211 1
miR-29b21 1
miR-34a2 2
miR-3731 1
miR-411 1
miR-7201 1
miR-931 1
Profiling 65 miRs1 1
U6/SNORD441 1

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Ijms 18 00363 g001 550
Figure 1. PRISMA Flow Diagram.
Figure 1. PRISMA Flow Diagram.
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Ijms 18 00363 g002 550
Figure 2. Stages of clinical translation in biomarker discovery [14,15,16,17,18,19,20,21].
Figure 2. Stages of clinical translation in biomarker discovery [14,15,16,17,18,19,20,21].
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Figure 3. Development, Evaluation and Application Chart.
Figure 3. Development, Evaluation and Application Chart.
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Table
Table 1. Most frequently investigated biomarkers of all studies included.
Table 1. Most frequently investigated biomarkers of all studies included.
Biomarker *Number of Articles% of Included StudiesEnd StageNumber of Studies at End Stage
ALDH151.6%Observational1
CA15-35115.9%Observational6
CEA195.9%Observational1
CK1961.9%Observational1
CTC enumeration15448.1%Clinical trial29
EGFR154.7%Observational6
ER134.1%Basic prognostic3
HER26119.1%Observational15
PIK3CA134.1%Observational1
PR72.2%Basic prognostic2
RASSF1A61. 9%Basic predictive5
THBS-192.8%Observational5
TP5351.6%Basic prognostic1
TWIST72.2%Observational2
VEGF226.9%Observational15
VEGFR134.1%Observational12
Vimentin61.9%Basic prognostic1
* The abbreviations used are standard abbreviations. Corresponding gene identities encoding for these biomarkers are presented in Appendix D.
Table
Table 2. Number of studies in which single biomarkers are investigated.
Table 2. Number of studies in which single biomarkers are investigated.
Number of Studies that Investigated a Specific BiomarkerFrequency% of All Included Studies
119068.8%
23813.8%
3124.3%
493.3%
5–10196.9%
>1082.9%

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Berghuis, A.M.S.; Koffijberg, H.; Prakash, J.; Terstappen, L.W.M.M.; IJzerman, M.J. Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility. Int. J. Mol. Sci. 2017, 18, 363. https://doi.org/10.3390/ijms18020363

AMA Style

Berghuis AMS, Koffijberg H, Prakash J, Terstappen LWMM, IJzerman MJ. Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility. International Journal of Molecular Sciences. 2017; 18(2):363. https://doi.org/10.3390/ijms18020363

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Berghuis, A. M. Sofie, Hendrik Koffijberg, Jai Prakash, Leon W. M. M. Terstappen, and Maarten J. IJzerman. 2017. "Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility" International Journal of Molecular Sciences 18, no. 2: 363. https://doi.org/10.3390/ijms18020363

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