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International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2017 Feb 9;18(2):363. doi: 10.3390/ijms18020363

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

A M Sofie Berghuis 1,*, Hendrik Koffijberg 1, Jai Prakash 2, Leon W M M Terstappen 3, Maarten J IJzerman 1
Editor: William Chi-shing Cho
PMCID: PMC5343898  PMID: 28208771

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.

Keywords: metastatic breast cancer, liquid biopsy, circulating biomarkers, blood-based biomarkers, circulating tumor cells (CTCs), utility, developmental stages, development evaluation and application chart (DEAC)

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.

Figure 1.

Figure 1

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PRISMA Flow Diagram.

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.

Figure 2.

Figure 2

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Stages of clinical translation in biomarker discovery [14,15,16,17,18,19,20,21].

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).

Figure 3.

Figure 3

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Development, Evaluation and Application Chart.

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.

Table 1.

Most frequently investigated biomarkers of all studies included.

Biomarker * Number of Articles % of Included Studies End Stage Number of Studies at End Stage
ALDH1 5 1.6% Observational 1
CA15-3 51 15.9% Observational 6
CEA 19 5.9% Observational 1
CK19 6 1.9% Observational 1
CTC enumeration 154 48.1% Clinical trial 29
EGFR 15 4.7% Observational 6
ER 13 4.1% Basic prognostic 3
HER2 61 19.1% Observational 15
PIK3CA 13 4.1% Observational 1
PR 7 2.2% Basic prognostic 2
RASSF1A 6 1. 9% Basic predictive 5
THBS-1 9 2.8% Observational 5
TP53 5 1.6% Basic prognostic 1
TWIST 7 2.2% Observational 2
VEGF 22 6.9% Observational 15
VEGFR 13 4.1% Observational 12
Vimentin 6 1.9% Basic prognostic 1
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* The abbreviations used are standard abbreviations. Corresponding gene identities encoding for these biomarkers are presented in Appendix D.

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.

Table 2.

Number of studies in which single biomarkers are investigated.

Number of Studies that Investigated a Specific Biomarker Frequency % of All Included Studies
1 190 68.8%
2 38 13.8%
3 12 4.3%
4 9 3.3%
5–10 19 6.9%
>10 8 2.9%
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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.

Abbreviations

CA15-3 Cancer Antigen 15-3
CTC Circulating Tumor Cell
DEAC Development, Evaluation and Application Chart
PFS Progression Free Survival
HER2 Human Epidermal Growth Factor Receptor 2
OS Overall Survival
VEGF Vascular Endothelial Growth Factor
VEGFR Vascular Endothelial Growth Factor Receptor
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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.

Figure A1.

Figure A1

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Review Protocol.

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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  2. Windrichova J, Fuchsova R, Kucera R, Topolcan O, Fiala O, Finek J, et al. Testing of a Novel Cancer Metastatic Multiplex Panel for the Detection of Bone-metastatic Disease—A Pilot Study. Anticancer research. 2016;36(4):1973-8.

  3. Shiomi-Mouri Y, Kousaka J, Ando T, Tetsuka R, Nakano S, Yoshida M, et al. Clinical significance of circulating tumor cells (CTCs) with respect to optimal cut-off value and tumor markers in advanced/metastatic breast cancer. Breast cancer (Tokyo, Japan). 2016;23(1):120-7.

  4. Reijm EA, Sieuwerts AM, Smid M, Bolt-de Vries J, Mostert B, Onstenk W, et al. An 8-gene mRNA expression profile in circulating tumor cells predicts response to aromatase inhibitors in metastatic breast cancer patients. BMC cancer. 2016;16.

  5. 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.

  6. Peng CK, Chen HD, Wallwiener M, Modugno C, Cuk K, Madhavan D, et al. Plasma S100P level as a novel prognostic marker of metastatic breast cancer. Breast cancer research and treatment. 2016;157(2):329-38.

  7. Peng C, Wallwiener M, Rudolph A, Cuk K, Eilber U, Celik M, et al. Plasma hyaluronic acid level as a prognostic and monitoring marker of metastatic breast cancer. International journal of cancer. 2016;138(10):2499-509.

  8. Nakauchi C, Kagara N, Shimazu K, Shimomura A, Naoi Y, Shimoda M, et al. Detection of TP53/PIK3CA Mutations in Cell-Free Plasma DNA From Metastatic Breast Cancer Patients Using Next Generation Sequencing. Clinical breast cancer. 2016;16(5):418-23.

  9. Mu ZM, Benali-Furet N, Uzan G, Znaty A, Ye Z, Paolillo C, et al. Detection and Characterization of Circulating Tumor Associated Cells in Metastatic Breast Cancer. International journal of molecular sciences. 2016;17(10).

  10. Matthew EM, Zhou L, Yang Z, Dicker DT, Holder SL, Lim B, et al. A multiplexed marker-based algorithm for diagnosis of carcinoma of unknown primary using circulating tumor cells. Oncotarget. 2016;7(4):3662-76.

  11. Lee CK, Davies L, Gebski VJ, Lord SJ, Di Leo A, Johnston S, et al. Serum Human Epidermal Growth Factor 2 Extracellular Domain as a Predictive Biomarker for Lapatinib Treatment Efficacy in Patients With Advanced Breast Cancer. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2016;34(9):936-44.

  12. Kristiansen S, Nielsen D, Soletormos G. Detection and monitoring of hypermethylated RASSF1A in serum from patients with metastatic breast cancer. Clinical epigenetics. 2016;8:35.

  13. Hyun KA, Goo KB, Han H, Sohn J, Choi W, Kim SI, et al. Epithelial-to-mesenchymal transition leads to loss of EpCAM and different physical properties in circulating tumor cells from metastatic breast cancer. Oncotarget. 2016;7(17):24677-87.

  14. Hainsworth JD, Murphy PB, Alemar JR, Daniel BR, Young RR, Yardley DA. Use of a multiplexed immunoassay (PRO Onc assay) to detect HER2 abnormalities in circulating tumor cells of women with HER2-negative metastatic breast cancer: Lack of response to HER2-targeted therapy. Breast cancer research and treatment. 2016;160(1):41-9.

  15. Gogoi P, Sepehri S, Zhou Y, Gorin MA, Paolillo C, Capoluongo E, et al. Development of an Automated and Sensitive Microfluidic Device for Capturing and Characterizing Circulating Tumor Cells (CTCs) from Clinical Blood Samples. PloS one. 2016;11(1):e0147400.

  16. Gasch C, Oldopp T, Mauermann O, Gorges TM, Andreas A, Coith C, et al. Frequent detection of PIK3CA mutations in single circulating tumor cells of patients suffering from HER2-negative metastatic breast cancer. Molecular oncology. 2016;10(8):1330-43.

  17. Deutsch TM, Riethdorf S, Nees J, Hartkopf AD, Schonfisch B, Domschke C, et al. Impact of apoptotic circulating tumor cells (aCTC) in metastatic breast cancer. Breast cancer research and treatment. 2016;160(2):277-90.

  18. De Luca F, Rotunno G, Salvianti F, Galardi F, Pestrin M, Gabellini S, et al. Mutational analysis of single circulating tumor cells by next generation sequencing in metastatic breast cancer. Oncotarget. 2016;7(18):26107-19.

  19. Bulfoni M, Gerratana L, Del Ben F, Marzinotto S, Sorrentino M, Turetta M, et al. In patients with metastatic breast cancer the identification of circulating tumor cells in epithelial-to-mesenchymal transition is associated with a poor prognosis. Breast cancer research: BCR. 2016;18(1):30.

  20. Brown-Glaberman U, Marron M, Chalasani P, Livingston R, Iannone M, Specht J, et al. Circulating Carbonic Anhydrase IX and Antiangiogenic Therapy in Breast Cancer. Disease markers. 2016;2016:9810383.

  21. Bredemeier M, Edimiris P, Tewes M, Mach P, Aktas B, Schellbach D, et al. Establishment of a multimarker qPCR panel for the molecular characterization of circulating tumor cells in blood samples of metastatic breast cancer patients during the course of palliative treatment. Oncotarget. 2016;7(27):41677-90.

  22. Bednarz-Knoll N, Efstathiou A, Gotzhein F, Wikman H, Mueller V, Kang Y, et al. Potential Involvement of Jagged1 in Metastatic Progression of Human Breast Carcinomas. Clinical chemistry. 2016;62(2):378-86.

  23. Aktas B, Kasimir-Bauer S, Muller V, Janni W, Fehm T, Wallwiener D, et al. Comparison of the HER2, estrogen and progesterone receptor expression profile of primary tumor, metastases and circulating tumor cells in metastatic breast cancer patients. BMC cancer. 2016;16.

  24. Zhang H, Luo M, Jin Z, Wang D, Sun M, Zhao X, et al. Expression and clinicopathological significance of FSIP1 in breast cancer. Oncotarget. 2015;6(12):10658-66.

  25. Wang HY, Ahn S, Kim S, Park S, Jung D, Park S, et al. Detection of circulating tumor cell-specific markers in breast cancer patients using the quantitative RT-PCR assay. International journal of clinical oncology. 2015;20(5):878-90.

  26. Wang D, Liu X, Hsieh B, Bruce R, Somlo G, Huang J, et al. Exploring Glycan Markers for Immunotyping and Precision-targeting of Breast Circulating Tumor Cells. Archives of medical research. 2015;46(8):642-50.

  27. Wallwiener M, Hartkopf AD, Riethdorf S, Nees J, Sprick MR, Schonfisch B, et al. The impact of HER2 phenotype of circulating tumor cells in metastatic breast cancer: a retrospective study in 107 patients. BMC cancer. 2015;15:403.

  28. Vishnoi M, Peddibhotla S, Yin W, Scamardo, AT, George GC, Hong DS, et al. The isolation and characterization of CTC subsets related to breast cancer dormancy. Scientific reports. 2015;5:17533.

  29. Staudigl C, Concin N, Grimm C, Pfeiler G, Nehoda R, Singer CF, et al. Prognostic relevance of pretherapeutic gamma-glutamyltransferase in patients with primary metastatic breast cancer. PloS one. 2015;10(4):e0125317.

  30. Shen B, Zheng MQ, Xu XY, Wei D. Translational Medicine Study on Circulating Tumor Cell Detection in Patients with Metastatic Breast Cancer. Journal of International Translational Medicine. 2015;3(2):93-7.

  31. Shaker O, Maher M, Nassar Y, Morcos G, Gad Z. Role of microRNAs -29b-2, -155, -197 and -205 as diagnostic biomarkers in serum of breast cancer females. Gene. 2015;560(1):77-82.

  32. Sefrioui D, Perdrix A, Sarafan-Vasseur N, Dolfus C, Dujon A, Picquenot JM, et al. Short report: Monitoring ESR1 mutations by circulating tumor DNA in aromatase inhibitor resistant metastatic breast cancer. International journal of cancer. 2015;137(10):2513-9.

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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 A1.

Translational stages of research on the enumeration of whole cells or cell clusters.

Biomarker Abbreviation Biomarker Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
CAMLs Cancer associated macrophage like cells 1 1
CAFs Cancer associated fibroblasts 1 1
Monocyte CD63 Type of white blood cell CD63 1 1
Monocyte CD64 Type of white blood cell CD64 1 1
CECs Circulating epithelial cells 2 2
CEPs Circulating epithelial progenitor cells 2 2
CETC Circulating epithelial tumor cells 2 1 1
aCTCs Apoptotic circulating tumor cells 1 1
CSCs Cancer Stem Cells 2 1 1
CTC Circulating tumor cells 156 4 19 20 46 35 1 29 2
CTC Cluster Circulating tumor cell clusters 3 1 1 1
NKs Natural Killer Cells 2 2
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Table A2.

Translational stages of research on the membrane expression of proteins.

Biomarker Abbreviation Biomarker Cell * Gene Gene ID Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
Akt2 AKT serine theronine kinase 2 CTC AKT2 208 4 1 1 1 1
CD133 Prominin 1 CTC PROM1 8842 1 1
CD44 CD44 molecule CTC CD44 960 1 1
ER Estrogen CTC ESR1 2099 13 1 2 1 6 3
Fibronectin Fibronectin CTC FN1 2335 1 1
HER 2 Human epidermal growth factor receptor 2 CTC ERBB2 2064 25 1 1 3 8 4 0 8
N-Cadherin Cadherin 2 CTC CDH2 1000 3 1 2
PR Progesteron CTC PGR 5241 7 1 4 2
VEGF Vascular endothelial growth factor CTC VEGFA 7422 4 2 2
VEGFR2 Vascular endothelial growth factor receptor 2 CTC KDR 3791 1 1
Vimentin Vimentin CTC VIM 7431 6 1 4 1
CD24 CD24 molecule Granulocytes CD24 100133941 1 1
TLR2 Toll Like Receptor 2 Lymphocytes TLR2 7097 1 1
TLR4 Toll Like Receptor 4 Lymphocytes TLR4 7099 1 1
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* Cells given in this column represent the cells in which these biomarkers were found.

Table A3.

Translational stages of research on gene expression within cell DNA or mRNA.

Biomarker Abbreviation Biomarker Cell * Gene Gene ID Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
CD34 CD34 Molecule CSCs CD34 947 1 1
Nanog NANOG CSCs NANOG 79923 1 1
Nestin Nestin CSCs NES 10763 1 1
Oct3/4 POU class 5 homeobox 1 CSCs POU5F1 5460 1 1
Sox2 SRY Box 2 CSCs SOX2 6657 1 1
ACTA1 Actin/α 1, skeletal muscle CTC ACTA1 58 1 1
AGR2 Anterior gradient 2 protein disulphide isomerase family member CTC AGR2 10551 1 1
ALDH1 Aldehyde dehydrogenase 1 family member A1 CTC ALDH1A1 216 5 1 1 2 1
AURKA Aurora Kinase A CTC AURKA 6790 1 1
BCL2 BCL2 apoptosis regulator CTC BCL2 596 1 1
BIRC5 baculoviral IAP repeat containing 5 CTC BIRC5 332 1 1
CCND1 Cyclin D1 CTC CCND1 595 1 1
CDC6 Cell Division Cycle 6 CTC CDC6 990 1 1
CENPF Centromere protein F CTC CENPF 1063 1 1
CEP55 Centrosomal protein 55 CTC CEP55 55165 2 1 1
CK19 Keratin type I cytoskeletal 19 CTC KRT19 3880 6 4 1 1
CK8 Cytokeratin 8 CTC KRT8 3856 1 1
CRABP2 Cellular retinoic acid binding protein 2 CTC CRABP2 1382 1 1
CSt6 promotor Cystatin E/M CTC CST6 1474 1 1
CXCL14 CXC motif chemokine ligand 14 CTC CXCL14 9547 2 2
CXXC5 CXXC finger protein 5 CTC CXXC5 51523 1 1
DTX3 Deltex E3 ubiquitin ligase 3 CTC DTX3 196403 1 1
DUSP4 Dual specificity phosphatase 4 CTC DUSP4 1846 1 1
EEF1A2 Eukaryotic translation elongation factor 1 α 2 CTC EEF1A2 1917 2 2
EGFR Epidermal growth factor receptor CTC EGFR 1956 6 1 2 1 2
ERBB3 ERB-b2 receptor tyrosine kinase 3 CTC ERBB3 2065 2 1 1
ERBB4 ERB-b2 receptor tyrosine kinase 4 CTC ERBB4 2066 1 1
ERCC1 ERCC excision repair 1, endonuclease non-catalytic subunit CTC ERCC1 2067 1 1
ESR1 Estrogen Receptor 1 CTC ESR1 2099 3 1 2
FGFR4 Fibroblast gorwth factor receptor 4 CTC FGFR4 2264 2 1 1
FKBP10 FK506 binding protein 10 CTC FKBP10 60681 1 1
FOX A1 Forkhead box A1 CTC FOX A1 3169 1 1
FOXC1 Forkhead box C1 CTC FOXC1 2296 1 1
GAPDH Glyceraldehyde-3-phosphate dehydrogenase CTC GAPDH 2507 1 1
HER2 Human epidermal growth factor receptor 2 CTC ERBB2 2064 17 1 3 2 9 1 1
HIF-1a hypoxia inducible factor-1 α CTC HIF1A 3091 1 1
IGFBP2 Insulin like growth factor binding protein 2 CTC IGFBP2 3485 1 1
IGFBP4 Insulin like growth factor binding protein 4 CTC IGFBP4 3487 1 1
IL17 BR Interleukin 17 receptor B CTC IL17RB 55540 1 1
ITGA6 Integrin subunit α CTC ITGA6 3655 1 1
Ki67 (proliferation marker) CTC MKI67 4288 1 1
KRT14 Keratin 14 CTC KRT14 3861 1 1
KRT17 Keratin 17 CTC KRT17 3872 1 1
KRT19 Keratin 19 CTC KRT19 3880 2 2
KRT20 Keratin type I cytoskeletal 20 CTC KRT20 54474 1 1
KRT7 Keratin 7 CTC KRT7 3855 1 1
KRT81 Keratin 81 CTC KRT81 3887 1 1
LAD1 Ladinin 1 CTC LAD1 3898 1 1
Mamma-globin Secretoglobin family 2a member 2 CTC SCGB2A2 4250 3 1 2
MELK Maternal embryonic leucine zipper kinase CTC MELK 9833 1 1
MUC1 CA15-3 CTC MUC1 4582 5 1 1 2 1
MYBL2 MYB proto-oncogene like 2 CTC MYBL2 4605 1 1
NDC80 NDC80 Kinetochore complex component CTC NDC80 10403 1 1
NUF2 NUF2, NDC80 kinetochore complex component CTC NUF2 83540 1 1
PIK3CA Phosphalidylinositol-4,5-bisphosphate 3 kinase catalytic subunit α CTC PIK3CA 5290 7 1 4 1 1
PIP Prolactin induced protein CTC PIP 5304 1 1
PKP3 Plakophilin 3 CTC PKP3 11187 1 1
PTPRK Protein tyrosine phosphatase, receptor type K CTC PTPRK 5796 1 1
PTRF Polymerase I and transcript release factor CTC PTRF 284119 2 2
PTTG1 Pituitary tumor transforming 1 CTC PTTG1 9232 1 1
RRM2 Ribonucleotide reductase regulatory subunit M2 CTC RRM2 6241 1 1
S100A7 S100 calcium binding protein A7 CTC S100A7 6278 1 1
SCGB1D2 Secretoglobin family 1D member 2 CTC SCGB1D2 10647 1 1
SLUG Snail family transcriptional repressor 2 CTC SNAI2 6591 1 1
SNAIL1 Snail family transcriptional repressor 1 CTC SNAI1 6615 1 1
SPDEF SAM Pointed domain containing ETS transcription factor CTC SPDEF 25803 1 1
TFF3 Trefoil factor 3 CTC TFF3 7033 2 1 1
TMEM45B Transmembrane protein 45B CTC TMEM45B 120224 1 1
TSPAN13 Tetraspanin 13 CTC TSPAN13 27075 1 1
TWIST TWIST CTC TWIST1 7291 7 3 2 2
TYMS Thymidylate synthesase CTC TYMS 7298 1 1
UBE2C Ubiquitin conjugating enzyme E2 C CTC UBE2C 11065 1 1
UBE2T Ubiquitin conjugating enzyme E2 T CTC UBE2T 29089 1 1
uPAR Tyrokinase plasminogen activator receptor CTC PLAU 5328 2 2
TP53 Tumor Protein P53 CTC TP53 7157 1 1
MRP1 ATP binding cassette subfamily C member 1 CTC ABCC1 4363 1 1
MRP2 ATP binding cassette subfamily C member 2 CTC ABCC2 1244 1 1
CK18 Cytokeratin 18 CTC KRT18 3875 1 1
TFF1 Trefoil factor 1 CTC TFF1 7031 2 1 1
BMS1 BMS1 ribosome biogenesis factor CTC BMS1 9790 1 1
SOX 17 SRY Box 17 CTC SOX17 64321 1 1
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* Cells given in this column represent the cells in which these biomarkers were found.

Table A4.

Translational stages of research on proteins investigated in parallel in studies which mainly focus on cells.

Biomarker Abbreviation Biomarker Membrane or Secreted Protein Gene Gene ID Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
HER2 Human epidermal growth factor receptor 2 Membrane MUC1 4582 3 2 1
pFAK phosphorylated-focal adhesion kinase Membrane PTK2 5747 1 1
CA15-3 CA15-3 , produced by MUC1 Secreted MUC1 4582 17 1 2 4 4 1 5
CAIX Carbonic anhydrase IX Secreted CA9 768 1 1
CEA Carcinoembryonic antigen related cell adhesion molecule Secreted CEACAM5 1048 6 1 1 1 1 1 1
CXCL1 chemokine (C-X-C Motif) Ligand-1 Secreted CXCL1 2919 1 1
Fibrinogen Fibrinogen Secreted FGA 2243 1 1
LDHA Lactate dehydrogenase Secreted LDHA 3939 2 1 1
M30 Cytokeratin 18 fragments Secreted KRT18 3875 3 1 2
P53 Tumor Protein P53 Secreted TP53 7157 1 1
TGF-B Transcription Growth Factor Beta 1 Secreted TGFB1 7040 1 1
TIMP-1 TIMP metallopeptidase inhibitor 1 Secreted TIMP1 7076 1 1
Bcl-2 BCL2 apoptosis regulator Secreted BCL2 596 1 1
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Table A5.

Translational stages of proteins.

Biomarker Abbreviation Biomarker Type of Biomarker Gene Gene ID Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
EGFR Epidermal growth factor receptor Membrane EGFR 1956 9 1 2 2 4
ENDO180 Mannose Receptor, C type 2 (Endo 180) Membrane MRC2 9902 1 1
Endoglin Glycoprotein co-receptor for peptides in the TGF family Membrane ENG 2022 1 1
E-selectin Selectin E (SELE) Membrane SELE 6401 2 1 1
HER2 Soluble Human epidermal growth factor receptor 2 Membrane ERBB2 2064 19 5 8 6
Jagged 1 Jagged 1 Membrane NOTCH1 4851 1 1
PDGFR-α Platelet derived growth factor receptor α Membrane PDGFRA 5156 1 1
RAGE soluble receptor for advanced glycation end products (sRAGE) Membrane AGER 177 1 1
RCF Red Cell Folate Membrane FOLR1 2348 1 1
sKIT Stem cell factor receptor/KIT proto-oncogene receptor tyrosine kinase Membrane KIT 3815 1 1
VEGFR-1 Vascular Endothelial Growth Factor Receptor 1 Membrane FLT1 2321 1 1
VEGFR-1 Vascular Endothelial Growth Factor Receptor 1 Membrane FLT1 2321 2 2
VEGFR-1 Vascular Endothelial Growth Factor Receptor 1 Membrane FLT1 2321 2 2
VEGFR-2 Vascular Endothelial Growth Factor Receptor 2 Membrane KDR 3791 1 1
VEGFR-2 Vascular Endothelial Growth Factor Receptor 2 Membrane KDR 3791 3 3
VEGFR-2 Vascular Endothelial Growth Factor Receptor 2 Membrane KDR 3791 4 1 3
VEGFR-3 Vascular endothelial growth factor receptor 3 Membrane FLT4 2324 1 1
a2-HS-glyco-protein fetuin-A Secreted AHSG 197 1 1
Activin A Inhibin beta A subunit Secreted INHBA 3624 1 1
ALP serum Alkaline Phosphatase Secreted ALPL 249 4 1 2 1
bFGF Fibroblast growth factor 2 Secreted FGF2 2247 1 1
big ET-1 big endothelin 1-growth factor Secreted EDN1 1906 1 1
CA19-9 Carbohydrate antigen Secreted 2 1 1
CAIX Carbonic anhydrase IX Secreted CA9 768 2 1 1
CEA Carcinoembryonic antigen (CEA) Secreted CEACAM5 1084 8 1 7
CML Carboxymethyllysine Secreted BCR 613 1 1
CRP C-reactive protein Secreted CRP 1401 1 1
CTX C-terminal telopeptide secreted CYP27A1 1593 3 1 2
CYFRA21-1 Cytokeratin 19 Fragment Secreted KRT19 3880 1 1
Cyst C inhibitor of IL-6 Secreted 1 1
DKK-1 Dickkopf-1 (DKK1) Secreted DKK1 22943 2 1 1
ENO1 ABS Antibodies against human α enolase Secreted ENO1 2023 1 1
ES Endostatin Secreted COL18A1 80781 1 1
FASN Fatty acid synthase Secreted FASN 2194 2 1 1
FSIP1 Fibrous sheath interacting protein Secreted FSIP1 161835 2 2
Galectin-1 Galectin-1 Secreted LGALS1 3956 1 1
Galectin-8 Galectin-8 Secreted LGALS8 3964 1 1
GASP-1 G-protein coupled receptor associated sorting protein 1 Secreted GPRASP1 9737 1 1
G-CSF Granulyte colony stimulating factor Secreted CSF3R 1441 1 1
GDF15 Growth factor differentiation factor 15 Secreted GDF15 9518 1 1
GGT Gamma-Glutamyltransferase Secreted GGT1 2678 1 1
HMGB1 soluble high mobility group box 1 (HMGB1) Secreted HMGB1 3146 1 1
HSP70 Heat shock protein 70 Secreted HSPA4 3308 1 1
IFN-y Interferon gamma Secreted IFNG 3458 1 1
IL-10 Interleukin 10 Secreted IL10 3586 1 1
IL-18 Interleukin 18 Secreted IL18 3606 1 1
IL-2 Interleukin 2 Secreted IL2 3558 1 1
IL-4 Interleukin 4 Secreted IL4 3505 1 1
IL-6 Interleukin 6 Secreted IL6 3569 4 2 2
IL-7 Interleukin 7 Secreted IL7 3574 1 1
IL-8 CXC motif chemokine ligand 8 Secreted CXCL8 3576 1 1
LDHA Lactate dehydrogenase Secreted LDHA 3939 1 1
M30 Cytokeratin 18 fragments Secreted KRT18 3875 1 1
Midkine Growth factor Secreted MDK 4192 1 1
MMP2 Matrix Metallopeptidase 2 Secreted MMP2 4313 1 1
MMP7 Matrix Metallopeptidase 7 Secreted MMP7 4316 1 1
MMP9 Matrix Metalloproteinase 9 Secreted MMP9 4318 2 1 1
MUC1 Cancer Antigen 15-3 Secreted MUC1 4582 21 1 4 13 2 1
MUC1 Antigen Secreted MUC1 4582 1 1
MUC16 Mucin 16 or Cancer antigen 125 or Secreted MUC16 94025 3 3
Nectin-4 Nectin Cell Adhesion molecule 4 Secreted NECTIN4 81607 1 1
NSE Neuron specific enolase Secreted ENO2 2026 1 1
OC Osteocalcin (bone gamma-carboxylglutamate) protein Secreted BGLAP 632 3 2 1
OPG Osteoprotegerin Secreted TNFRSF11B 4982 1 1
PAI-1 Plasminogen activator inhibitor 1 Secreted SERPINE1 5054 1 1
Pik3CA phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α Secreted PIK3CA 5290 1 1
PLG Plasminogen Secreted PLG 5340 1 1
Prothrombin Coagulation factor II Secreted F2 2147 1 1
PTH Parathyroid hormone Secreted PTH 5741 1 1
RANKL Receptor activator of nuclear factor kappa-B ligand Secreted TNFSF11 8600 1 1
Survivin baculoviral IAP repeat containing 5 Secreted BIRC5 332 1 1
TATI Tumor associated trypsin inhibitor Secreted SPINK 6690 1 1
TGF-B1 Transcription Growth Factor Beta 1 Secreted TGFB1 7040 4 1 1 2
THBS-1 Thrombospondin (TSP-1) Secreted THBS1 7057 5 2 3
TIMP1 Tissue inhibitor of metalloproteinase 1 Secreted TIMP1 7076 3 2 1
TK1 Thymidine kinase1 Secreted TK1 7083 2 1 1
TNC Tenascin-C Secreted TNC 3371 1 1
TPA Tissue polypeptide antigen Plasminogen activator, tissue type Secreted PLAT 5327 1 1
TRACP5a Tartrate resistant acid phosphatase 5a Secreted ACP5 54 2 1 1
TSH Thyroid stimulating hormone Secreted TSHB 7252 1 1
TWEAK Tumor necrosis factor related weak inducer of apoptosis Secreted TNFSF12 8742 1 1
u-PAR Urokinase-type plasminogen activator Secreted PLAU 5328 1 1
VEGF Vascular Endothelial Growth Factor Secreted VEGFA 7422 11 4 7
VEGF-A Vascular Endothelial Growth Factor A Secreted VEGFA 7422 5 5
VEGF-C Vascular Endothelial Growth Factor C Secreted VEGFC 7424 2 1 1
YKL-40 Chitinase-3-like protein 1 Secreted CHI3L1 1116 2 2
1CTP C-terminal telopeptide of collagen type I Secreted CYP27A1 1593 1 1
Fibrin α fibrin alfa Secreted FGA 2243 1 1
OPN Osteopontin (Secreted phosphoprotein 1) Secreted SPP1 6696 2 1 1
Osteonectin Osteonectin Secreted SPARC 6678 1 1
Periostin Periostin Secreted POSTN 10631 1 1
Phosphocholine lipoprotein Secreted PCYT1A 5130 1 1
VCAM1 Vascular endothelial adhesion molecule Secreted VCAM1 7412 1 1
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Table A6.

Translational stage of genes investigated in studies that focus on circulating DNA.

Biomarker Abbreviation Biomarker Gene Gene ID Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
AKR1B1 Aldo-keto reductase family 1 AKR1B1 231 1 1
AKT1 AKT serine threonine kinase 1 AKT1 207 1 1
APC APC, WNT signaling pathway APC 324 1 1
ARHGEF7 Rho guanine nucleotide exchange factor 7 ARHGEF7 8874 1 1
COL6A2 Collagen type VI α 2 chain COL6A2 1292 1 1
ESR1 Estrogen Receptor 1 ESR1 2099 4 4
FGFR1 Fibroblast growth factor receptor 1 FGFR1 2260 1 1
FGFR2 Fibroblast growth factor receptor 2 FGFR2 2263 1 1
GPX7 Glutathione peroxidase 7 GPX7 2882 1 1
GSTP1 Glutathione S-transferase pi1 GSTP1 2950 1 1
HER2 Human epidermal growth factor receptor 2 ERBB2 2064 2 2
HIST1H3C Histone cluster 1 H3 family member C HIST1H3C 8352 1 1
HOXB4 Homeobox B4 HOXB4 3214 1 1
IDH2 Isocitrate dehydrogenase 2 IDH2 3418 1 1
KU86 X-ray repair cross complementin 5 XRCC5 7520 1 1
PIK3CA Phosphalidylinositol-4,5-bisphosphate 3 kinase catalytic subunit α PIK3CA 5290 6 6
PTEN Phosphatase and tensin homolog PTEN 5728 1 1
RARβ2 gene Retinoic acid receoptor beta RARB 5915 1 1
RASGRF2 Ras protein specific guanine nucleotide-releasing factor 2 RASGRF2 5924 1 1
RASSF1A Ras association domain family member 1 RASSF1 11186 6 1 5
Stratifin Stratifin SFN 2810 1 1
TM6SF1 Transmembrane 6 superfamily member 1 TM6F1 53346 1 1
TMEFF2 Transmembrane protein with EGF like and two follistatin like domains 2 TMEFF2 23671 1 1
TP53 Tumor Protein P53 TP53 7157 5 4 1
WGA Whole genome amplification * * 1 1
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* 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 A7.

Translational stages of microRNAs.

Biomarker Total Number of Articles Basic Research Proof of Concept Comparison of Methods Basic Predictive Research Basic Prognostic Research Prognostic Validation Observational Trials Health Economic Analyses
miR-10b 4 3 1
miR-1260 1 1
miR-1280 1 1
miR-141 2 1 1
miR-155 3 3
miR-16 2 2
miR-17 1 1
miR-197 1 1
miR-19a 1 1
miR-200a 1 1
miR-200b 1 1
miR-200c 2 2
miR-2015 1 1
miR-203 1 1
miR-21 1 1
miR-21 1 1
miR-29b2 1 1
miR-34a 2 2
miR-373 1 1
miR-41 1 1
miR-720 1 1
miR-93 1 1
Profiling 65 miRs 1 1
U6/SNORD44 1 1
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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.

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