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

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.

Figure 1

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

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

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

* 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%

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.

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

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

* 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

* 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

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

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

* 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