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BMC Cancer

Open Access
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The evidence base for circulating tumour DNA blood-based biomarkers for the early detection of cancer: a systematic mapping review

  • Ian A. Cree1, 2, 3Email author,
  • Lesley Uttley4,
  • Helen Buckley Woods4,
  • Hugh Kikuchi5,
  • Anne Reiman2,
  • Susan Harnan4,
  • Becky L. Whiteman6,
  • Sian Taylor Philips7,
  • Michael Messenger8,
  • Angela Cox9,
  • Dawn Teare4,
  • Orla Sheils10,
  • Jacqui Shaw11 and
  • For the UK Early Cancer Detection Consortium
BMC Cancer201717:697

https://doi.org/10.1186/s12885-017-3693-7

Received: 8 March 2017

Accepted: 18 October 2017

Published: 23 October 2017

Abstract

Background

The presence of circulating cell-free DNA from tumours in blood (ctDNA) is of major importance to those interested in early cancer detection, as well as to those wishing to monitor tumour progression or diagnose the presence of activating mutations to guide treatment. In 2014, the UK Early Cancer Detection Consortium undertook a systematic mapping review of the literature to identify blood-based biomarkers with potential for the development of a non-invasive blood test for cancer screening, and which identified this as a major area of interest. This review builds on the mapping review to expand the ctDNA dataset to examine the best options for the detection of multiple cancer types.

Methods

The original mapping review was based on comprehensive searches of the electronic databases Medline, Embase, CINAHL, the Cochrane library, and Biosis to obtain relevant literature on blood-based biomarkers for cancer detection in humans (PROSPERO no. CRD42014010827). The abstracts for each paper were reviewed to determine whether validation data were reported, and then examined in full. Publications concentrating on monitoring of disease burden or mutations were excluded.

Results

The search identified 94 ctDNA studies meeting the criteria for review. All but 5 studies examined one cancer type, with breast, colorectal and lung cancers representing 60% of studies. The size and design of the studies varied widely. Controls were included in 77% of publications. The largest study included 640 patients, but the median study size was 65 cases and 35 controls, and the bulk of studies (71%) included less than 100 patients. Studies either estimated cfDNA levels non-specifically or tested for cancer-specific mutations or methylation changes (the majority using PCR-based methods).

Conclusion

We have systematically reviewed ctDNA blood biomarkers for the early detection of cancer. Pre-analytical, analytical, and post-analytical considerations were identified which need to be addressed before such biomarkers enter clinical practice. The value of small studies with no comparison between methods, or even the inclusion of controls is highly questionable, and larger validation studies will be required before such methods can be considered for early cancer detection.

Keywords

cfDNActDNACancerDetectionDiagnosisLiquid biopsy

Background

The early detection of cancers before they metastasise to other organs allows definitive local treatment, resulting in excellent survival rates. This is particularly true for breast cancer, but also others, including lung and colorectal cancer [1]. Early detection and diagnosis has therefore been a major goal of cancer research for many years, and the concept of early detection from a blood sample has been the focus of considerable effort. However, to date no blood biomarkers have had sufficient sensitivity and specificity to warrant their clinical use for early cancer detection, and their potential remains unrealised [2]. Hanahan and Weinberg [3] identified the major biological attributes of cancer, and it is apparent that most if not all of these biological processes give rise to biomarkers present in blood [4]. Circulating cell free DNA produced from cancers is known as circulating tumour DNA (ctDNA), and represents a subset of the circulating DNA (cfDNA) normally present at low levels in the blood of healthy individuals.

Since the first description of circulating cfDNA in blood [5, 6], it has become clear that total ctDNA levels rise in a number of disorders in addition to cancer including myocardial infarction [7], serious infections, and inflammatory conditions [8], as well as pregnancy where it can be used for prenatal diagnosis [9]. The source of this DNA appears to be mainly the result of cell death – either by necrosis or apoptosis [5, 911]. A raised ctDNA level is therefore non-specific, but may indicate the presence of serious disease. In blood, ctDNA is always present as small fragments, which makes assay design challenging [12]. Nevertheless, many analytical methods are available to measure ctDNA, and the field is rapidly maturing to the point where it may be clinically relevant to many patients.

In 2014, the UK Early Cancer Detection Consortium (ECDC) conducted a rapid mapping review of blood biomarkers of potential interest for cancer screening [13], and identified 814 biomarkers, including 39 ctDNA biomarkers. This paper uses the list generated from the mapping review, updated with relevant publications published since its completion to discuss the candidacy of ctDNA markers for early detection of cancer.

Methods

Our mapping review [13] conducted comprehensive searches of the electronic databases Medline, Embase, CINAHL, the Cochrane library, and Biosis to obtain relevant literature on blood-based biomarkers for cancer detection in humans (PROSPERO no. CRD42014010827). The search period finished in July 2014, therefore the searches have been updated to December 2016 using the same search terms. The abstracts of the publications retrieved were reviewed to identify those with validation data (usually indicated by case-control design) and to determine what ctDNA biomarkers had been measured in serum or plasma. Full details of the methods used are published elsewhere [13], and described briefly here. English language publications of any sample size were eligible and the full eligibility criteria used are provided in Table 1.
Table 1

Search criteria for ctDNA publications

Inclusion Criteria

Exclusion Criteria

English language studies

Studies published in non-English language

Studies within last seven years (2010–2016)

Studies published in 2009 or earlier

Controlled studies

Citation titles without abstracts

Validation Studies (comparison with controls)

Parallel publications and reviews based on the same or overlapping patient populationsa

Cancer detection/ diagnosis/screening

Prognosis or prediction (treatment response) associated markers

Biomarkers measured in blood plasma or serum

(markers or biomarkers)

Tissue, blood cells, or other bodily fluid samples

DNA (including cfDNA and ctDNA)

Abstracts of panels which do not state which biomarkers are studied

Human DNA

Viral and microbial DNA

aReviews and meta-analyses are cited, but not considered as evidence, but studies were included if they appeared to contain new data

The search strategy was deliberately inclusive, using keywords and subject headings as follows, to provide a comprehensive list of those ctDNA candidate biomarkers that had been used to identify cancers from blood samples. The search terms included ‘cancer’ ‘diagnosis’, ‘markers’, ‘blood’, and ‘screening’ with ‘DNA’, ‘cfDNA’, or ‘ctDNA’. Keywords and subject headings were determined by members of the ECDC working with the review team at the University of Sheffield. The results of the searches were collated in an Endnote database and results tabulated, with references, size of study, and methods used. To avoid bias, two reviewers conducted screening; references identified by either as relevant were included for further inspection. Those featuring ctDNA with data related to diagnosis or detection of three or more types of cancer were identified and retained for closer scrutiny to determine their potential utility.

Results

Following the updated searches and study selection, a total of 84 ctDNA markers were identified from 94 individual publications (Table 2 and Fig. 1).
Table 2

Individually identified markers with detection ability in ctDNA

No

Biomarker

Acronym

Cancer

DNA alteration

Assay type (qPCR, ddPCR, BEAMing, NGS, Other)

Size Cases (controls)

Plasma or Serum

Refs

1

14–3-3 sigma

14–3-3 s

Breast

Methylation

qPCR

106 (74)

Serum

[48]

2

absent in melanoma 1

AIM1; Beta/gamma crystallin domain-containing protein 1

Lung

Methylation

qPCR

76 (30)

Serum

[62]

3

ADAM: metallopeptidase with thrombospondin type 1 motif, 1

ADAMTS1

Pancreatic

Methylation

qPCR

42

Serum

[63]

4

Adenomatous Polyposis Coli

APC

Lung

Methylation

qPCR

76 (30)

Serum

[62]

CRC

Mutation

qPCR

33 (10)

Plasma

[64]

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

CRC

Mutation

qPCR

191

Plasma

[65]

CRC

Methylation

qPCR

33

Serum

[53]

CRC

Mutation

PCR

104

Serum

[66]

Ovarian

Methylation

qPCR

87 (62)

Serum

[67]

Renal

Methylation

PCR

35 (54)

Serum

[68]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

Lung

Methylation

qPCR

110 (50)

Plasma

[70]

Renal

Methylation

qPCR

27 (15)

Plasma

[71]

CRC

Methylation

PCR

60 (100)

Plasma

[72]

5

ALU repeat

Alu 115 bp

Breast

NA

qPCR

39 (49)

Plasma

[22]

Alu 247 bp

Pancreatic

NA

qPCR

73 (43)

Plasma

[73]

CRC

NA

qPCR

50 (35)

Plasma

[20]

Breast

NA

qPCR

293 (100)

Plasma

[19]

Thyroid

NA

qPCR

176 (19)

Plasma

[24]

CRC

NA

qPCR

104 (173)

Serum

[23]

6

basonuclin 1

BNC1

Pancreatic

Methylation

qPCR

42

Serum

[63]

7

BIN1

BIN1

Breast

Methylation

qPCR

76 (30)

Serum

[62]

8

BLU

BLU

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

9

BRAF

BRAF (V600E)

Melanoma

Mutation

qPCR

221

Both

[17]

Lung

Mutation

NGS

68 (107)

Plasma

[75]

LCH

Mutation

qPCR

30

Plasma

[76]

CRC

Mutation

qPCR

106

Plasma

[77]

Thyroid

Mutation

qPCR

77

Plasma

[78]

CRC

Mutation

BEAMing

503

Plasma

[21]

CRC

Mutation

qPCR

191

Plasma

[65]

10

BRCA1

BRCA1

Breast

Methylation

qPCR

89

Serum

[79]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

Ovarian

Methylation

PCR

50

Serum

[80]

Ovarian

Methylation

PCR

33 (33)

Plasma

[81]

11

CALCA

CALCA

Ovarian

Methylation

PCR

30 (30)

Plasma

[82]

12

CDH1

CDH1

Ovarian

Methylation

qPCR

87 (62)

Serum

[67]

13

CDH13

CDH13

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

Lung

Methylation

qPCR

110 (50)

Plasma

[70]

14

CDO1

CDO1

Various

Methylation

qPCR

150 (60)

Plasma

[83]

15

CHD1

CHD1

Lung

Methylation

qPCR

76 (30)

Serum

[62]

16

CST6

CST6

Breast

Methylation

qPCR

196 (37)

Plasma

[84]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

17

CHRM2

CHRM2

Gastric

Methylation

qPCR

58 (30)

Serum

[85]

18

CYCD2

CYCD2

CRC

Methylation

qPCR

30 (30)

Plasma

[86]

19

DAPK1

DAPK1

HNSCC

Methylation

PCR

40 (41)

Serum

[87]

20

DCC

DCC

Lung

Methylation

qPCR

76 (30)

Serum

[62]

21

DCLK1

DCLK1

Lung

Methylation

qPCR

65 (95)

Plasma

[88]

Lung

Methylation

qPCR

32 (8)

Plasma

[89]

22

DKK3

DKK3

Breast

Methylation

qPCR

604 (59)

Serum

[90]

23

DLEC1

DLEC1

Lung

Methylation

qPCR

110 (50)

Plasma

[70]

HNSCC

Methylation

PCR

40 (41)

Serum

[87]

24

DNA (NOS)

DNA

Lung

NA

qPCR v Seq

30 (26)

Plasma

[91]

Various

No

NGS

77 (35)

Plasma

[45]

Various

No

NGS

640

Plasma

[16]

Lung

No

qPCR

65 (44)

Plasma

[92]

Ovarian

No

bDNA

36 (41)

Serum

[93]

25

e-cadherin

e-cadherin

Colorectal

Methylation

PCR

60 (100)

Plasma

[72]

26

EGFR

EGFR

Lung

Mutation

NGS

68 (107)

Plasma

[75]

27

EP300

EP300

Ovarian

Methylation

PCR

30 (30)

Plasma

[82]

28

ERBB2

HER2

Lung

Mutation

NGS

68 (107)

Plasma

[75]

Breast

Amplification

qPCR

120 (98)

Plasma

[14]

Oesphageal

Amplification

qPCR

41 (34)

Plasma

[94]

29

ESR

ESR

Breast

Methylation

qPCR

106 (74)

Serum

[48]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

30

FAM5C

FAM5C

Gastric

Methylation

qPCR

58 (30)

Serum

[85]

31

FHIT

FHIT

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

Renal

Methylation

qPCR

27 (15)

Plasma

[71]

32

Glyceraldehyde-3-phosphate dehydrogenase

GAPDH

Breast

NA

qPCR

200 (100)

Serum

[26]

Breast

NA

qPCR

33 (50)

Serum

[27]

Breast

NA

qPCR

27 (32)

Serum

[28]

Breast

NA

qPCR

33 (32)

Serum

[29]

33

GNA11

GNA11

Uveal Melanoma

Mutation

NGS

28

Plasma

[34]

34

GNAQ

GNAQ

Uveal Melanoma

Mutation

NGS

28

Plasma

[34]

35

GPC3

GPC3

Pancreatic

Methylation

qPCR

30 (30)

Plasma

[86]

36

GSTP1

GSTP1

Breast

Methylation

qPCR

89

Serum

[79]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

Prostate

Methylation

PCR

12 (10)

Plasma

[95]

Prostate

Methylation

qPCR

31 (44)

Plasma

[96]

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

Renal

Methylation

PCR

35 (54)

Serum

[68]

Prostate

Methylation

PCR

31 (34)

Serum

[97]

37

HIC1

HIC1

CRC

Methylation

PCR

30 (30)

Plasma

[98]

CRC

Methylation

qPCR

30 (30)

Plasma

[86]

38

HOXA7

HOXA7

Various

Methylation

qPCR

150 (60)

Plasma

[83]

39

HOXA9

HOXA9

Various

Methylation

qPCR

150 (60)

Plasma

[83]

40

HOXD13

HOXD13

Breast

Methylation

qPCR

253 (434)

Serum

[99]

41

IgH

FR3A/VLJH

Lymphoma

Clonality

NGS

75

Plasma

[43]

42

ITIH5

 

Breast

Methylation

qPCR

604 (59)

Serum

[90]

43

INK4A

INK4A

HCC

Methylation

Seq

66 (43)

Plasma

[100]

44

KLK10

KLK10

Lung

Methylation

qPCR

110 (50)

Plasma

[70]

45

KRAS

KRAS

Lung

Mutation

NGS

68 (107)

Plasma

[75]

CRC

Mutation

qPCR

52

Plasma

[101]

CRC

Mutation

qPCR

35 (135)

Plasma

[30]

CRC

Mutation

qPCR

229 (100)

Plasma

[102]

CRC

Mutation

qPCR

106

Plasma

[77]

Lung

Mutation

qPCR

82 (11)

Plasma

[103]

CRC

Mutation

BEAMing

503

Plasma

[21]

CRC

Mutation

qPCR

191

Plasma

[65]

CRC

Mutation

PCR

104

Serum

[66]

46

LINE1 Repeat

LINE1 79 bp

CRC

NA

qPCR

50 (35)

Plasma

[20]

LINE1 300 bp

CRC

NA

qPCR

503

Plasma

[21]

Breast

NA

qPCR

293 (100)

Plasma

[19]

47

MDG1

MDG1

CRC

Methylation

PCR

30 (30)

Plasma

[98]

48

Microsatellite alterations

FHIT LoH

Lung

NA

PCR

87 (14)

Plasma

[104]

FHIT LoH

Lung

NA

PCR

32 (10)

Serum

[105]

LoH

Oesophageal

NA

PCR

18 (22)

Plasma

[106]

LoH

CRC

NA

qPCR

33

Serum

[53]

3p LoH

Lung

NA

qPCR

64

Plasma

[107]

49

mitochondrial DNA

mtDNA

Breast

NA

qPCR

60 (51)

Plasma

[108]

50

MLH1

hMLH1

Breast

Methylation

qPCR

253 (434)

Serum

[99]

51

MYC

MYC

Neuroblastoma

Amplification

ddPCR

44

Plasma

[42]

52

MYF3

MYF3

Pancreatic

Methylation

qPCR

30 (30)

Plasma

[86]

53

MYLK

MYLK

Gastric

Methylation

qPCR

58 (30)

Serum

[85]

54

O(6)-methyl-guanine-DNA methyltransferase

MGMT

Lung

Methylation

qPCR

76

Serum

[62]

CRC

Methylation

qPCR

33

Serum

[53]

Breast

Methylation

qPCR

89

Serum

[79]

55

OPCML

OPCML

Ovarian

Methylation

qPCR

87 (62)

Serum

[67]

56

P14 ARF tumor suppressor protein gene

P14

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

Renal

Methylation

PCR

35 (54)

Serum

[68]

57

P16 cyclin-dependent kinase inhibitor 2A

P16, CDKN2A

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

Renal

Methylation

PCR

35 (54)

Serum

[68]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

Breast

Methylation

qPCR

253 (434)

Serum

[99]

HNSCC

Methylation

qPCR

40 (41)

Serum

[87]

58

P21

P21

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

59

P53

 

Various

Mutation

qPCR

20 (16)

Plasma

[109]

Various

NA

qPCR

120 (120)

Plasma

[110]

CRC

Mutation

qPCR

191

Plasma

[65]

CRC

Mutation

PCR

104

Serum

[66]

SCLC

Mutation

qPCR

51 (123)

Plasma

[55]

60

PCDHGB7

PCDHGB7

Breast

Methylation

qPCR

253 (434)

Serum

[99]

61

Peptidylprolyl isomerase A

cyclophilin A, gCYC, PPIA

CRC

NA

qPCR

229 (100)

Plasma

[102]

62

PIK3CA

PIK3CA

Breast

Mutation

qPCR

76

Both

[18]

Lung

Mutation

NGS

68 (107)

Plasma

[75]

CRC

Mutation

BEAMing

503

Plasma

[21]

CRC

Mutation

qPCR

191

Plasma

[65]

63

Prostaglandin-endoperoxid synthase 2

PTGS2

Renal

Methylation

PCR

35 (54)

Serum

[68]

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

64

Protocadherin 10

PCDH10

CRC

Methylation

qPCR

67

Plasma

[111]

65

Retinoid-acid-receptor-beta gene

RARbeta2

Breast

Methylation

PCR

20 (25)

Plasma

[112]

CRC

Methylation

qPCR

33

Serum

[53]

Renal

Methylation

PCR

35 (54)

Serum

[68]

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

66

RASSF1A

RASSF1A

Breast

Methylation

PCR

93 (76)

Plasma

[113]

Breast

Methylation

PCR

20 (25)

Plasma

[112]

Breast

Methylation

qPCR

39 (49)

Plasma

[22]

Breast

Methylation

qPCR

604 (59)

Serum

[90]

Melanoma

Methylation

qPCR

84 (68)

Plasma

[114]

Lung

Methylation

qPCR

76 (30)

Serum

[62]

Testicular

Methylation

qPCR

73 (35)

Serum

[47]

CRC

Methylation

qPCR

33

Serum

[53]

Ovarian

Methylation

qPCR

87 (62)

Serum

[67]

Renal

Methylation

PCR

35 (54)

Serum

[68]

Lung

Methylation

qPCR

63 (36)

Plasma

[74]

Lung

Methylation

qPCR

110 (50)

Plasma

[70]

HCC

Methylation

PCR

40 (20)

Serum

[115, 116]

HCC

Methylation

PCR

50 (50)

Serum

[117]

Renal

Methylation

PCR

27 (15)

Plasma

[71]

Breast

Methylation

qPCR

253 (434)

Serum

[99]

CRC

Methylation

PCR

30 (30)

Plasma

[98]

Renal

Methylation

qPCR

157 (43)

Serum

[118]

Ovarian

Methylation

PCR

50

Serum

[80]

Ovarian

Methylation

PCR

30 (30)

Plasma

[82]

67

RUNX3

RUNX3

Ovarian

Methylation

PCR

87 (62)

Serum

[67]

68

Septin 9

Septin 9

CRC

Methylation

qPCR

97 (172)

Plasma

[119]

CRC

Methylation

qPCR

378 (285)

Plasma

[120]

CRC

Methylation

qPCR

60 (24)

Plasma

[121]

CRC

Methylation

qPCR

55 (1457)

Plasma

[58]

Lung

Methylation

qPCR

70 (100)

Plasma

[122]

CRC

Methylation

qPCR

135 (341)

Plasma

[123]

CRC

Methylation

qPCR

50 (94)

Plasma

[124]

CRC

Methylation

qPCR

44 (444)

Plasma

[59]

69

SFN

SFN

Breast

Methylation

qPCR

253 (434)

Serum

[99]

70

SFRP5

SFRP5

Ovarian

Methylation

qPCR

87 (62)

Serum

[67]

71

SHOX2

SHOX2

Lung

Methylation

qPCR

188 (155)

Plasma

[125]

Lung

Methylation

qPCR

118 (212

Plasma

[126]

72

SOX17

SOX17

Breast

Methylation

qPCR

114 (60)

Plasma

[127]

Various

Methylation

qPCR

150(60)

Plasma

[83]

73

SLC26A4

SLC26A4

Thyroid

Methylation

qPCR

176 (19)

Plasma

[24]

74

SLC5A8

SLC5A8 SLC26A4

Thyroid

Methylation

qPCR

176 (19)

Plasma

[24]

75

SRBC

SRBC

Pancreatic

Methylation

qPCR

30 (30)

Plasma

[86]

76

TAC1

TAC1

Various

Methylation

qPCR

150 (60)

Plasma

[83]

77

human telomerase reverse transcriptase DNA

hTERT

CRC

NA

qPCR

35 (135)

Plasma

[30]

HCC

NA

qPCR

70 (30)

Plasma

[31]

HCC

NA

qPCR

60 (29)

Plasma

[32]

HNSCC

NA

qPCR

200

Plasma

[33]

78

TFPI2

TFPI2

Ovarian

Methylation

PCR

87 (62)

Serum

[67]

79

THBD-M

THBD-M

CRC

Methylation

qPCR

107 (98)

Plasma & Serum

[128]

80

TIMP3

TIMP3

Renal

Methylation

PCR

35 (54)

Serum

[68]

Breast

Methylation

qPCR

36 (30)

Plasma

[69]

81

TMS

TMS

Pancreatic

Methylation

qPCR

30 (30)

Plasma

[86]

82

UCHL1

UCHL1

HNSCC

Methylation

PCR

40 (41)

Serum

[87]

83

Von Hippel Lindau gene

VHL

CRC

Methylation

qPCR

30 (30)

Plasma

[86]

Pancreatic

Methylation

qPCR

30 (30)

Plasma

[86]

Renal

Methylation

qPCR

157 (43)

Serum

[118]

84

ZFP42

ZFP42

Various

Methylation

qPCR

150 (60)

Plasma

[83]

CRC colorectal cancer, HNSCC head and neck squamous cell carcinoma, HCC hepatocellular carcinoma, LCH Langerhans cell histocytosis, SCLC small cell lung cancer

Fig. 1

PRISMA diagram

The ctDNA biomarkers divided naturally into two groups:
  1. I.

    those with potential specificity for neoplasia (ctDNA - usually mutations or DNA alterations such as methylation), and

     
  2. II.

    those designed to measure DNA levels, which may not be specific to neoplasia.

     
Figure 2 shows the distribution of studies by cancer type, including two publications on amplification [12, 14], and one on clonality [15]. One of the amplification papers looked at HER2 [14], while the other examined multiple targets by NGS [12].
Fig. 2

Number of targets and publications by tumour type, showing the expected concentration of studies on common cancer types. CRC, colorectal cancer; HNSCC, head and neck squamous cell carcinoma; HCC, hepatocellular carcinoma

Of the 94 publications included, 72 publications (77%) were case-control design diagnostic validation studies, and 22 were case series. The size and design of the studies varied widely. The largest study included 640 cancer patients [16]. The median study size was 65 cases, with a mean of 98 cases (range 12–640 cancer patients), indicating that the bulk of studies (67/94, 71%) included <100 patients (Fig. 3).
Fig. 3

Study size. There are occasional large studies, but the vast majority are small, evidenced by the low median and averages for both cases and controls

Most publications were focussed on ctDNA in plasma (n = 67) rather than serum (n = 25) with 2 comparing both. Plasma was used for 38 markers, and serum for 28 markers, and either for 18 markers (Fig. 4). Two comparative studies of serum and plasma were conducted: one for BRAF mutations, and the other for PIK3CA mutations [17, 18].
Fig. 4

Use of serum or plasma for studies. The majority use plasma, but serum is preferred for methylation studies by some. Only three studies looked at both serum and plasma

The target of ctDNA studies and the methods used to measure these targets varied considerably (Figs. 5 and 6 respectively). Non-specific total ctDNA levels (quantitation) were usually estimated by size distribution assays based on repeats: LINE1, and ALU were used in 3 [1921] and 6 publications respectively [2025]. However, some single genes were also used to measure DNA levels – particularly GAPDH in a series of 4 publications on breast cancer [2629], and hTERT in 4 publications [3033]. The majority of publications examined gene methylation markers (n = 49), though most examined methylation of multiple target genes for a particular tumour type (Fig. 5). Genes commonly mutated in cancer were also markers of interest, namely APC, BRAF, EGFR, HER2, GNAQ, GNA11, KRAS, P53, and PIK3CA. Only one gene, APC, was studied for both methylation and mutation. Few markers were used to identify particular tumour types, but some are particularly likely to occur in certain tumour types. GNAQ and GNA11 mutations have been identified in the plasma of uveal melanoma patients and are rare in other tumour types [34]. Other mutations are not tumour type-specific, and mutations in 6 of the 9 genes listed above were reported in multiple tumour types.
Fig. 5

Targets: many studies looked at multiple targets, mainly either mutations or methylated genes

Fig. 6

Choice of method. Most publications used just one method, but biomarkers were measurable by more than one assay in 6 instances

Discussion

The number of publications on ctDNA is increasing rapidly [35, 36], and a recent review emphasises the potential of the field [37]. Most (71%) are small case control studies with less than 100 patients, and in our view very few studies meet the requirements of analytical validation allowing their use within accredited (ISO:15,189) clinical laboratories, though some may have unpublished commercially-held analytical validation data. The stage and size of the tumours included is variable, and few studies are large enough to give robust subgroup assessments. Larger tumours produce more ctDNA, though tumour type also has an impact [16]. The value of small studies with no comparison between methods, or even the inclusion of controls is highly questionable. Most include a statement that ‘larger studies are required’, but larger trials rarely result due to the necessary cost implications. Unless well-designed prospective studies based on sample size calculations are performed, there is little likelihood of such methods reaching clinical practice for the detection of cancer at an early stage. There is also a likelihood of bias in that negative results for these markers are rarely if ever reported, and unlike clinical trials, there is no requirement for the registration of diagnostic validation studies. The use of ctDNA for early cancer detection comes under existing molecular pathology guidance, which emphasises the requirements for careful pre-analytical preparation, analysis, and reporting of results [38]. It is important that studies adhere to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidance [39], and regional guidance (e.g. US Food and Drug Adminstration (FDA); UK National Institute for Health and Care Excellence (NICE); Clinical & Laboratory Standards Institute (CLSI)). It is hardly surprising then that, to date, no ctDNA markers have made it into screening programmes, due in part to the economic feasibility of completing the necessary stages of validation [40]. Nevertheless, there is encouraging evidence that ctDNA can be used to detect cancers of many types [16], and the poor quality of many studies should not detract from this fact.

A plethora of methods are available for ctDNA measurement, which have been well reviewed elsewhere [41]. BEAMing, PCR clamping methods, and deep sequencing using NGS are now the most commonly used [42, 43] and are widely regarded as the most sensitive methods currently available. A recent report of copy number variation (CNV) in breast cancer is not surprising given the ability of this method to detect such changes in pregnancy [15]. However, it should be noted that many of these methods are expensive. The development of highly sensitive NGS methods for ctDNA may prove necessary to obtain the best results [44], but large blood samples (> 10 ml may be needed as the number of DNA molecules present in small samples is often low) [45]. This may be at odds with the key requirement of cost effectiveness for screening programmes, and in our view this represents a real challenge for ctDNA. The problem is probably not insuperable if automation allows the integration of such methods into large blood sciences laboratories, but this is not as yet the case.

As ctDNA is composed largely of short fragments, short amplicons are required for maximum sensitivity of PCR reactions, particularly if mutations are being detected [46]. This is compounded by DNA loss in some reactions, particularly bisulphite modification of DNA, and it may be preferable to use nuclease protection assays [47, 48]. Methylation of key genes involved in carcinogenesis can be found in ctDNA, and has been studied by many groups, but it should be noted that substantial numbers of normal controls also have methylation of ctDNA for these genes [49].

It is clear that high sensitivity methods will be needed if ctDNA is to be used for early cancer detection. Several factors affect the sensitivity of ctDNA measurement. The first is the extraction method, and there are as yet too few studies which have compared the different options available, which now include automated instruments as well as manual extraction systems [50, 51]. The proportion of tumour derived DNA (ctDNA) in total cfDNA is greater in plasma than serum, and the higher ctDNA levels in serum are due to leakage from leukocytes during clotting [17]. The dilution effect for ctDNA in serum results in a reduced ability to detect mutations, particularly by methods with low analytical sensitivity [50]. Most groups working in the field realise this, and the majority of publications now look at plasma rather than serum.

Several publications were noteworthy, including one influential study which did not include healthy controls [16]. However, the comparison of DNA levels and multiple mutations in plasma from many different tumours types is helpful [44], and makes it clear that some tumours (e.g. gliomas) do not have high ctDNA levels in plasma, as previously found when comparing CSF with plasma [52]. This is also one of several publications that examines early stage disease, and shows that patients with localised disease have lower ctDNA levels [16]. Few publications have examined the ability of ctDNA to detect smaller tumours, though all agree that ctDNA levels increase as tumours enlarge [42].

Choice of target also influences results: the use of LINE1 and ALU repeats allows quantitative size distribution of DNA to be measured. Several publications suggest that this can distinguish cancer, and even pre-cancerous conditions from controls [30]. The size distribution of CRC appears to be different from other tumours due to first pass hepatic metabolism [20, 53]. Absolute quantitation by single gene methods such as GAPDH or hTERT will result in lower estimates of DNA content, and it is likely that this is due to the higher sensitivity of the ALU and LINE1 assays [30].

The use of mutations common within cancers is attractive, and the use of ctDNA to provide companion diagnostic information in patients in whom biopsy material is not available is now entering practice [54]. However, it should be noted that such mutations in P53 can occur in the blood of healthy controls, and could give rise to substantial numbers of false positive results [55].

Septin 9 methylation is often regarded as a model for future work [56, 57], and it is notable that there are some large studies [58] within the evidence base for the use of this marker in colorectal cancer, often used in addition to other markers, such as faecal occult blood testing (FoBT) or faecal immunohistochemical testing (FIT). Pre-analytical factors have been examined for this marker [59], including diurnal variation [60]. Plasma methylation of Septin 9 is now available as a commercial test (Epi proColon 2.0; Epigenomics AG, Berlin, Germany) which has recently obtained FDA approval for colorectal cancer screening (April 2016). This is the first blood test to be approved for cancer screening, and represents an encouraging milestone.

Other methylation targets have been studied in depth and show considerable promise. These include APC for colorectal cancer, with a large number of studies (Table 2), and SHOX3, for which a recent meta-analysis suggests that it could have an important role in the diagnosis of lung cancer [61].

There is an encouraging trend towards larger, more ambitious studies, supported by the commercial sector (e.g. (https://clinicaltrials.gov/ct2/show/NCT02889978, and https://clinicaltrials.gov/ct2/show/NCT03085888). Case control studies (particular retrospective ones) can give biased results, and prospective studies in at-risk cohorts would be more useful in examining the predictive capability of these markers. Such prospective studies should include controls proven not to have cancer. The comparison of new with existing methods (e.g. tumour markers, radiology), and competing technologies, is recommended, and often required by regulators. This has cost implications for funding bodies, but is essential if the field is to progress rapidly.

Conclusions

While ctDNA analysis may provide a viable option for the early detection of cancers, not all cancers are detectable using current methods. However, improvements in technology are rapidly overcoming some of the issues of analytical sensitivity, and it is likely that mutation and methylation analysis of ctDNA will improve specificity for the diagnosis of cancer.

Abbreviations

14–3-3 s: 

14–3-3 sigma or tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein theta

ADAM: 

metallopeptidase with thrombospondin type 1 motif, 1

AIM1: 

absent in melanoma 1

ALU: 

Alu repeat/element 9e

APC: 

Adenomatous Polyposis Coli

ARF: 

alternate reading frame

BIN1: 

bridging integrator 1

BLU: 

zinc finger MYND-type containing 10

BM: 

biomarker

BNC1: 

basonuclin 1

bp: 

base pair

BRAF: 

B-Raf proto-oncogene, serine/threonine kinase

BRCA1: 

breast cancer 1, DNA repair associated

BRINP3: 

BMP/Retinoic Acid Inducible Neural Specific 3

CALCA: 

calcitonin related polypeptide alpha

CDH1: 

cadherin 1

CDH13: 

cadherin 13

CDO1: 

cysteine dioxygenase type 1

cfDNA: 

circulating cell-free DNA

CHD1: 

chromodomain helicase DNA binding protein 1

CHRM2: 

cholinergic receptor muscarinic 2

CINAHL: 

Cumulative Index to Nursing and Allied Health Literature

CLSI: 

Clinical & Laboratory Standards Institute

CRC: 

colorectal carcinoma

CST6: 

cystatin 6

ctDNA: 

circulating tumour DNA

CYCD2: 

cyclin D2

DAPK1: 

death-associated protein kinase 1

DCC: 

DCC Netrin 1 receptor

DCLK1: 

doublecortin like kinase 1

ddPCR: 

digital droplet polymerase chain reaction

DKK3: 

Dickkopf WNT signaling pathway inhibitor 3

DLEC1: 

deleted in lung and esophageal cancer 1

DNA: 

dexoxyribonucleic acid

ECDC: 

UK Early Cancer Detection Consortium

EGFR: 

epidermal growth factor receptor (HER1)

EP300: 

E1A binding protein P300

ERBB2: 

erb-B2 receptor tyrosine kinase 2 (HER2)

ESR: 

estrogen receptor 1

FAM5C: 

BMP/retinoic acid inducible neural specific 3 (BRINP3)

FDA: 

US Food and Drug Adminstration

FHIT: 

fragile histidine triad

FIT: 

faecal immunohistochemical testing

FoBT: 

faecal occult blood testing

GAPDH: 

glyceraldehyde-3-phosphate dehydrogenase

gCYC: 

cyclophilin A

GNA11: 

G protein subunit alpha 11

GNAQ: 

G protein subunit alpha Q

GPC3: 

glypican 3

GSTP1: 

glutathione S-transferase pi 1

HCC: 

hepatocellular carcinoma

HER1: 

human epidermal growth factor receptor 1

HER2: 

human epidermal growth factor receptor 2

HIC1: 

HIC ZBTB transcriptional repressor 1

HNSCC: 

head and neck squamous cell carcinoma

HOXA7: 

Homeobox A7

HOXA9: 

Homeobox A9

HOXD13: 

Homeobox D13

hTERT: 

human telomerase reverse transcriptase DNA

IgH: 

immunoglobulin heavy locus

INK4A: 

cyclin dependent kinase inhibitor 2A (CDKN2A/P16)

ISO: 

International Standards Organization

ITIH5: 

inter-alpha-trypsin inhibitor heavy chain family member 5

KLK10: 

kallikrein related peptidase 10

KRAS: 

KRAS Proto-Oncogene, GTPase

LCH: 

Langerhans cell histocytosis

LINE1: 

long interspersed nuclear element 1

LoH: 

loss of heterozygosity

Max: 

maximum

MDG1: 

microvascular endothelial differentiation gene 1

MGMT: 

O(6)-methyl-guanine-DNA methyltransferase

Min: 

minimum

MLH1: 

MutL Homolog 1

mtDNA: 

mitochondrial DNA

MYC: 

MYC proto-oncogene

MYF3: 

myogenic differentiation 1 (MYOD1)

MYLK: 

myosin light chain kinase

NGS: 

next generation sequencing

NICE: 

UK National Institute for Health and Care Excellence

NOS: 

not otherwise specified

OPCML: 

opioid binding protein/cell adhesion molecule like

P14: 

P14 ARF tumor suppressor protein gene

P16: 

P16 cyclin-dependent kinase inhibitor 2A (CDKN2A)

P21: 

cyclin dependent kinase inhibitor 1A

P53: 

tumor protein P53

PCDH10: 

Protocadherin 10

PCDHGB7: 

protocadherin gamma subfamily B7

PCR: 

polymerase chain reaction

PIK3CA: 

phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha

PPIA: 

Peptidylprolyl isomerase A

PTGS2: 

Prostaglandin-endoperoxid synthase 2

qPCR: 

quantitative polymerase chain reaction

RARbeta2: 

Retinoid-acid-receptor-beta gene

RASSF1A: 

Ras association domain family member 1

RUNX3: 

runt related transcription factor 3

SFN: 

Stratifin

SFRP5: 

secreted frizzled related protein 5

SHOX2: 

short stature homeobox 2

SLC26A4: 

solute carrier family 26 member 4

SLC5A8: 

solute carrier family 5 member 8

SOX17: 

SRY-Box 17

SRBC: 

serum deprivation response factor-related gene

STARD: 

Standards for Reporting of Diagnostic Accuracy Studies

TAC1: 

tachykinin precursor 1

TFPI2: 

tissue factor pathway inhibitor 2

THBD-M: 

thrombomodulin

TIMP3: 

tissue inhibitor of metalloproteinase 3

TMS: 

tumor differentially expressed protein 1

UCHL1: 

Ubiquitin C-Terminal Hydrolase L1

V600E: 

Mutation resulting in an amino acid substitution at position 600 in BRAF, from a valine (V) to a glutamic acid (E)

VHL: 

Von Hippel Lindau gene

ZFP42: 

ZFP42 Zinc Finger Protein

Declarations

Acknowledgements

We are grateful to the wider Early Cancer Detection Consortium for their assistance in putting together this paper, and for the many discussions which underpin it. Patient and Public representatives were involved in this work.

Funding

This work was conducted on behalf of the Early Cancer Detection Consortium, within the programme of work for work packages & 2. The Early Cancer Detection Consortium is funded by Cancer Research UK under grant number: C50028/A18554. It was subsequently supported by an unrestricted educational grant from PinPoint Cancer Ltd. (www.pinpointcancer.co.uk), following cessation of the grant in 2016. Neither of the two funding bodies had any input or influence over the design, study, collection, analysis, or interpretation of the data.

Availability of data and materials

The papers quoted are publically available from the publishers, and many are now open access.

Authors’ contributions

IC, SH, BW, and STP designed the study. Searches were performed by HBW. LU and HBW performed the mapping review with input from the ECDC. HK and IC scanned the resulting publications relating to ctDNA. The draft manuscript was prepared by IC with input fom MM, AC, DT, OS, AR, HK, HBW, BW and JS. All authors agreed the final version. All authors read and approved the final manuscript.

Authors’ information

IC is a pathologist and has recently moved to a post with the International Agency for Research on Cancer of the World Health Organisation in Lyon. LU, and SH are Research Fellows in systematic review and HBW is an Information Specialist working at the University of Sheffield, UK. HK is a scientist and PhD student working on early cancer detection. AR is a Lecturer in Biomedical Science working at Coventry University, UK. STP is an associate professor with a NIHR Career Development Fellowship using quantitative research methods to assess new screening programmes. MM is a healthcare scientist at the University of Leeds with expertise in biomarker and in vitro diagnostic (IVD) development, validation and clinical evaluation. AC is Professor of Cancer Genetic Epidemiology at the University of Sheffield, UK. DT is Reader in Epidemiology and Biostatistics at the University of Sheffield, UK. OS is Director of the Trinity Translational Medicine Institute (TTMI) and Professor in Molecular Pathology at Trinity College Dublin, Eire. JS is Professor of Translational Cancer Genetics at Leicester University, UK, with a particular interest in cfDNA.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The ECDC has grant funding for early cancer biomarker research from Cancer Research UK who funded this work. The ECDC involves several companies as follows: GE Healthcare, Life Technologies, NALIA Systems Ltd., and Perkin-Elmer. Individual ECDC members have declared their interests to the ECDC secretariat. IC was formerly chairman and CEO of PinPoint Cancer Ltd., a spin-out company from ECDC which in part funded the completion of this work though provision of staff time (IC). MM is supported by the National Institute for Health Research Diagnostic Evidence Co-operative Leeds. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the UK Department of Health.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
WHO Classification of Tumours Group, International Agency for Research on Cancer (IARC), World Health Organization
(2)
Faculty of Health and Life Sciences, Coventry University
(3)
Institute of Ophthalmology, University College London
(4)
The School of Health and Related Research, The University of Sheffield
(5)
Department of Pathology, University Hospitals Coventry and Warwickshire
(6)
London North West Healthcare NHS Trust, Northwick Park Hospital
(7)
Warwick Medical School, University of Warwick
(8)
Leeds Centre for Personalised Medicine and Health, University of Leeds and NIHR Diagnostic Evidence Co-Operative Leeds, Leeds Teaching Hospitals NHS Trust
(9)
Sheffield Institute for Nucleic Acids, Department of Oncology and Metabolism, The University of Sheffield, Medical School
(10)
Sir Patrick Dun Research Laboratory, Central Pathology Laboratory, St James’s Hospital & Trinity College Dublin
(11)
University of Leicester, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary

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