December 2012 Circulating DNA markers of Gastro-intestinal … · 2017. 1. 19. · gastro-intestinal cancer biomarker literature reporting the strengths, weaknesses and successes

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University of Portsmouth

MD Thesis

Dr. R. J. Mead

Queen Alexandra Hospital, Portsmouth.

December 2012

Circulating DNA markers

of Gastro-intestinal

Cancers

The thesis is submitted in partial fulfilment of the

requirements for the award of the degree of Doctor

of Medicine of the University of Portsmouth

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Abstract.

Introduction: Early diagnosis represents the best opportunity for cure of gastro-intestinal

cancers; however current gastro-intestinal cancer screening programmes have low test

sensitivity and low patient acceptability. It is hoped that a better understanding of

carcinogenesis and the development of new biomarkers will provide answers to these

clinical problems.

Hypothesis and aims: This thesis examines the current understanding of gastro-intestinal

carcinogenesis focusing on colorectal and oesophageal cancers. It reviews the circulating

gastro-intestinal cancer biomarker literature reporting the strengths, weaknesses and

successes of current approaches.

The study tests the hypothesis that control patients and patients with colorectal and

oesophageal neoplasia have differing plasma levels and fragmentation patterns of cell free

nuclear and mitochondrial DNA, and that endoscopic removal of these lesions returns the

levels and patterns to normal.

We aim to show how new techniques of DNA processing can improve results, and how the

application of these results could form part of a diagnostic approach in an at risk

population. We compare our results to and including carcinoembryonic antigen levels.

Results: Cell free DNA was isolated from 164 patients, including 71 patients with

oesophageal neoplasia, 50 patients with colorectal neoplasia, 35 patients without

endoscopic abnormality and 8 patients with Barrett’s oesophagus.

This is the first report of statistically significant differences in circulating DNA quantities and

patterns in patients with early oesophageal and colorectal neoplasia (p≤ 0.005). Logistic

regression of the best DNA marker for colorectal neoplasia demonstrated a ROC of 0.888,

with a sensitivity of 81.1% and specificity of 75.8%. Logistic regression of the best DNA

marker for oesophageal neoplasia demonstrated a ROC of 0.778 with a sensitivity of 81.3%

and specificity of 60.5%. Carcinoembryonic antigen performed poorly with a ROC of 0.547

and did not add diagnostically. There were no significant changes in markers from patients

resected at endoscopy.

Conclusions: These circulating markers in combination with other markers offer the

prospect of a simple blood test as a possible screen for colorectal and oesophageal

dysplasia and cancers in an at risk population.

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Table of Contents.

Abstract. ................................................................................................................................ 1-2

Table of Contents. ................................................................................................................. 1-3

Declaration. ......................................................................................................................... 1-10

List of Figures. ..................................................................................................................... 1-11

List of Tables. ...................................................................................................................... 1-13

Acknowledgements. ............................................................................................................ 1-15

Dissemination. .................................................................................................................... 1-15

Chapter 1 Gastro-intestinal cancers in the UK. ................................................................... 1-16

1.1 Introduction. ....................................................................................................... 1-16

1.2 Carcinogenesis theory. ........................................................................................ 1-17

1.2.1 Gene sequence changes. ............................................................................ 1-17

1.2.2 Epigenetic changes. ..................................................................................... 1-18

1.2.3 Extra-genetic changes. ................................................................................ 1-18

1.2.4 The metaplasia, dysplasia, carcinoma sequence. ....................................... 1-19

1.3 The Colon. ........................................................................................................... 1-19

1.3.1 Polyp adenoma and carcinoma pathogenesis. ........................................... 1-19

1.3.2 Serrated adenomas and Lynch syndrome. ................................................. 1-21

1.4 The oesophagus. ................................................................................................. 1-21

1.4.1 Barrett’s oesophagus pathogenesis. ........................................................... 1-21

1.4.2 Barrett’s dysplasia and carcinoma pathogenesis. ....................................... 1-22

1.5 Genetic hallmarks in colon and oesophageal adenocarcinomas. ....................... 1-25

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1.6 Clinical diagnosis of oesophageal and colonic carcinomas. ................................ 1-28

1.6.1 UK Colorectal cancer screening guidelines. ................................................ 1-28

1.6.2 UK Barrett’s oesophagus surveillance guidelines. ...................................... 1-30

1.7 Conclusions. ........................................................................................................ 1-31

Chapter 2 Circulating markers of gastro-intestinal cancers................................................ 2-33

2.1 Introduction. ....................................................................................................... 2-33

2.2 Circulating DNA tumour markers – genomics. .................................................... 2-34

2.2.1 Origins of circulating DNA. .......................................................................... 2-34

2.2.2 Circulating cell free DNA Studies. ............................................................... 2-35

2.2.2.1 Nuclear DNA studies. .............................................................................. 2-36

2.2.2.1.1 DNA quantification............................................................................ 2-36

2.2.2.1.2 DNA hyper-methylation. ................................................................... 2-38

2.2.2.1.3 DNA hypo-methylation. .................................................................... 2-39

2.2.2.1.4 DNA Mutation. .................................................................................. 2-39

2.2.2.1.5 Loss of heterozygosity, allelic imbalance, and aneuploidy. .............. 2-40

2.2.2.1.6 DNA sequencing. ............................................................................... 2-41

2.2.2.2 Mitochondrial DNA studies. .................................................................... 2-42

2.3 Circulating RNA tumour markers – transcriptomics. .......................................... 2-42

2.3.1 RNA marker studies. ................................................................................... 2-43

2.4 Circulating protein tumour markers- proteomics. .............................................. 2-44

2.4.1 Origins of circulating proteins. .................................................................... 2-44

2.4.2 Circulating protein studies .......................................................................... 2-45

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2.4.2.1 Protein signature studies ........................................................................ 2-46

2.4.2.2 Circulating Tumour antigen studies ........................................................ 2-47

2.4.2.3 Autologous T-cell activating antigen studies .......................................... 2-49

2.4.2.4 Auto-antibody studies ............................................................................. 2-49

2.4.2.5 Miscellaneous protein studies ................................................................ 2-50

2.4.2.5.1 MMP - matrix metalloproteinases .................................................... 2-50

2.4.2.5.2 TIMP 1 (Tissue inhibitor of metalloproteinases)............................... 2-50

2.4.2.5.3 Tissue Polypeptide antigen - cytokeratins 8, 18 and 19 ................... 2-51

2.4.2.5.4 Specific Tissue Polypeptide antigen – cytokeratin 18 ....................... 2-51

2.4.2.5.5 CYFRA 21-1 – cytokeratin 19 ............................................................. 2-51

2.4.2.5.6 Nuclear matrix protein antigens ....................................................... 2-51

2.4.2.5.7 VEGF – vascular endothelial growth factor ...................................... 2-52

2.4.2.5.8 CD 44 ................................................................................................. 2-52

2.4.2.5.9 Kallikrein ........................................................................................... 2-52

2.4.2.5.10 Laminin .............................................................................................. 2-53

2.4.2.5.11 βHCG - beta human chorionic gonadotrophin .................................. 2-53

2.5 Circulating tumour cells. ..................................................................................... 2-53

2.6 Circulating DNA markers in colonic and oesophageal adenocarcinoma. ........... 2-53

2.7 Conclusions. ........................................................................................................... 56

Experimental Hypothesis and Aims .................................................................................... 2-57

Chapter 3 Methods. ............................................................................................................ 3-58

3.1 Introduction. ....................................................................................................... 3-58

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3.1.1 Plasma processing. ...................................................................................... 3-58

3.1.2 DNA isolation and purification. ................................................................... 3-58

3.1.2.1 High Yield Plasma-XS nucleospin modification. ...................................... 3-59

3.1.3 DNA quantification. ..................................................................................... 3-60

3.1.4 Absolute qPCR. ............................................................................................ 3-63

3.1.4.1 Primer selection and optimization. ......................................................... 3-63

3.1.4.2 Amplicons and cycling conditions. .......................................................... 3-65

3.1.4.3 DNA quantity calculation. ....................................................................... 3-67

3.1.4.4 DNA dilution correction factor. ............................................................... 3-69

3.1.4.5 PCR assay co-efficient of variation results. ............................................. 3-69

3.1.5 CEA analysis. ................................................................................................ 3-70

3.1.6 Patient choice, pathological grading and disease staging. ......................... 3-71

3.1.7 Statistical analysis. ...................................................................................... 3-71

3.2 Experimental protocols. ...................................................................................... 3-73

3.2.1 Venepuncture. ............................................................................................ 3-73

3.2.2 Cell and plasma separation. ........................................................................ 3-73

3.2.3 Nucleospin DNA purification. ...................................................................... 3-74

3.2.4 qPCR. ........................................................................................................... 3-75

3.2.5 CEA analysis. ................................................................................................ 3-76

Chapter 4 Colon results. ...................................................................................................... 4-77

4.1 Demographics. .................................................................................................... 4-77

4.2 Patient groups. .................................................................................................... 4-78

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4.3 Mean and median marker values. ...................................................................... 4-78

4.4 Statistical significance - Mann Whitney U-tests. ................................................ 4-79

4.5 Logistic regression and ROC curve values. .......................................................... 4-80

4.6 Box plots and whisker analysis............................................................................ 4-82

4.7 Correlation of Total cfDNA with age. .................................................................. 4-86

4.8 Conclusions. ........................................................................................................ 4-86

Chapter 5 Oesophago-gastric results. ................................................................................. 5-88

5.1 Demographics. .................................................................................................... 5-88

5.2 Patient groups. .................................................................................................... 5-89

5.3 Mean and median marker values. ...................................................................... 5-90

5.4 Statistical significance - Mann Whitney U-tests. ................................................ 5-90

5.5 ROC curve values and Logistic regression. .......................................................... 5-91

5.6 Box plots and whisker analysis............................................................................ 5-93

5.7 Conclusions. ........................................................................................................ 5-96

Chapter 6 Paired colonic and oesophageal results. ............................................................ 6-98

6.1 Introduction. ....................................................................................................... 6-98

6.2 Colon results. ...................................................................................................... 6-98

6.2.1 Demographics . ........................................................................................... 6-98

6.2.2 Lesions. ........................................................................................................ 6-98

6.2.3 Mean results. .............................................................................................. 6-98

6.2.4 Significance testing. .................................................................................... 6-99

6.2.5 Individual markers. .................................................................................... 6-100

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6.2.5.1 CEA analysis. .......................................................................................... 6-100

6.2.5.2 Line 1 79bp - total cfDNA. ..................................................................... 6-100

6.2.5.3 Mitochondrial cfDNA. ........................................................................... 6-101

6.2.5.4 Alu 115bp fragment. ............................................................................ 6-102

6.2.5.5 Line 1 300bp fragment. ......................................................................... 6-103

6.2.5.6 Alu 247bp fragment. ............................................................................. 6-103

6.2.6 Marker ratios............................................................................................. 6-104

6.2.6.1 Line 1 79:300. ........................................................................................ 6-104

6.2.6.2 Alu 115:247. .......................................................................................... 6-105

6.3 Oesophageal results. ......................................................................................... 6-107

6.3.1 Demographics . ......................................................................................... 6-107

6.3.2 Lesions. ...................................................................................................... 6-107

6.3.3 Mean results. ............................................................................................ 6-107

6.3.4 Significance testing. .................................................................................. 6-108

6.3.5 Individual markers. .................................................................................... 6-108

6.3.5.1 Line 1 79bp - total cfDNA. ..................................................................... 6-108

6.3.5.2 Mitochondrial cfDNA. ........................................................................... 6-109

6.3.5.3 Alu 115 fragment. ................................................................................. 6-110

6.3.5.4 Line 1 300bp fragment. ......................................................................... 6-110

6.3.5.5 Alu 247bp fragment. ............................................................................ 6-111

6.3.6 Marker ratios............................................................................................. 6-111

6.3.6.1 Line 1 79:300. ........................................................................................ 6-111

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6.3.6.2 Alu 115:247. .......................................................................................... 6-112

6.4 Conclusions. ...................................................................................................... 6-113

Chapter 7 Discussion and conclusions. ................................................................................ 114

7.1 Control population results. .................................................................................. 114

7.2 Colonic results. ..................................................................................................... 116

7.3 Oesophageal results. ............................................................................................ 120

7.4 Paired results. ...................................................................................................... 123

7.5 Sample processing. .............................................................................................. 125

7.6 Implication for the origins of circulating DNA. .................................................... 125

7.7 Diagnostic testing. ................................................................................................ 127

7.8 Conclusions. ......................................................................................................... 128

Addendum ........................................................................................................................... 129

Reflections after Viva Voce examination ............................................................................. 129

Example of experimental amplification curves - Line 1 79bp .............................................. 131

Raw data - sample set up line 1 79bp ............................................................................... 7-132

Raw data - results Line 1 79bp .......................................................................................... 7-141

References ........................................................................................................................... 153

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Declaration.

Whilst registered as a candidate for the above degree, I have not been registered for any

other research award. Results and conclusions embodied in this thesis are my work and

have not been submitted for any other academic award.

Word count = 34,030

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List of Figures.

Figure 1 -Top 20 cancers by incidence in the UK 2009. ...................................................... 1-16

Figure 2 Adenoma pathways to cancer. ............................................................................. 1-20

Figure 3 COX II pathways in Barrett’s oesophagus. ............................................................ 1-24

Figure 4 Modified DNA loading results. .............................................................................. 3-60

Figure 5 Melt curve for Line 1 79 bp 400/400 primer amplification. ................................. 3-64

Figure 6 Line 1 79 bp standard curve. ................................................................................. 3-65

Figure 7 Plasma XS spin protocol. ....................................................................................... 3-75

Figure 8 Combined DNA marker ROC curve. ...................................................................... 4-81

Figure 9 Combined DNA marker plus CEA ROC curve. ........................................................ 4-82

Figure 10 CEA box plot analysis. ......................................................................................... 4-82

Figure 11 Total Nuclear DNA box plot analysis. .................................................................. 4-83

Figure 12 Mitochondrial DNA box plot analysis. ................................................................. 4-83

Figure 13 Alu 155bp DNA box plot analysis. ....................................................................... 4-84

Figure 14 Alu 247bp DNA box plot analysis. ...................................................................... 4-84

Figure 15 Line 1 300bp DNA box plot analysis. ................................................................... 4-85

Figure 16 Line 1 79:300 DNA ratio box plot analysis. ......................................................... 4-85

Figure 17 Alu 115:247 DNA ratio box plot analysis. ........................................................... 4-86

Figure 18 Correlation of total cfDNA with age. ................................................................... 4-86

Figure 19 Normal group vs. all invasive cancer group. ....................................................... 5-92

Figure 20 Normal group vs. all abnormal oesophagus group. ............................................ 5-92

Figure 21 Total Nuclear DNA box plot analysis. .................................................................. 5-93

Figure 22 Mitochondrial DNA box plot analysis. ................................................................. 5-94

Figure 23 Alu115bp DNA box plot analysis. ........................................................................ 5-94

Figure 24 Alu 247bp DNA box plot analysis. ....................................................................... 5-95

Figure 25 Line 1 300bp DNA box plot analysis. ................................................................... 5-95

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Figure 26 Line 1 79:300 DNA fragment ratio box plot analysis. ......................................... 5-96

Figure 27 Alu 115:247 DNA fragment ratio box plot analysis. ............................................ 5-96

Figure 28 Paired CEA values pre and post resection. ....................................................... 6-100

Figure 29 Paired total cfDNA values pre and post resection. ........................................... 6-101

Figure 30 Paired mitochondrial cfDNA values pre and post resection. ............................ 6-102

Figure 31 Paired Alu 115bp values pre and post resection. ............................................. 6-102

Figure 32 Paired Line 300bp values pre and post resection. ............................................ 6-103


Figure 34 Paired Line 1 79:300 ratio values pre and post resection. ................................ 6-105

Figure 35 Paired Alu 115:247 ratio values pre and post resection. .................................. 6-106

Figure 36 Paired total cfDNA values pre and post resection. ........................................... 6-109

Figure 37 Paired mitochondrial cfDNA values pre and post resection. ............................ 6-109


Figure 39 Paired Line 1 300bp values pre and post resection. ......................................... 6-110


Figure 41 Paired Line 1 79:300 ratio values pre and post resection. ................................ 6-112

Figure 42 Paired Alu 115:247 ratio values pre and post resection. .................................. 6-112

Figure 43 Paired Line 1 79:300 ratio values pre and post resection. ................................... 124

Figure 44 Paired Line 1 300bp values pre and post resection. ............................................ 124

Figure 45. DNA quantities in a patient with progressive oesophageal cancer. ................... 126

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List of Tables.

Table 1 Hallmarks of colorectal cancers. ............................................................................ 1-26

Table 2 Hallmarks of oesophageal cancers. ........................................................................ 1-27

Table 3 Circulating tumour antigens. .................................................................................. 2-48

Table 4 Colorectal circulating DNA papers............................................................................ 55

Table 5 Oesophageal circulating DNA papers. ....................................................................... 55

Table 6 Optimization experimental layout. ........................................................................ 3-63

Table 7 Optimization experimental results for line 1 79bp primer. ................................... 3-64

Table 8 PCR amplicons. ....................................................................................................... 3-65

Table 9 Plate layout for all experiments. ............................................................................ 3-66

Table 10 PCR cycling conditions for each amplicon. ........................................................... 3-66

Table 11 Mean standard curve values for each primer. ..................................................... 3-67

Table 12 Intra-assay coefficient of variation experimental results. ................................... 3-70

Table 13 Normal population indication for endoscopy. ..................................................... 4-77

Table 14 Mean values for all markers. ................................................................................ 4-78

Table 15 Median values for all markers. ............................................................................. 4-79

Table 16 Statistical significance testing. ............................................................................. 4-79

Table 17 ROC curve characteristics. .................................................................................... 4-80

Table 18 Normal population indication for endoscopy. ..................................................... 5-88

Table 19 Mean value for all markers. ................................................................................. 5-90

Table 20 Median value for all markers. .............................................................................. 5-90

Table 21 Statistical significance testing. ............................................................................. 5-90

Table 22 ROC curve characteristics. .................................................................................... 5-91

Table 23 Paired means pre and post resection, and normal and polyps. .......................... 6-99

Table 24 Significant P values. .............................................................................................. 6-99

Table 25 Paired means pre and post resection, and control and mucosal neoplasia. ..... 6-108

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Table 26 Significant P values. ............................................................................................ 6-108

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Acknowledgements.

This project would not have been possible without the support of my wife Zoe, and the

understanding of my children Ben, Alex and Jamie.

It also took the belief and vision of Pradeep, the experience and understanding of Ian, and

the support of many who encouraged and cajoled me into finishing this. They know who

they all are.

Dissemination.

R.J.Mead, M.D.Duku, P.Bhandari, I.Cree. Circulating tumour markers can define patients

with normal colons, benign polyps, and cancers. Br J Cancer. 2011 Jul 12;105(2):239-

45.Epub 2011 Jun.

G.R.Longcroft-Wheaton, M.Duku, R.J.Mead, D.Poller, P.Bhandari. Acetic acid spray is an

effective tool for the endoscopic detection of neoplasia in patients with Barrett's

esophagus. Clin Gastroenterol Hepatol. 2010 Oct;8(10):843-7. Epub 2010 Jun 30.

Digestive diseases Federation 2012.

Circulating tumour markers can discriminate between patients with and without

oesophageal neoplasia. (Oral presentation)

British Society of Gastroenterology Meeting 2011.

Circulating tumour markers can define patients with normal colons,

benign polyps and cancers. (Oral presentation)

World Congress of Gastroenterology 2009.

Impact of EMR on the management of Barrett’s neoplasia in a UK setting. (Poster)

British Society of Gastroenterology Meeting 2007.

Endoscopic mucosal resection techniques for upper gastrointestinal neoplasms: A video

review. (Oral presentation)

Gastro-intestinal cancers in the UK.

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Chapter 1 Gastro-intestinal cancers in the UK.

1.1 Introduction.

Gastro-intestinal carcinomas are some of the most common and rapidly increasing cancers

in the United Kingdom (UK) and the western world1-3 (Figure 1). Societal costs are

considerable with emotional, structural, and financial losses.

The epithelial cells lining the bowel wall are the most usual sites affected, developing

typically into squamous or adeno-carcinomas.

Colorectal adenocarcinoma, the most common gastro-intestinal cancer and oesophageal

adenocarcinoma, the most rapidly increasing cancer in the UK, are perhaps the two most

important gastro-intestinal cancers of modern times. Twenty-three thousand six hundred

and twenty-three people die from these two conditions in the UK each year (2010)2, and

these are the focus of this thesis.

Figure 1 -Top 20 cancers by incidence in the UK 2009.


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1.2 Carcinogenesis theory.

Current theory suggests that slow accumulation of random genetic and epigenetic

aberrations, catalysed by carcinogens and inherited deoxy-ribonucleic acid (DNA) repair

enzyme defects, lead to pro-cancerous expression of key genes. Eventually this results in a

common cancer phenotype.

Inherited genetic coding, environmental factors, inflammation (driving increased cellular

replication), and the build up of errors from repeated genetic replication (ageing) effect the

speed at which cancers develop, and reflect the age related incidence.

1.2.1 Gene sequence changes.

Genes promoting and supporting cellular growth (oncogenes), genes involved in cellular

differentiation and shut down (tumour suppressors), and genes controlling cellular death

(apoptosis) are present in all cells. Pro-cancerous mutations to these key genes and/ or

epigenetic changes may produce uncontrolled proliferation signals. Similarly failure of

tumour suppression and apoptosis pathways also result in uncontrolled proliferation.

Neoplastic transformation often requires both.

Primary genetic damage usually occurs through oxidation of DNA bases and itself may lead

to failure of genetic expression. Secondary inaccurate repair of the damaged bases leads to

mutations in the genetic sequence which may or may not alter gene expression. These

changes occur throughout our lives and largely result in ageing, but at each stage neoplasia

may result.


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Increasing age and genetic damage may result in chromosomal instability (CIN). Loss of

chromosomal areas (deletion) and multiplication of others (amplification) may occur

increasing the rate of genetic change and increasing the risk of carcinoma development.

Abnormal copy numbers of complete chromosomes (aneuploidy) may be seen in advanced

neoplasia.

1.2.2 Epigenetic changes.

Changes promoting cancer are not restricted to genetic coding sequences. Epigenetic DNA

changes allow modification of gene expression without change to the coding (genetic)

region sequence. The most studied effect is at islands of relatively high Cytosine and

Guanine base content (CpG islands) close to gene promoter regions. High levels of cytosine

methylation (5-methylcytosine) in these CpG islands acts as a physiological gene silencing

mechanism. Aberrant hypermethylation, and therefore silencing, of tumour suppressor and

apoptotic genes are a frequently reported change in neoplasia progression.

Hypomethylation of CpG sites is also seen in neoplasia, potentially releasing control of

oncogenes, and by a different mechanism increasing susceptibility to DNA breaks (CIN)4.

A less well studied epigenetic phenomenon, genetic imprinting, occurs when DNA

methylation fixes genetic expression to a single predetermined parental allele. Loss of

methylation and therefore imprinting of oncogenes may allow uncontrolled expression of

the usually silent allele.

1.2.3 Extra-genetic changes.

Histone proteins act as structural scaffolding for DNA. They dictate the folding of DNA

within chromatin influencing access to and expression of genes.


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Epigenetic methylation, acetylation, and substitution of key amino acids in these proteins

alter their structure changing gene accessibility and regulation which may favour

expression of pro-cancerous genes5 (or repression of tumour suppressors).

Stromal cells and the local extracellular matrix are also extra-genetic co-factors supporting

the development of cancer6. Co-evolution of supporting cells7, and co-option of

inflammatory cells to provide further local growth stimulus8 demonstrate the range of

possible contributory factors combining to create the neoplastic environment.

1.2.4 The metaplasia, dysplasia, carcinoma sequence.

With increasing genetic damage gastro-intestinal lesions develop through common

pathological stages: Benign metaplasia (oesophageal cancer only – Barrett’s oesophagus),

pre-cancerous dysplasia; carcinoma in situ; and finally invasive cancer. A carcinogenic

process is required for neoplastic changes to develop but once invasive cancer has formed

it is self sustaining.

1.3 The Colon.

The colon is lined by columnar-type epithelium, measuring approximately 150cm long,

joining the small intestine to the anus. Its main function is to reabsorb water and control

waste excretion. Anatomically it is divided into six parts; caecum and ileo-caecal valve;

ascending colon; transverse colon; descending colon; sigmoid colon; and rectum.

1.3.1 Polyp adenoma and carcinoma pathogenesis.

A sequential morphological and pathological change from normal mucosa to adenoma with

low (LGD) then high grade dysplasia (HGD), and finally carcinoma is recognised as the most

common route for disease progression explaining 80% of cancers in the normal population9,

10.


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The proposed genetic model for this follows on from work by Fearon and Vogelstein11, with

growth of abnormal epithelial cells from the surface of the bowel mucosa into the

glandular crypts.

The initiating event is loss of the Wnt (Wingless and Int drosophila gene) pathway through

APC dysfunction, often due to mutation, followed by further genetic loss and damage to

onco- and tumour-suppressor genes eventually leading to cancer.

Three central combinations have been associated with adenomas that progress to

carcinoma: 17p loss (P53) and Kras mutation, 8q (C-MYC) and 13q (mir 17-92 cluster) gain,

and 18q (DCC, SMAD4) loss and 20q (MMP9, SRC, TNFRSF6B) gain 12. However, although

present they are not sufficient to initiate cancer, with further genetic change required.

Figure 2 Adenoma pathways to cancer.

CIMP= CpG hypermethylation phenotype, MMR = mis-match repair gene, APC = adenoma Polyposis coli, DCC= deleted in colon cancer, P53 = protein 53, MSS = microsatellite stable, MSI = microsatellite instability, SMAD = mothers against decapentaplegic homolog, hMLH1 = human mutL homolog 1, P16(INK4) = cyclin dependent kinase inhibitor 2A, PTEN = phosphatase and tensin homolog.

13


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1.3.2 Serrated adenomas and Lynch syndrome.

A subset of colorectal cancers has been characterized with deficient DNA mismatch repair

linked to mutations of repair genes such as MSH2 (MutS Homolog 2), MSH6 (MutS

Homolog 6), MLH1 (MutL Homolog 1), and PMS2 (post-meiotic segregation increased 2)14, 15.

These mutations result in high frequency microsatellite instability (H-MSI), instability in the

repeat number of usually highly conserved 1-6 bp DNA sequences present throughout the

normal genome, reflecting defective DNA mis-match repair.

Inherited defects of these genes lead to H-MSI Hereditary Non-Polyposis Colorectal Cancer

or Lynch syndrome and account for 6% of all colon cancers16.

Sporadic H-MSI is also found in 15-20% of colon cancers17, and sessile serrated adenomas

are likely precursors. High levels of BRAF mutations, an oncogene similar to K-Ras, and

aberrant DNA methylation are found in serrated adenomas and mirror the genotypical

changes seen in sporadic H-MSI cancers18.

1.4 The oesophagus.

The oesophagus is lined by stratified squamous epithelium. Measuring 25 cm in length, the

oesophagus joins the pharynx to the stomach, with an anatomical sphincter at its lower

end. Its main function is to transmit food from the pharynx to the stomach, and prevent

backwash of acid and stomach contents.

1.4.1 Barrett’s oesophagus pathogenesis.

Barrett’s oesophagus is a benign condition, involving metaplastic change of the epithelium.

However, once Barrett’s oesophagus has developed the risk of adenocarcinoma increases

by 30-150 folds. Metaplastic change is not seen in colonic neoplasia.


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Barrett’s oesophagus was first described in 195019 and involves replacement of squamous

epithelium with a specialised columnar epithelium, including intestinal-type mucosa. The

development of Barrett’s oesophagus and adenocarcinoma is considered to be driven by

reflux of acid and bile (refluxate) into the lower oesophagus. However, evidence for this is

mainly epidemiological in humans, with gastro-oesophageal reflux linked to obesity.

At a molecular level, caudal type homeobox 1 and 2 (CDX1, CDX2) expression are perhaps

the only recognised pathogenetic molecular changes in Barrett’s oesophagus. They are not

usually expressed in squamous or gastric mucosa, and are involved only in the

development of normal intestinal-type mucosa.

Conjugated bile acids and the inflammatory cytokines Tumour necrosis Factor α (TNF-α)

and Interleukin 1 β (IL-1β) have been shown to increase CDX1 mRNA expression in vitro in

colorectal cancer cell lines, but only when the CDX1 promoter is un-methylated or partially

methylated. Study of bile salt exposure in rat squamous cell lines and in metaplastic cells

from the rat OGJ have also shown increased CDX2 expression.

Although the promoter methylation status in squamous epithelium prior to Barrett’s

oesophagus formation is uncertain, these studies at a molecular level link refluxate with

increased expression of genes involved development of normal intestinal type mucosa and

possibly with development of Barrett’s oesophagus. Perhaps this is key to further

understanding the molecular pathway and explaining the well known epidemiological

associations20-22.

1.4.2 Barrett’s dysplasia and carcinoma pathogenesis.

Once Barrett’s oesophagus is established it remains unclear which changes lead to

adenocarcinoma, but inflammatory pathways are key.


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The healing of oesophagitis with acid suppression and experiments with bile salts causing

acid disassociation constant (pka) dependent mucosal injury demonstrate the role of acid

and bile salts in inflammation. More specifically Taurine-conjugated bile salts cause chronic

mucosal injury when the oesophageal reflux is acidic (pH 4); glycine-conjugated bile salts

when the pH is 4–6; and un-conjugated bile salts when the pH is neutral or alkaline23.

Clinical studies demonstrating prolonged episodes of acid reflux and higher levels of

oesophageal taurine-conjugated and un-conjugated bile salts in patients with Barrett’s

oesophagus, tie the principles together. Acid and bile as the causes of inflammation in

Barrett’s seem certain24-28.

At a molecular level the causes are less certain. Chronic acid and bile salt injury induce

reactive oxygen species and free radicals, depleting antioxidants and increasing the

expression of oxidative-stress-related genes29. Reduced levels of glutathione and vitamin C

in the metaplastic epithelium indicate that anti-oxidant defences are compromised, and

high levels of oxygen free radicals and lipid-peroxidation products confirm this29.

Primary free radical DNA damage results in genetic change which may include key cell

regulatory genes, and therefore a molecular beginning for the neoplastic process. Cells

with such mutations would usually undergo forced cell-cycle arrest or apoptosis by a P53

dependent mechanism. However, bile salts and inflammation prevent this through

proteosome mediated P53 degradation and migration inhibition factor by-pass of P53

function allowing genetic abnormalities to accumulate8, 30. Multiple other pathways are

likely to contribute. Inflammatory stimulus of prostaglandin synthesis through the

arachidonic acid pathway (Figure 3 below) is also important. Inducible cyclo-oxygenase-2

(COX-2) is found in increasing amounts through dysplasia and carcinoma31 with

prostaglandin E2, one of the main product types from the COX-2 pathway, increasing cell

proliferation32.


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Protein kinase C and P38 mitogen activated protein kinase (MAPK*) pathways are

proposed as further growth promoting mediators of the increased COX-2 products 33, 34.

Lastly experiments treating cell lines with pulsed acidified bile show an important role for

the C-myc oncogene protein, contributing to uncontrolled cellular growth35

Figure 3 COX II pathways in Barrett’s oesophagus.

* Previously known as Extra-cellular regulated kinase (ERK).


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Together these findings provide evidence for bile and acid reflux driving genetic and

molecular change, leading to the accumulation of genetically abnormal cells, with a pro-

proliferative drive able to escape the normal cell cycle controls. Cells become dysplastic

and finally cancerous.

Clinical observations demonstrate a more complex understanding is needed, with neoplasia

development despite healing of oesophagitis and also following acid suppression with a

proton pump inhibitor (PPI).

Some evidence to explain this follows from prolonged proton pump inhibitor (PPI - acid

suppressing medication) usage itself. Secondary bacterial colonization of the gullet at

neutral pH can produce inflammatory de-conjugated bile salts which continue to inflame

the gullet36, and secondary hyper-gastrinaemia acting at cholecystikinin 2 (CCK2) receptors

drives cellular proliferation37. Perhaps unsurprisingly even on a PPI up to 80% of patients

continue to reflux some acid and up to one third bile salts 38-40.

Human study substantiating the pathological mechanisms in Barrett’s oesophagus is not

available, with evidence mainly from cell line studies, animal models and case reports30, 41-43.

This may not accurately reflect human pathophysiology, and therefore molecular

confirmation for the role of acid and bile reflux in human neoplasia remains unproven.

1.5 Genetic hallmarks in colon and oesophageal adenocarcinomas.

Over the last 50 years there have been many studies of genetic change in cancers.

Observation of genetic, epigenetic, and extra-genetic changes have demonstrated multiple

pathways to cancer, however an incomplete and confusing picture has emerged. To gather

and organise this new information a conceptual framework, “hallmarks of cancer”, was

developed and published in 200044 and updated in 201145.


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This serves to provide common understanding across cancers whilst allowing work to

continue searching for further understanding of cancer pathogenesis.

These “hallmarks of cancer” are: Self-sufficiency in growth signalling; insensitivity to

growth-inhibitory (antigrowth) signalling; evasion of programmed cell death (apoptosis);

limitless replicative potential; sustained angiogenesis; and tissue invasion and metastasis44.

They serves as a useful way to review the main genetic changes found in oesophageal and

colonic cancer and these are tabulated below.

Table 1 Hallmarks of colorectal cancers.

HALLMARKS of CANCER (DNA)

Colorectal cancer

Genetic change Prevalence Reference

Self-sufficiency in growth signalling

SRC up regulation 90+% Talamonti 1993, Frame 2004.

C-Myc up regulation 66% Smith 1993, Sansom 2007.

K-Ras up regulation 37% Salbe 2000, DeReynies 2008.

Insensitivity to growth-

inhibitory (antigrowth)

signalling

P53 down regulation 90+% Veloso 2000

SMAD-4 down regulation 40% Woodford-Richens 2001

APC down regulation 90+% Sansom 2007

PTEN down regulation 20% Nassif 2004, Goel 2004.

Evasion of programmed cell

death (apoptosis)

P53 down regulation 50% Jansson 2001

DCC down regulation 50-70% Forcet 2001

mir 17-92 up-regulation 100% Diosdado 2009, He 2005

TNFRSF6B up regulation 53 - 73% Pitti 1998, Mild 2002.

SMAD 4 down regulation 40% Woodford-Richens 2001

Limitless replicative potential Telomerase up regulation 90% Engelhardt 1997.

Sustained angiogenesis

HIF up regulation 44-55% Cao 2009, Wu 2010.

VEGF up regulation 37-100% Okuno 2001, Xiong 2002

MMP 9 up regulation 63-74% Xeng 1999

TGF-β up regulation 38% Xiong 2002

FGF down regulation - Mathonnet 2006.

Tissue invasion and metastasis

E-cadherin down regulation 19 - 33% Dorudi 1993, Rosivatz 2004

MMP-9up regulation 20-75% Zeng 1999, Fan 2006.

TIMP up regulation 42 - 100% Zeng 1995, Murashighe 1996

nm 23 H(1) down regulation 62% Fan 2006.


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Table 2 Hallmarks of oesophageal cancers.

HALLMARKS of CANCER (DNA)

Oesophageal cancer

Genetic change Prevalence Reference

Self-sufficiency in growth signalling

SRC up regulation 20% Kumble 1997.

β-Catenin up regulation 60% Bailey 1998, Seery 1999.

COX II up regulation 80 - 97% Wilson 1998, Lagorce 2003.

EGFR / ERBb2 / HER2neu up regulation 10 - 70%

Kim 1997, Al-Kasspooles 1993, Hardwick 1997.

Insensitivity to growth-

inhibitory (antigrowth)

signalling

P53 down regulation 95 -100% Barrett 1999.

SMAD-4 down regulation 20 - 35% Barrett 1996, Van Dekken 2008.

P16 down regulation 12 - 100% Van Dekken 2008, Shi 2008.

Cyclin D up regulation 30 - 92% Bani-Hani 2000, Murray 2006.

Cyclin E up regulation 14% Sarbia 1999.

P27 down regulation 83% Singh 1998

Evasion of programmed cell death (apoptosis)

P53 down regulation 64 - 87% Younes 1993, Van Dekken 2008.

P16 down regulation 68 - 75% Wong1997, Klump 1998.

15 LOX-1 (13-S-HODE) down regulation - Shureiqui 2001

Fas ligand up regulation 100% Bennett 1998, Younes 1999.

Limitless replicative potential Telomerase up regulation 100% Morales 2001, Shammas 2008.

Sustained angiogenesis

HIF-1, and 2 up regulation 51 - 60% Stein 2004, Griffiths 2007.

VEGF up regulation 64 - 100% Saad 2005, Von Rahden 2005.

MMP up regulation 60- 95% Salmela 2001, Grimm 2010.

Tissue invasion and metastasis

E-cadherin down regulation 100% Bailey 1998, Washington 1998.

MMP-1, 7, 12 up regulation 60-95% Salmella 2001, Grimm 2010.

TIMP-1, &3 up regulation 53 - 73% Salmella 2001.

CD-44 up regulation 70 - 75% Castella 1996, Lagorce 1998.

β-cathepsin up regulation 73% Hughes 1998.


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Clearly some changes appear in both cancers but there is temporal heterogeneity in the

dysplasia carcinoma sequence. From a diagnostic point of view, without knowing the stage

of disease this makes the diagnostic use of specific genetic changes less attractive. Some of

the changes can also be found in “normal” patients46. It seems unlikely that a genetic

signature diagnosing early cancers or dysplasia will emerge, although patterns may suggest

risk of cancer and subtype.

1.6 Clinical diagnosis of oesophageal and colonic carcinomas.

Current clinical diagnosis of gastro-intestinal cancer is based around a clinical history,

examination, and routine blood tests. The final diagnosis is predominantly made at

endoscopy with confirmation on histological examination47-50. There are no diagnostic

blood or stool tests recommended for early or late gastro-intestinal cancers 51.

The current clinical approach is certainly effective, but early diagnosis of gastro-intestinal

cancers is unlikely as symptomatic change often occurs late in disease. Clinical symptoms

and blood tests are not specific criteria and are often found in the normal population

without cancer52, 53.

Clinically this is a well recognised problem. There are clear costs involved in investigating

normal patients, but there are also substantial outcome advantages gained from early

diagnosis54. Fortunately in the UK we have programmes screening for colorectal cancer and

providing surveillance in Barrett’s oesophagus patients for oesophageal adenocarcinoma48,

55.

1.6.1 UK Colorectal cancer screening guidelines.

Average risk, asymptomatic patients aged 60-70 years of age are offered screening with

faecal occult blood testing (fOBT) 55.


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For every 100 participants, the programme is expected to find 20 positive faecal occult

bloods, detect 2 patients with colorectal cancer and 6 patients with polyps55.

While practical and affordable, this system leaves room for improvement 56, 57, with a low

estimated point sensitivity for cancer (40.58%) , and even lower sensitivity for significant

dysplastic polyps (5.00%)55. Many cancers and pre-cancerous polyps will be missed. Gains in

sensitivity are made by offering a programme of fOBT testing, but compliance is likely to

fall over the program timescale. Initial UK compliance rates of 56.8%55 are unlikely to

improve much, as demonstrated by American studies, which have a similar patient group

and which share common health values. Worldwide some programmes only achieve 18% 58-

61 compliance, and put the UK achievement into perspective. It seems unlikely that further

gains will arise from improved compliance, and opportunities to prevent colon cancer will

be missed.

Immuno-histochemical testing for faecal occult blood (iFoBT) will offer some gains62-65 and

it is likely this will replace fOBT in the near future. The test utilises a specific antibody

reaction to human globin, labelled with a fluorescing compound. This allows more specific

and sensitive testing for faecal human blood, and with a numerical fluorescence result

allows a variable test sensitivity and specificity. In combination with other risk factors such

as age weight, sex, and smoking history a tailored risk assessment for the individual could

be produced. However, the need for faecal sampling remains, and in the longer term

investment into better screening approaches may be needed to tackle the relatively low

uptake.


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Recently a large clinical trial looking at the role of flexible sigmoidoscopy in colorectal

cancer screening has concluded 66. This demonstrated reduced incidence and mortality

from colorectal cancer, with improved sensitivity for cancer at 66%, and for polyps of 97%.

However, the sensitivity of flexible sigmoidoscopy for detecting cancer is limited to the left

side of the colon, and quality issues regarding completion of the test remain. Compliance

with the intervention is approximately 1/3 of those invited (although in smaller trials this

has been as high as 46%)67. This is undergoing national roll out as part of the bowel cancer

screening programme (BCSP).

Biomarkers from blood and urine are active areas of research looking at alternative or

additive strategies to improve the screening programme avoiding invasive testing. This

would be more acceptable for population screening, and therefore could offer a new gold

standard.

Hospital based endoscopic screening in high and moderate risk cancer groups is also part of

a national approach, advocated by specialist bowel screening guidelines 47.

1.6.2 UK Barrett’s oesophagus surveillance guidelines.

Finding Barrett’s oesophagus over 3 cm in length prompts entry into surveillance

programmes 48. This programme requires upper endoscopy every 2 years. If dysplasia is

discovered, guidelines recommend more frequent endoscopy (yearly) and the number of

biopsy samples taken increase.

This approach to “screening” presents a numbers of difficulties, not least the identification

of patients with Barrett’s oesophagus within the general population. Current practice only

identifies Barrett’s incidentally, and estimation of patients not identified for screening

suggests 20 unidentified : 1 in screening 68.


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Ongoing study of techniques to diagnose Barrett’s in high risk populations is ongoing, with

the best so far the cytosponge69.

Even when in surveillance programmes evidence of improvement over a symptomatic

diagnostic approach is limited, with many cases discovered at a late stage and often

inoperable. Outcome data is unimpressive70-77, and worldwide these programmes are areas

of ongoing discussion78. A randomised trial comparing screening to a symptomatic

diagnostic approach in Barrett’s oesophagus is currently under way in the UK (Barrett’s

oesophagus surveillance study - BOSS trial).

Identification of dysplastic change and early cancer is the main reason for the diagnostic

difficulties. Visual abnormalities are subtle and in the low prevalence surveillance

population are easily missed even in expert hands. The current gold standard of systematic

quadrantic biopsies, designed to overcome this problem, however only samples only 2% of

the total surface area and the poor diagnostic outcome is therefore perhaps unsurprising.

Research into improving the detection of abnormal areas in Barrett’s has been ongoing for

many years. In expert hands, in a high prevalence population, 95% of cases can be

identified with 2% acetic acid dye spray79. This is an effective and economical technique,

and if the population prevalence could be enriched this would be a suitable screening

technique.

1.7 Conclusions.

Oesophageal and colon cancer are important gastro-intestinal cancers, with 23,610

deaths a year caused by them.

Despite a great deal of research over many years, a diagnostic molecular picture

remains elusive, and their pathogenesis remains incompletely understood.

The current gold standard invasive diagnostic tests mainly diagnose advanced

cancers, even in screening and surveillance programmes.


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Better diagnostic testing for early and pre-cancerous neoplasia would almost

certainly result in better patient outcome.

The use of biomarkers to improve current testing for colonic and oesophageal

neoplasia, or to enrich the incidence in the Barrett’s surveillance population, is a

desirable area for clinical research.

Circulating markers of gastro-intestinal cancers.

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Chapter 2 Circulating markers of gastro-intestinal cancers.

2.1 Introduction.

Cancer biomarkers have been sought for many years with prostate specific antigen (PSA) in

prostatic adenocarcinoma a good clinical example of what can be achieved80.

The ideal diagnostic marker for cancer is well characterised as a non-invasive test81, 82 which

is socially highly acceptable, with 100% sensitivity and specificity for all stages including

pre-cancerous lesions, and correlating with stage and treatment. It must be inexpensive,

allowing population screening, utilising generally available technology, and be operator

independent.

Unfortunately, in colorectal cancer tests there are problems with social acceptability,

invasiveness, and accuracy 56, 83, and in oesophageal cancer there is no such test.

Studies of circulating DNA, RNA, proteins and circulating tumour cells have generated many

potential cancer markers and will be reviewed below, but none have found a clinical

diagnostic role to date51.

Routine sampling of blood and urine, using polymerase chain reaction (PCR) assays of

nucleic acids and enzyme-linked immuno-sorbent assays of proteins (ELISA) are already

part of accepted clinical practice84-87. Therefore new circulating DNA, ribonucleic acid (RNA)

and protein markers hold great hope for diagnostic and screening purposes, and research

in these areas may quickly advance from bench to bedside.

Circulating cell free DNA (cfDNA) was one of the earliest targets for researchers studying

cancer88 and is the most established. With established DNA protocols and extensive PCR

expertise in our laboratory, circulating cfDNA is therefore the focus of this thesis.


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2.2 Circulating DNA tumour markers – genomics.

2.2.1 Origins of circulating DNA.

Circulating DNA in the human blood stream was first described in 1948 by P. Mandel88, and

since then has been described in many forms .89-91 The quantity and pattern of circulating

DNA appears to differ in different physiological states including pregnancy92, cancers 90, 91,

93-102, benign diseases 103-105, and after trauma106, 107. Although the potential of cfDNA to

act as a diagnostic marker is clear, the origin of the DNA and the mechanisms leading to its

presence are not clear.

The most commonly cited theory is that increased and abnormal apoptosis in tissue leads

to increased circulating DNA. This theory is based around circulating cfDNA fragmentation

analysis demonstrating a “fragment ladder” phenomenon at multiples of approximately

180bp, similar to the pattern found in cells apoptosing 108, 109.

However, there are five main problems with this theory in cancer. Firstly inhibition of

apoptotic pathways is considered one of the hallmarks of cancer110 so high DNA levels in

blood don’t seem to correlate with this. Secondly, degradation of apoptotic cells generally

occurs without DNA spillage and therefore levels of cfDNA should remain largely

unchanged 111. Thirdly, DNA fragmentation patterns and quantities change as cancer

progresses, however apoptotic processes do not. Fourthly, in pregnancy circulating

maternal DNA length is increased, although there is no obvious reason for an increase in

maternal apoptosis 112, 113.

Lastly circulating DNA quantities in healthy normal patients and cancer patients can be

similar 101, 114.


2-35

A dynamic process combining necrosis and apoptosis seems most likely to explain

increased circulating DNA in cancer patients. A study of cell lines (following induced cellular

necrosis, and apoptosis) and further study in patients supports this, demonstrating

fragments > 10,000 bp as well as an “apoptotic ladder” phenomenon in both108, 115.

Circulating white blood cells and ‘free’ nucleases are increased in the blood of cancer

patients 116, 117, and it is possible that these may break larger fragments from necrotic cells

into multiple smaller pieces. These two observations potentially explain different ‘total

‘DNA levels as well as different fragmentation patterns in the cancer and normal patients.

This is an attractive and potentially comprehensive theory.

However, this is likely to be overly simplistic with findings from other studies seeming to

paint a more complicated picture. Unexplained observations of homeostatic DNA

excretion118, 119; cell to cell nucleic acid transfer120-123; cell bound circulating DNA (ccDNA) 95,

124-126; and immune and genetic modulating nucleic acids127, 128 need to be included to

complete the full picture.

Perhaps some processes engendering survival advantage in normal patients are

advantageous to cancer cells possibly helping evasion of immune regulation, and in the co-

opting of local and distant cells which aid the cancerous process.

2.2.2 Circulating cell free DNA Studies.

DNA markers in blood have been researched most frequently reflecting the clinical

familiarity with blood sampling and the interaction of blood with all cells in the body. Urine

has not been studied as much but is promising, especially in the study of urological

cancers129-134. Urine has the advantage of collecting DNA over a period of time, and

contains mainly smaller fragments, a possible source of enriched tumour DNA135-139.


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Picking between these two fluids is difficult; the use of urine is advantageous in some

respects as sampling is non-invasive, however perhaps for screening purposes social

embarrassment may impact on patient acceptability. Research in both is underway136, 140-143.

This thesis used blood because of ease of sampling patients at the time of endoscopy and

surgery.

Circulating DNA can be isolated from plasma and serum blood products. It is unclear which

of the two offers the best medium94, 115, 144-149. Although there is more cfDNA in serum than

plasma, the source and tumour specificity of the extra cfDNA is uncertain100, 108, 148. It is

currently felt that contaminating genomic leukocyte DNA may be the source150, 151. Plasma

has become increasingly accepted as a better, purer source than serum148, 150-154.

2.2.2.1 Nuclear DNA studies.

2.2.2.1.1 DNA quantification.

Across multiple cancer types and multiple genetic sequences total cfDNA quantification for

diagnostic purposes is encouraging89, 95, 155-158.

Study results in cancer patients serum show an increase in total DNA quantity, and

“apoptotic” long fragment (>180 bp) DNA115, 149. Short and long fragment ratios change in

cancer patients reflecting relatively more “apoptotic” long fragment circulating DNA, and

providing a more sensitive discriminator between normal and cancer patients.

Many studies look at prognosis as well as diagnosis, with higher levels of cfDNA generally

associated with a poorer prognosis96, 159-161.


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A single study in the oesophagus studying squamous carcinoma looked quantitatively at

cyclin D1 oncogene amplification and showed a relative increase in copy number in both

tissue and plasma, with prognostic significance162. Oesophageal adenocarcinoma has not

been studied.

In colorectal cancer a short (115 bp) Alu sequence has been used to accurately quantify

total circulating serum DNA, with a significant difference (P 0.006) between the cancer

and control group. A longer (247 bp) Alu sequence, proposed as another measure of

apoptotic DNA, was also significantly different. However, it was the ratio of long : short

fragments which performed best at discriminating cancer form normal patients with a

receiver operating characteristic curve (ROC) of 0.78 149. Alu sequences are a family of

abundant repetitive DNA elements in the human genome classified as short (<300 bp)

interspersed elements (Sine). Line or Long interspersed elements are a group of similar

genetic elements, also found in large numbers throughout the human genome. A Line 1 79

bp sequence has also been shown to be significantly elevated in colorectal cancer and a

Line 1 300 bp sequence in breast cancer serum147, 163 . Line 1 79 bp may prove to be a more

accurate measure of total DNA with the ability to measure small (<100 bp) DNA fragments

and reflecting total tumour DNA more accurately.

PCR analysis of these interspersed element markers is technically simple and economical

making them an attractive option for diagnostic testing. With the majority of studies above

looking at advanced cancers using serum, the use of plasma may provide more accurate

results and may allow testing in early and pre-cancerous disease. In particular study of

plasma cfDNA in colorectal and oesophageal cancer had not been explored and this forms

the basis for this research project using ALU and LINE 1 markers.


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Quantitative study of DNA is not standardised with results affected by purification

protocols and inflammatory disease, which confound interpretation89, 109, 115, 149, 164-176. The

careful selection of purification techniques and control groups in the analysis of early and

pre-cancerous changes is therefore crucial in any future study.

2.2.2.1.2 DNA hyper-methylation.

Diagnostic approaches looking at epigenetic methylation are reported in many cancers and

pre-cancerous conditions90, 177-179.

Early papers looking at cancers and dysplastic tissues180 showed the diagnostic possibilities

studying multiple aberrantly hyper-methylated promoter regions in colonic and

oesophageal adenocarcinomas181, 182. Difficulties translating the technology into use for

much smaller quantities of circulating DNA held back advances in this field, however the

recent and novel use of restriction enzymes 178 and better purification techniques have

begun to overcome these 183. This approach requires complex pre-PCR processing of

samples, and may make interpretation uncertain.

However, the resulting serum and plasma studies do show different patterns depending on

cancer type184 and can even discriminate between patients with colon cancer and intestinal

polyps 185 .

Transmembrane protein containing epidermal growth factor and follistatin domains (TPEF)

186, Aristaless-like Homeobox-4 (ALX4) and Septin 9 (S9)177, 179 in particular showed good

results in colonic neoplasia. S9 has undergone further validation in the only large screening

study to date, presented in abstract form only187. Disappointingly it had a 37% detection

rate for stage I early cancers, and only a 50% sensitivity including more advanced cancers.

The sensitivity for diagnosing adenomas was not reported, and leaves results in 6431/7938

study participants unexplained 187. This represents the only clinically relevant screening

study to date.


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With variation in hyper-methylated genes in normal tissue suggesting that change in the

methylation pattern is a marker of neoplasia risk rather than diagnostic of cancer

development188. Further complexity in interpreting results is added by the observations

that differing methylation patterns are found in cancer patients with the same disease, and

even differing patterns in the same patients when comparing pre-cancerous changes185.

Increasing methylation occurs with increasing age and means that control groups must be

corrected for this, further complicating interpretation.

With this level of complexity required for interpretation it seems unlikely a diagnostic

methylation pattern will be found.

2.2.2.1.3 DNA hypo-methylation.

Hypomethylation of key oncogene promoter regions may also contribute to oncogenesis,

allowing increased replication of cancer promoting proteins189. A study of circulating

lymphocyte DNA showed a significant decrease in genomic methylation in patients with

colorectal adenomas190. Gastric and head and neck cancers have been associated with

higher risk in individuals with line 1 hypo-methylation191, 192. These techniques also require

pre-PCR processing and age specific control groups, the same problems with complex

results are also likely to affect diagnostic testing role.

2.2.2.1.4 DNA Mutation.

Genetic mutation is detected in colorectal cancer and adenomas in 23-95% of patients 136,

137, 193-195, K-Ras, APC and P53 being the most common. Key mutations in cancers can also

be picked up in plasma, however to date there is no diagnostic combination available and

the only clinical role is for K-Ras directing choice of chemotherapy196.


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Similarly to other techniques, mutation analysis has its problems, with only very low copy

numbers of circulating mutant genes being present (<1% in adenomas and early colon

cancers137). The presence of detectable mutations in normal patients who do not develop

cancer on long term follow up also suggests risk rather than diagnosis and limits its role 46.

2.2.2.1.5 Loss of heterozygosity, allelic imbalance, and aneuploidy.

In patients heterozygous at key regulatory gene sites, demonstrable loss of heterozygosity

(LOH) in cancer DNA has been shown with microsatellite markers in both tissue and

circulating DNA138, 184, 197-200.

A single retrospective cfDNA study in colon cancer identified 59% of cases201 and two

studies in oesophageal adenocarcinoma identify 81-96% of cases199, 202. In keeping with

mutation and methylation, testing is likely to reflect increased risk rather than the cancer

process itself.

Single Nucleotide Polymorphisms (SNP) allowing even more accurate mapping of genetic

changes was hoped to offer a breakthrough with very large micro-array studies conducted

extensively in tissue. However, whilst the results in tissue have been impressive, there are

concerns with the feasibility of using this technique on cfDNA 203.

An initially impressive study in ovarian cancer with SNP markers showed allelic imbalance in

95% of cancer patients, and no loss of heterozygosity in the normal population. This looked

like a breakthrough, however surprisingly high DNA levels were also present in the “control”

population raising the possibility of contaminating genomic DNA. This would significantly

affect the loss of heterozygosity analysis in the normal population and call the study result

into question170. With tumour changes not always reflected in blood samples, and other

doubts over methodology and accuracy 154, 193, 204 microsatellite and SNP results must be

interpreted with caution.


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Fluorescence In Situ Hybridization (FISH) techniques looking at larger chromosomal

amplification and deletion have shown excellent results in tissue. Results in an oesophageal

study identified HGD and adenocarcinoma with 84% sensitivity and 93% specificity 205.

Circulating markers have yet to be studied because of technical difficulties with DNA

quantity, and the requirement for tumour DNA specificity.

Similarly flow cytometric studies of aneuploidy in Barrett’s oesophagus and oesophageal

adenocarcinoma tissue have shown excellent results, and represent the single best marker

of disease206, 207. However, no study to date has identified these changes in blood or urine,

and it is difficult to see how this would be possible.

2.2.2.1.6 DNA sequencing.

Sequencing offers the most accurate assessment of circulating DNA, with information on

fragment length, copy number, and gene sequence and mutations. Until recently selection

and gene enrichment were required to analyse particular genomic sequences, but new

platforms are able to sequence all circulating DNA with excellent results. This technique

offers the potential to analyse all of the above markers at the same time.

A new study in breast cancer sequenced all DNA in normal and neoplastic groups, with

validation in a second set. The results were startling with a ROC of 0.92 for stage I disease,

and 70% sensitivity with 100% specificity208. Interestingly both Line and Alu DNA elements

were significant, with Line being more significant. Further work to prove a clinical role is

required because of the complex statistics and use of whole genomic pre-amplification,

both of which may produce undetectable biases 209, 210. Repeat analysis by a different

technique such as PCR, against a clinically relevant non-breast cancer group, could confirm

its significance.


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2.2.2.2 Mitochondrial DNA studies.

Mitochondrial DNA has been studied less frequently and never in oesophageal and

colorectal cancer. Results are mixed with some studies showing decreased levels

(MATPase8, ND1)173, 211, and others increased212. Mitochondria lack intrinsic DNA repair

enzymes and are therefore sensitive to oxidative damage, especially important in

oesophageal adenocarcinoma pathogenesis. Quantitative mitochondrial DNA testing may

therefore complement nuclear DNA quantification.

Levels are also raised in traumatic conditions and probably inflammatory conditions, which

are likely to confound previous studies106. The choice of patient and control groups is once

again important.

With no previous study of mitochondrial markers in oesophageal and colorectal cancers the

oxidation sensitive mitochondrial ND1 sequence was chosen to complement study of

nuclear DNA markers in this thesis211.

2.3 Circulating RNA tumour markers – transcriptomics.

The origin of circulating cell free RNA (cfRNA) is uncertain but it is likely to arise from

similar processes suggested for cfDNA.

Although damage to DNA is a key step in carcinogenesis not all change leads to cancer. RNA

levels reflecting cellular and genomic activity was hoped to identify the cancer phenotype

bridging the diagnostic gap. Unfortunately, normal levels of RNA expression may only differ

slightly from that seen in cancer cases, and other diseases, making quantification difficult.

With cfRNA levels in the blood even lower, analysis of circulating RNA is challenging.

Furthermore optimal purification and processing techniques have not been agreed, and

interpreting negative studies is therefore difficult. RNA results will require careful

validation in large populations.


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2.3.1 RNA marker studies.

Multiple studies using micro-array gene expression analysis in tissue have identified a wide

range of differing genes which may be helpful, but unfortunately without a consistent

picture. A meta-analysis has compared the results across 25 studies and picked out the

more consistent markers which may be helpful in future studies213.

In colorectal cancer a micro-array and RT-PCR study identified a 5 gene marker set using

circulating white blood cell RNA214. Test characteristics in a validating set identified 88% of

cancers correctly. However, only 67% of the normal group were identified, half of the

cancer patients had advanced disease, and an indeterminate group still remained after

analysis. Improvements to the assay will be needed before further clinical evaluation.

A second micro-array and RT-PCR study used a panel of 7 biomarkers and also tested on

circulating white blood cell RNA samples. This was found to have a sensitivity of 72%, and

specificity of 70%215. Most of the subjects (60%) had early cancers, and the control group

had undergone normal colonoscopy making this one of the best and most clinically relevant

colon studies to date. Further testing in a larger screening population is needed.

Telomerase mRNA shows potential, with two small studies in colorectal cancer showing at

best 82% sensitivity at 100% specificity, although against healthy controls216, 217. Further

evidence of potential is shown in a small clinically relevant hepatocellular carcinoma (HCC)

study including chronic liver disease controls, showing 77% sensitivity and 97%

specificity218. All three studies require validation.

Plasma cytokeratin 19 (CK19) a marker for epithelial cells and CEA RNA analysis in cancer

patients showed a sensitivity of 83%,and specificity of 76%219. However, other groups have

not found similar results220 and the study identified advanced cancers from healthy controls,

suggesting these figures may fall in a clinically relevant validating population.


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Most recently in abstract form only, an RNA multi-gene signature identified colon cancer

and adenomas in a validating set analysis using RT-PCR with 82-85% sensitivity221.

Unfortunately there is no comment on specificity or stage of disease studied but this may

be useful in future diagnostic work.

In oesophageal cancer there are 3 studies. Circulating tumour cell survivin mRNA (an anti-

apoptosis gene) has been studied in a small and retrospective study looking at response to

chemo-radio therapy in oesophageal squamous and adenocarcinoma. There was only

significance in non-responders, and RNA was only found in advanced disease 222. More

promising results in bladder cancer with urine sampling showed a difference between

normal and bladder cancer patients that increased with grade of tumour. This finding

requires further validation, but shows some potential for future research134, 223.

In oesophageal squamous cancer, plasma carcinoembryonic antigen (CEA) and squamous

cell carcinoma (SCC) antigen mRNA (markers of circulating tumour cells) 224, showed a

prognostic significance when either SCC or CEA was detected. However, these were only

present in 13% of patients, (8% CEA antigen and 4% SCC) and reflect advanced disease225.

These are unlikely to represent diagnostic targets.

2.4 Circulating protein tumour markers- proteomics.

2.4.1 Origins of circulating proteins.

The presence of proteins in plasma is normal, and consists mainly of albumin (55%). Other

classical plasma proteins include coagulation factors, immunoglobulins, peptide hormones,

cytokines, and paracrine messengers.


2-45

Abnormal plasma proteins may be derived from cellular death and damage leading to

release of non-plasma proteins which can be highly specific e.g. troponin I in cases of

myocardial infarction. Abnormal secretion patterns in tumour cells may also be a source of

non-plasma proteins, which whilst being much less controversial than the idea of DNA

secretion 226 , have still not currently been identified.

2.4.2 Circulating protein studies

Approximately 289 plasma proteins have been detected, covering concentrations 10 orders

of magnitude different. This number is expected to increase and does not reflect all the

post translational modifications and precursor variations of each protein including

immunoglobulins with 10,000,000 different sequences.

Whilst the complexity of the plasma proteomics is apparent, the study of circulating

proteins also offers great possibilities 226.

Proteins as the functional products of DNA and RNA are involved in all cell growth and

change. They also offer great promise in bridging the diagnostic gap between DNA coding

and cancer phenotype. However, like DNA and RNA there are problems to overcome.

Protein components of the cellular cancer process can be very subtlety different to ‘normal’

cellular proteins quantitatively and structurally. These changes can affect function. There

are no sensitive and specific assays available at present to detect these subtly different

proteins.


2-46

Detection of circulating abnormal proteins can be challenging. Their usual position is

primarily within the intra-cellular compartment, and circulating levels are therefore much

lower than other normal circulating proteins. Changing levels of acute phase inflammatory

proteins, clotting proteins and variation with meals and in other disease states further

confound analysis227. Advanced techniques are required to obtain relevant proteins at

suitable concentrations, which may require prior digestion, or depletion and fractioning.

These approaches are not standardised, and generation of a protein assay can be laborious

with small changes difficult to account for. Identifying possible candidate proteins has been

sped up by using high throughput techniques such as matrix-assisted laser desorption

ionization time of flight (MALDI-TOF), surface enhanced laser desorption ionization time of

flight (SELDI-TOF), and mass spectroscopy (MS).

These offer the possibility to screen very complex proteins mixtures such as urine and

plasma, identifying individual proteins and protein signatures within them228. Statistical

analysis of such data is challenging and can be misleading229, with differing signatures

found in the same cancers, and variability between samples not uncommon230-233. Further

ELISA testing is generally required to validate and test individual markers234, 235.

2.4.2.1 Protein signature studies

An impressive study in colorectal cancer patient’s sera identified six peaks using SELDI-TOF,

with subsequent development of a validating ELISA assay in a second blinded population.

Complement C3a-desArg was the best performing marker in both cancers and adenomas

with approximate sensitivity and specificity of 96%236. However, the final diagnostic results

reflected a partial training set and classification did not include lesions that were

indeterminate. Furthermore the study control group only consisted of healthy volunteers.


2-47

A second study initially employing mass spectrometry identified five proteins for antibody

development and further testing in serum. Nicotinamide N-methyltransferase and

proteasome activator complex subunit 3, were the best performing markers but only just

improved on CEA237. Comparison between CEA and another putative protein marker

Macrophage migration inhibitory factor(MMIF) 238 using ELISA also showed little

improvement.

There are no oesophageal cancer studies.

2.4.2.2 Circulating Tumour antigen studies

Multiple circulating tumour antigens have been developed including those in current

clinical practice, CEA, CA 19-9, and CA-125.

Originally they were developed by reacting cancer patients sera with mouse B-lymphocytes,

and then harvesting the antibodies to test against other cancer patients sera.

Over time further reactive antigens have been identified and sequenced, although no

tumour antigens have been identified with any clinical diagnostic role. They represent the

oldest239 and largest group studied (see Table 3).


2-48

Table 3 Circulating tumour antigens.

Name Properties and research roles CEA CEA can increase with various GI cancers, allowing follow up and assessment of response to

chemotherapy240. Studies use combination or comparison to show improved clinical utility.241-243 CA 19-9 CA 19-9 is a glyco-lipid often raised in pancreatic cancer. Unfortunately it is also expressed in a range

of other cancers, cystic fibrosis and liver disease244, 245. It can be used to follow up patients and assess treatment response51.

CA 50 (sialylated Lewis (a) antigen) CA 50 is an alternative antigen epitope of CA19-9. It is no better and it’s behaviour and use reflects this245.

CA 242 (sialylated Lewis (a) antigen) CA 242 is the final antigen in a triplet against sialylated Lewis (a) antigen, neither is better than the other, but perhaps in combination they add some extra sensitivity in detection of pancreatic cancer.246

CA 125 (Carbohydrate antigen 125, MUC 16)

Ca 125 (MUC16) is a protein associated with ovarian cancer but may be raised in pancreatic and colorectal cancers, with other benign conditions. It has follow up, and treatment response roles247.

CA 72-4 (Tumour Associated Glycoprotein TAG-72)

Ca-72-4 is a tumour associated glycoprotein, with limited specificity for gastric cancer248.

CA 15-3 (MUC 1) Ca15-3 is a glycoprotein with some specificity for breast cancer at an advanced stage249.

CA 27.29 (MUC 1) CA 27.29 is also an epitope of MUC-1. Elevated CA 27.29 levels are primarily associated with metastatic breast cancer and may be useful for predicting recurrent breast cancer. CA 27.29 levels are not useful for screening or diagnosis of malignant disorders250, 251.

CA 11-19 The EDP Biotech company reports that this protein in a test (Colomarker) is able to pick 99% of patients with colorectal cancer from the normal population. There are no published trials of this marker in pubmed.

Chapter 2 – Circulating markers in gastro-intestinal cancers.

2-49

2.4.2.3 Autologous T-cell activating antigen studies

Autologous T-cell activating class I MHC antigens are a new avenue for investigation with new

markers isolated after investigation into melanomas, oesophageal cancer, astrocytoma, and Hodgkin

lymphoma; NY-ESO-1; MAGE; GAGE; BAGE; tyrosinase; SSX2; TRP-I; CDK4; and ı-catenin.

Tissue expression studies in colon252 and oesophageal cancers253 look promising, but studies of

circulating markers are mostly still awaited.

A study looking at a single patient with circulating antibodies to T cell derived antigen NY-ESO-1 in

metastatic adenocarcinoma found higher titres associated with progressive disease254.

In advanced oesophageal cancer serum NY-ESO-Y1antigen identified only 30% of cases but in

combination with a FAS ligand antibody identified 89% of tumours with 100% specificity255. With

poor availability of any markers in oesophageal cancer this may warrant further study.

2.4.2.4 Auto-antibody studies

Detection of serum antibodies to tumour antigens in a reverse ELISA may provide useful circulating

serum markers for cancer diagnosis and prognosis256. These markers are of course the opposite of

protein antigens above, and testing consists of ELISA with bound peptides selectively binding auto-

antibodies from cancer patient’s sera. Secondary analysis allows identification of the individual

antigens.

In colorectal cancer a combination of five auto-antibodies picked by screening peptides produced

from cancer complementary DNA (cDNA) libraries, show a diagnostic sensitivity of 58%, and

specificity of 92.4%. Addition of CEA analysis to these further increases the sensitivity to 77.6%, a

good clinical test. However, the comparator group were healthy volunteers and no validation

exercise has been performed257.

Breast and lung cancer studies have shown more promising results258, 259, perhaps demonstrating the

difficulties in diagnosing colorectal cancer.


2-50

In a single study of Barrett’s oesophagus, oesophageal squamous and adenocarcinomas, anti-P53

antibodies were detected in plasma. However, the diagnostic yield at 11% in Barrett’s patients, and

20% in oesophageal adenocarcinoma was very low and is unlikely to help diagnostically 260.

2.4.2.5 Miscellaneous protein studies

2.4.2.5.1 MMP - matrix metalloproteinases

A model to detect colorectal cancers and significant polyps, using gender, smoking history, pain, and

age adjusted ELISA MMP9 levels demonstrated a sensitivity of 78%, and specificity of 77% 261.

However, 45% of patients had advanced disease and the control group included 46 healthy

volunteers. These proteins catalyse the dissolution of the extracellular matrix, and perhaps identify

more advanced aggressive disease moving towards an invasion phenotype. They therefore may not

be the best marker for early disease.

MMP7 has been studied in colorectal cancer, but there was only a statistically significant change in

advanced cancers, suggesting it is less suitable for diagnostic work.262

MMP2 has been studied with CEA but has lower serum sensitivity (11.1% vs 27.8% in Dukes A).

Similar to CEA it is useful if high in the primary tumour, associated with worse prognosis and higher

recurrence263-265. Together the two markers were more sensitive, (28-33%) but not significantly

better than current markers264, 266.

2.4.2.5.2 TIMP 1 (Tissue inhibitor of metalloproteinases)

Impressive results with ELISA kits in a large number of patients show promise in early cancer

diagnosis, with 52% sensitivity at 98% specificity 267 and even higher sensitivity in early cancers (56%

in Dukes A and B268). A pre-operative study showed correlation with stage and outcomes, suggesting

a role in post-operative decision making265. However, a lack of positive findings in adenomas, and a

correlation with age269 may limit its usefulness as a screening tool for colorectal cancer prevention.


2-51

2.4.2.5.3 Tissue Polypeptide antigen - cytokeratins 8, 18 and 19

This is an antibody to epithelial cytokeratin fragments 8, 18, and 19. It has been trialled as a

circulating marker for colorectal cancer but the results have been mixed with positive and negative

results 270, 271.

2.4.2.5.4 Specific Tissue Polypeptide antigen – cytokeratin 18

This is an antibody to epithelial cytokeratin fragment 18. A trial using it as a circulating marker for

colorectal cancer met with little success271 . Another study in lung cancer also demonstrated a low

sensitivity (40%) and use as a diagnostic marker is therefore unlikely272.

2.4.2.5.5 CYFRA 21-1 – cytokeratin 19

A study in oesophageal squamous and adenocarcinoma showed a low sensitivity and specificity273.

Further study of cytokeratins in gastrointestinal cancers is unlikely to add to diagnostic testing, at

least with ELISA.

2.4.2.5.6 Nuclear matrix protein antigens

Nuclear matrix protein markers in cancer patients serum and urine have been described since

1992274, 275, with an FDA approved diagnostic urinary test in bladder cancer276. Colon cancer specific

proteins 2, 3, and 4 are new nuclear matrix markers initially picked using mass spectrometry analysis

and re-analysed with an ELISA against normal, adenomatous, and colon cancer patients serum277-279.

Control groups were healthy volunteers, patients with hyperplastic polyps, and non-advanced

adenomas. Testing for “significant” polyps and colon cancer patients showed the test appears

sensitive with excellent Receiver Operator Characteristics (area under curve 0.82-0.90). This will

require further evaluation in a larger study.

Nucleosomes which consist of nuclear matrix proteins and associated DNA are unfortunately less

sensitive than cfDNA 280, and so further analysis is unlikely to improve diagnostic testing.


2-52

2.4.2.5.7 VEGF – vascular endothelial growth factor

Studies looking at serum VEGF levels have shown significant differences between cancer and normal

patients281, 282, however in a small study comparing this with circulating DNA, it was less useful.283

This would seem to preclude a diagnostic role for serum VEGF levels, particularly with bigger more

advanced tumours requiring enhanced vascular supply.

2.4.2.5.8 CD 44

CD44 is a transmembrane glycoprotein family with multiple roles, including cell to cell interactions.

The exact role depends on the particular isoform. It has been suggested as a marker of breast and

prostate cancer stem cells284, and changes in cd44 have been associated with tumour metastasis285.

A clinical trial in different intestinal diseases and cancer patients found significant differences from

normal patients286. However, when 3 antibodies were used to detect early colon cancer only 25-50%

of patients were identified, and only 63% of patients with non-metastatic gastric cancer were

identified. It is therefore unlikely to play a major role in diagnostic testing of cancer in the gastro-

intestinal tract. 287.

2.4.2.5.9 Kallikrein

Kallikreins are a subgroup of serine proteases which cleave proteins. Within cancer tissue Kallikrein

hK3 has a very well recognised role as a cancer biomarker, but by an alternative name, PSA.

Physiologically hK1 releases bradykinin from kininogen, and plasmin from plasminogen. The role in

enzyme pathways for other members of this family is not known however they play a prognostic role

in other types of cancer, including colorectal cancer, in both tissue and serum288-292. There have been

no diagnostic studies to date.


2-53

2.4.2.5.10 Laminin

In colorectal cancer a serum ELISA assay for laminin showed a sensitivity of 89% and a specificity of

88%. Nearly half of the patients had advanced disease, including metastasis and the control group

were healthy controls. Further studies are needed293.

2.4.2.5.11 βHCG - beta human chorionic gonadotrophin

This was trialled as a circulating marker for colorectal cancer with little success271.

2.5 Circulating tumour cells.

Circulating tumour cells offer hope acting as diagnostic, prognostic and therapeutic markers. They

always signify cancer when detected, but are found mainly in late/ advanced disease and in small

quantities, representing a metastatic phase.

They may play an important role in therapeutics and individualised chemotherapy regimens 294-296,

but their use prognostically shows mixed results 219, 297-300. As they generally detect cancer at a late

stage they are unlikely to play a diagnostic role with late disease generally symptomatic and

accurately diagnosed within currently available practice 301, 302.

Alternative techniques using analysis of cytokeratin mRNA in purified blood220, 224, 303 can detect the

presence of circulating cells at an earlier stage. Unfortunately as RNA and not the cell itself some of

the advantages are lost and the later stage of disease remains difficult to surmount diagnostically.

2.6 Circulating DNA markers in colonic and oesophageal adenocarcinoma.

Worldwide there are 27 papers looking at the diagnostic and prognostic application of cfDNA

markers in colonic and oesophageal adenocarcinoma. All are nuclear DNA studies. The papers

report quantitative DNA fragmentation changes, tumour suppressor gene promoter

hypermethylation, patient lymphocyte DNA tumour suppressor gene promoter hypermethylation 304,

oncogene mutations, and loss of heterozygosity at tumour suppressor genes.


2-54

The main studies in colonic cancer are tabulated in Table 4 and consist of 22 papers in English, 1

paper in Japanese (English abstract, Hibi et.al.305), and 1 in abstract form only (Church et.al.187).

There are 3 papers looking at oesophageal adenocarcinoma, these are tabulated in Table 5.

Most studies involve patients with advanced cancer (stages III-IV), commonly representing half of

the study population. These may not represent patients with curable disease.

There are only 5 studies looking to identify colonic adenomas136, 137, 140, 187, and no studies look to

identify oesophageal dysplasia.

There are only 4 papers with clinically relevant control groups137, 187, 304, 306. Eight papers don’t report

findings in control groups, and fifteen use ‘healthy volunteers’, which may not represent a good

control population. There are no studies of oesophageal adenocarcinoma using clinically relevant

control groups such as patients with risk factors for cancer or other diseases.

These markers hold great promise for future study, but none are used clinically yet, and most don’t

identify early disease.


55

Table 4 Colorectal circulating DNA papers.

* = only tested in positive tissue patients. I-IV = staging grade of cancer, early (I) to advanced(IV).

Table 5 Oesophageal circulating DNA papers.

Author Year DNA marker Serum / plasma / urine Control group (n=) Cancer / adenoma (n=) ValidationPrognostic / diagnostic /

therapeuticSensitivity Specificity

Hibi 1998 LOH at 8 microsatellite markers serum N/A I-IV cancers (44) N diagnostic 0% XXX

Anker 1997 K-ras mutation plasma healthy volunteer (6) adv cancer (14) N diagnostic 43% XXX

De Kok 1997 K-ras mutation serum N/A adv cancer (14) N diagnostoc 43% XXX

Kopreski 1997 K-ras mutation serum or plasma healthy volunteer (28) adv cancer (31) N diagnostic 39% XXX

Ryan 2003 K-ras mutation serum N/A I-IV cancers (78) N prognostic 41% XXX

Su 2005 K-ras mutation urine N/A cancer/adenoma (20) N diagnostic 83%* XXX

su 2008 K-ras mutation urine N/A cancer/adenoma (20) N diagnostic 95%* XXX

Hibi 1998 P53 and K-ras mutation serum N/A I-IV cancers (44) N diagnostic 23% XXX

Wang 2004 APC, TP53, K-ras mutation serum healthy volunteer I-IV cancers (104) N diagnsotic 35% XXX

Diehl 2005 APC mutation plasma age matched volunteer (10) I-IV cancer (22) (and polyps(11)) N diagnostic 72% *(39%) XXX

Diehl 2008 APC, TP53, PIK3CA mutation plasma N/A I-IV cancers (25) N diagnostic 50% XXX

Frattinin 2008 K-ras mutation, P16 methylation. plasma healthy volunteers (20) I-IV cancers (70) N diagnostic 25% XXX

Lecomte 2003 K-ras mutation, P16 methylation plasma N/A I-IV cancers (58) N prognostic 64% XXX

Ally 2005 oestrogen receptor methylation leukocytes disease free control group (57) cancer (57) N diagnostic non significant non significant

Grutzman 2008 Sept9 methylation plasma healthy volunteer (183) I-IV cancer (126) Y diagnostic 72% 90%

De Vos 2009 Sept9 methylation plasma healthy control (97) I-IV cancer (172) Y diagnostic 56% 95%

Church 2010 Sept9 methylation plasma ave risk group (930) I-IV cancer and polyps (53) Y diagnostic 50% 91%

Tanzer 2010 Sept9 and ALX4 methylation plasma health volunteer (22) cancer/adenoma (54) Y diagnostic 40% 95%

Holdenrieder 2001 Nucleosome DNA quantification serum healthy and benign disease (172) various adv cancer (418) N therapeutic N/A N/A

Thijssen 2002 pico green DNA quantification serum or plasma age matched volunteer (28) adv cancer (26) N prognostic XXX XXX

Umetani 2006 Alu 115 +247 DNA quantification and ratio serum healthy volunteer (51) adv cancer (136) N diagnostic 63% 92%

Pucciarelli 2009 Alu 115 +247 DNA quantification plasma healthy volunteer (55) I-IV cancer (136) N diagnostic 79% 86%

Flamini 2006 G-3-PD DNA quantification plasma healthy volunteer (75) I-IV cancer (75) N diagnostic 81% 73%

Boni 2007 Rnase P DNA quantification plasma healthy volunteer (67) cancer (67) N diagnostic 100% XXX

Author Year DNA marker Serum / plasma / urine Control group (n=) Cancer / adenoma (n=) ValidationPrognostic / diagnostic /

therapeuticSensitivity Specificity

Kawakami 2000 APC methylation plasma healthy control (20) I-IV cancer (52) N prognostic 25% 100%

Eisenberger 2006 LOH at 12 microsattelite markers serum healthy volunteer (10) cancer (32) N diagnostic 81% XXX

Banki 2008 β-actin plasma healthy volunteer (44) recurrent cancer (42) N prognostic 100% XXX

56

2.7 Conclusions.

The characteristics of ‘ideal’ diagnostic markers for the early detection of cancer

have been well described, and circulating markers offer many of these features.

A number of research studies into circulating DNA, RNA, and proteins have identified

potential markers which may be useful diagnostically and prognostically. However,

none have been proven clinically suitable to date.

DNA, with well established sensitive analytical techniques offers a pragmatic area

for further research, with the possibility of rapid, inexpensive translation into clinical

practice.

Alu, Line and mitochondrial DNA quantification in plasma offers the opportunity for

an economical and sensitive diagnostic assay for both oesophageal and colorectal

cancers.

2-57

Experimental Hypothesis and Aims

Hypothesis

This thesis examines the hypothesis that plasma from patients with pre-cancerous, early cancer and

advanced colorectal and oesophago-gastric cancers contains increasing quantities and changing

fragmentation patterns of cfDNA, and that this may have a diagnostic application as part of simple

blood test.

This thesis also examines the hypothesis that plasma cfDNA levels and fragmentation patterns from

patients with pre-cancerous, and early colorectal and oesophago-gastric cancers return to normal

after endoscopic resection.

Aims:

1. To develop assays to determine the quantities of nuclear and mitochondrial cfDNA.

2. To develop assays to determine the fragmentation patterns of nuclear and mitochondrial

cfDNA.

3. To examine the use of nuclear and mitochondrial cfDNA quantities as a potential diagnostic

test in pre-cancerous, and early colorectal and oesophago-gastric cancers.

4. To examine the use of fragmentation patterns in nuclear and mitochondrial cfDNA as a

potential diagnostic test in pre-cancerous, and early colorectal and oesophago-gastric

cancers.

5. To examine the effect of combining cfDNA with carcinoembryonic antigen as a potential

diagnostic test in pre-cancerous, and early colorectal cancers.

3-58

Chapter 3 Methods.

3.1 Introduction.

Previous studies using serum with Alu and Line 1 markers, purified cfDNA fragments >100bp long

and showed significant discrimination between healthy volunteers and cancer patients.

This research project utilised plasma to quantify cfDNA fragments >20bp with Alu, Line 1, and

mitochondrial markers.

Plasma processing however requires further technical choices with no accepted standard for DNA

sampling, processing time, separation, or measurement 152, 154, 307. The available techniques are

discussed below with the reasons for the subsequent protocols described. The full laboratory

protocols are documented at the end of the chapter.

3.1.1 Plasma processing.

Studies show changes in DNA levels and fragmentation when plasma and serum are left for greater

than 2-4 hours before processing. Processing within this period is recommended153, 308. No

differences in quantity or integrity were seen between samples stored at 4°C or room temperature

within this period.

A double centrifuge protocol separating plasma from whole blood has shown improved

reproducibility. Additional cell separation techniques such as histopaque and filtration are probably

not helpful153, 309, 310. Our isolation protocol processed samples within 4 h, utilising a triple spin

technique with histopaque separation. This followed our standardised laboratory protocol, chosen

to reduce cellular contamination, and pre-dating the above research.

3.1.2 DNA isolation and purification.

Common DNA isolation techniques include: binding of DNA to silica membranes followed by alcohol

washing; phenol and sodium acetate washing and precipitation with cold ethanol or iso-propanol

followed by spinning down and drying.

3-59

Both methods re-suspend DNA with addition of an aqueous solution. A variation on the silica

membrane technique is to use microscopic silica coated magnetic beads e.g. Dynabeads. These are

mixed with the DNA and body fluid solution outside of a magnetic field and then separated from the

suspensions by attraction of the beads to a magnetic field. The supernatant fluid minus DNA and

beads is removed by pipette. The technique uses the same chaotropic salts and ethanol washes, but

is quicker, much easier to mechanise and can be up-scaled for larger use. It does not use centrifuges

or filters.

Most papers in the medical literature use silica membrane spin column techniques 108, 139, 147, 154, 167.

They offer highly standardized tissue specific procedures, quick and easy use, few toxic or harsh

chemicals, minimise contaminating inhibitors, and are used in NHS laboratories311-313. DNA isolation

relies on the formation of hydrogen bonds between the DNA and the silica membranes in the

presence of high concentrations of chaotropic salts, and relative dehydration. The DNA containing

solution is spun through silica membrane columns which bind the DNA when the conditions above

are in place. The chaotropic salts are then removed with an alcohol-based wash and spin. The DNA is

finally eluted after addition of a low-ionic-strength aqueous solution such as TE (10mM Tris-HCl, 0.1

mM EDTA) buffer or water with a final spin down into a collecting tube.

Newer columns have been developed for purifying small DNA fragments (<100 bp) thought to

contain more tumour DNA. Plasma-XS spin columns (Macherey-Nagel) economically purify

fragments >20 bp long, and are used in this study.

3.1.2.1 High Yield Plasma-XS nucleospin modification.

The spin column manufacturer recommends that up to740 µl of plasma can be loaded onto the spin-

column without compromising quantification. With our low concentration samples, it was desirable

to process larger volumes of plasma. A preliminary study showed that up to 960 μL of our plasma (2x

480 μL aliquots with 2 x 720 μL of binding buffer, loaded in 4 x 600 μL aliquots) can be loaded

without losing DNA (Figure 4).

3-60

Figure 4 Modified DNA loading results.

3.1.3 DNA quantification.

Quantitative study of purified cfDNA has been measured in three main ways: absorbance (Ultra

Violet (UV) spectrophotometry)314; fluorescent DNA binding dyes (e.g. ethidium bromide, Pico

green)306; and quantitative PCR reaction101, 149, 315.

UV spectrophotometry is the easiest and most common technique for measuring DNA generally.

DNA and most of the common contaminants found in DNA preparations absorb UV light and

measurements between the 220 nm and 320nm region and are used to determine concentration

and assess purity. Measurement at 260 nm quantifies DNA; 230 nm guanidium salts and phenol

(process contaminants); 280 nm tyrosine and tryptophan (used as an indicator of protein

contamination); and 320 nm turbidity of the sample which is normally subtracted after a background

reading. These separate absorbance readings allow measurement of the DNA concentration and

provide information about the contaminant levels.

0 0.005

0.01 0.015

0.02 0.025

0.03 0.035

0.04 0.045

Sam

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1 2 3 4 5 6 7 8 9 10

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Modified protocol - total DNA yield

3-61

Absorbance value at 260 nm (A260) is used to calculate DNA concentration with an equation derived

from Beer’s Law:

Concentration (µg/mL) = (A260 reading – A320 reading) x dilution factor x 50 µg/ml.

A260 of 1.0 = 50 µg/mL pure DNA.

Proteins and guanidium salts do contribute to absorbance at A260, therefore if they are present at

high levels in low concentration DNA preparations they will lead to an overestimation of the DNA

concentration312. A good quality DNA sample should have an A260/A280 ratio of 1.7-2.0 and an

A260/A230 ratio of greater than 1.5.

The nanodrop spectrophotometer 1000 (Thermoscientific) is accurate (± 10-15%) when samples are

greater than 10 ng/μL, and is unsuitable for the purified DNA samples we are using at <3 ng/μL.

There is also no information about relative quantities of specific genes or fragmentation patterns

offering a potentially more sensitive test. Therefore this method was not chosen for the main study.

Quantitative fluorescent DNA dyes e.g. ethidium bromide, fluoresce markedly when bound to

double stranded DNA. Isolated DNA is usually loaded onto an agarose gel and separated according to

size and charge by exposure to an electric field. The negative charge associated with the DNA

backbone causes migration towards the anode. The dye is then applied to the gel and the DNA

concentration is calculated by a fluorometer comparing fluorescent intensity to that of a known

concentration and molecular weight marker band. This technique can provide information on

specific genes, fragment size and DNA quantity, however DNA content less than 10 ng is very difficult

to measure on agarose gels. Therefore this technique is also not suitable for our purposes.

3-62

Pico Green is a newer double-stranded DNA binding fluorescent dye. It works in solution comparing

the unknown DNA to a prepared standard curve of appropriate DNA and is more sensitive, able to

detect pg of DNA. However, 200 bp fragments are required for successful quantitation316; genomic

and fragment DNA each require their own standard curves; there is no information about relative

quantities of specific genes; and fragmentation pattern analysis is not possible. This technique is also

not suitable for our purposes.

The last and most recent technique uses quantitative real time PCR (qPCR) a development from

standard PCR. It allows the measurement of the target sequence PCR product as it replicates by the

generation of a DNA bound fluorescent signal. The fluorescence signal increases with each

amplification cycle. The PCR cycle number at which the exponentially rising fluorescence first

increases significantly above the background, is called the threshold cycle (Ct). This provides an

inverse quantitative relationship to the original DNA concentration of the amplicon.

Absolute qPCR for DNA quantitation is well established in the forensic science community, as well as

the medical research community317. Research has shown it to be highly accurate and reproducible

with blood and plasma, even at low picogram DNA concentrations149, 318. Absolute qPCR also relies

on a standard curve methodology, but is not reliant on similar fragmentation size.

With the use of differing size amplicons either side of the “apoptotic fragment” size e.g. 79 bp and

300 bp, this technique allows highly accurate and reliable quantitative and fragment analysis of both

nuclear and mitochondrial DNA. This technique was chosen for our analysis and has previously been

used with Alu, Line and mitochondrial markers.

Whichever methods are chosen caution must be used when comparing yields. Contaminants,

accuracy, and reaction efficiency differences may cause variable measurements and results.

3-63

3.1.4 Absolute qPCR.

3.1.4.1 Primer selection and optimization.

A sufficient magnesium (Mg2+) concentration is an essential component required for DNA

polymerase function, however too much Mg2+ can cause non-specific amplification. Primer

concentration must also be at a level that allows optimal efficiency of the reaction, but not so high

that non-specific annealing and primer-primer interactions occur (primer-dimers). It is therefore

essential to optimise these two factors specifically for each target.

The primers were tested for optimal conditions initially with 3 master mixes, mix A std

manufacturers double strength master mix, mix B = + 1 mM MgCl2, and mix C = + 2 mM MgCl2.

Forward and reverse primers were tested at concentrations of 100 - 400 mM in a grid formation. A

positive control to confirm PCR reaction, and a 400/400 mM concentration negative control was

added to rule out primer-dimer artefacts (Table 6.). Preliminary Ct results for Line 1 79 bp are shown

in Table 7. The concentration of 400 mM forward and reverse primers without additional

magnesium was chosen for efficiency and ease of preparation.

Table 6 Optimization experimental layout.

1 2 3 4 5 6 7 8 9 10 11 12

A MM A 15ul

MM A 15ul

MM A 15ul

MM B 15ul

MM B 15ul

MM B 15ul

MM C 15ul

MM C 15ul

MM C 15ul

F1 5ul F1 5ul F1 5ul F1 5ul F1 5ul F1 5ul F1 5ul F1 5ul F1 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul

B MM A 15ul

MM A 15ul

MM A 15ul

MM B 15ul

MM B 15ul

MM B 15ul

MM C 15ul

MM C 15ul

MM C 15ul

MM B+ve 13ul

MM B-ve 13ul

F2 5ul F2 5ul F2 5ul F2 5ul F2 5ul F2 5ul F2 5ul F2 5ul F2 5ul

F2 5ul F4 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul

R2 5ul R4 5ul

2ul DNA 2ul water

C MM A 15ul

MM A 15ul

MM A 15ul

MM B 15ul

MM B 15ul

MM B 15ul

MM C 15ul

MM C 15ul

MM C 15ul

F4 5ul F4 5ul F4 5ul F4 5ul F4 5ul F4 5ul F4 5ul F4 5ul F4 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul R1 5ul R2 5ul R4 5ul

3-64

Table 7 Optimization experimental results for line 1 79bp primer.

Well Identifier Ct Well Identifier Ct Well Identifier Ct Well Identifier Ct

A01 MMA 100/100 22.4 A05 MMB 100/100 20.7 A07 MMC 100/100 21.3

A02 MMA 100/200 20.6 A04 MMB 100/200 N/A A08 MMC 100/200 20.8 B11 MMB 200/200 18.6

A03 MMA 100/400 20.9 A06 MMB 100/400 20.5 A09 MMC 100/400 20.6

+ve control

B01 MMA 200/100 19.4 B04 MMB 200/100 19.4 B07 MMC 200/100 N/A B12 MMB 400/400 N/A

B02 MMA 200/200 19 B05 MMB 200/200 N/A B08 MMC 200/200 19.6

-ve control

B03 MMA 200/400 18.9 B06 MMB 200/400 18.9 B09 MMC 200/400 19.6

C01 MMA 400/100 19.3 C04 MMB 400/100 19.8 C07 MMC 400/100 20.5

C02 MMA 400/200 18.8 C05 MMB 400/200 19.6 C08 MMC 400/200 19.6

C03 MMA 400/400 18.7 C06 MMB 400/400 19 C09 MMC 400/400 20

Melt curves to look for non-specific amplification and evidence of primer-dimers were analysed, e.g.

Figure 5.

Figure 5 Melt curve for Line 1 79 bp 400/400 primer amplification.

Standard curves were tested in the optimised PCR conditions to ensure appropriate efficiency, range

and correlation (R2). R2 Values greater than 0.995 were achieved confirming accuracy of

experimental standard curve in each case.

3-65

Figure 6 Line 1 79 bp standard curve.

R2 = 0.998.

.

3.1.4.2 Amplicons and cycling conditions.

The 5 previously described amplicons are detailed in Table 8.

Table 8 PCR amplicons.

The plate setup for all amplicons and experiments is shown in Table 9.

Name Sequence 5’-3’ (forward and reverse primer) Product length [Primer]nM Source

5'-CCTGAGGTCAGGAGTTCGAG-3' 200

5'-CCCGAGTAGCTGGGATTACA-3' 200

5'-GTGGCTCACGCCTGTAATC-3' 200

5'-CAGGCTGGAGTGCAGTGG-3' 200

5’-AGGGACATGGATGAAATTGG-3' 400

5’-TGAGAATATGCGGTGTTTGG-3' 400

5'-ACACCTATTCCAAAATTGACCAC-3' 400

5'-TTCCCTCTACACACTGCTTTGA-3' 400

5'-CACCCAAGAACAGGGTTTGT-3' 400

5'-TGGCCATGGGATTGTTGTTAA-3' 400Lin, C. S.Interact Cardiovasc Thorac Surg, 2008

Diehl, F. Nat Med 2008

297bp Sunami, E Ann N Y Acad Sci 2008

79bp

108bp

Umetani, N. Clin Chem 2006

247 Umetani, N. Clin Chem 2006

115Alu 115

Alu 247

Line 1 79bp

Line 1 300bp

Mitochondrial ND1

3-66

Table 9 Plate layout for all experiments.

1 2 3 4 5 6 7 8 9 10 11 12

A NTC NTC NTC STD 1 STD 1 STD 1 STD 2 STD 2 STD 2 STD 3 STD 3 STD 3

B STD 4 STD 4 STD 4 STD 5 STD 5 STD 5

SAMPLE

1

SAMPLE

1

SAMPLE

1

SAMPLE

2

SAMPLE

2

SAMPLE

2

C

SAMPLE

3

SAMPLE

3

SAMPLE

3

SAMPLE

4

SAMPLE

4

SAMPLE

4

SAMPLE

5

SAMPLE

5

SAMPLE

5

SAMPLE

6

SAMPLE

6

SAMPLE

6

D

SAMPLE

7

SAMPLE

7

SAMPLE

7

SAMPLE

8

SAMPLE

8

SAMPLE

8

SAMPLE

9

SAMPLE

9

SAMPLE

9

SAMPLE

10

SAMPLE

10

SAMPLE

10

E

SAMPLE

11

SAMPLE

11

SAMPLE

11

SAMPLE

12

SAMPLE

12

SAMPLE

12

SAMPLE

13

SAMPLE

13

SAMPLE

13

SAMPLE

14

SAMPLE

14

SAMPLE

14

F

SAMPLE

15

SAMPLE

15

SAMPLE

15

SAMPLE

16

SAMPLE

16

SAMPLE

16

SAMPLE

17

SAMPLE

17

SAMPLE

17

SAMPLE

18

SAMPLE

18

SAMPLE

18

G

SAMPLE

19

SAMPLE

19

SAMPLE

19

SAMPLE

20

SAMPLE

20

SAMPLE

20

SAMPLE

21

SAMPLE

21

SAMPLE

21

SAMPLE

22

SAMPLE

22

SAMPLE

22

H

SAMPLE

23

SAMPLE

23

SAMPLE

23

SAMPLE

24

SAMPLE

24

SAMPLE

24

SAMPLE

25

SAMPLE

25

SAMPLE

25

SAMPLE

26

SAMPLE

26

SAMPLE

26

After experimental optimization of primer concentrations, magnesium concentration and thermal

cycling conditions, qRT-PCR was carried out as described in Table 10.

Table 10 PCR cycling conditions for each amplicon.

Name PCR conditions

Alu 115

Alu 247

Line 1 79bp

Line 1 300bp

Mitochondrial ND1

50⁰C 2 mins, 95⁰C 10mins (30 cycles of: 95⁰C 15s, 51⁰C 30s, 57⁰C 45s).

50⁰C 2 mins, 95⁰C 10mins, (30 cycles of: 95⁰C 15s, 60⁰C 1min).

94⁰C 2 min, (2 cycles of 94⁰C 20s, 67⁰C 30s, 70⁰C 45s), (2 cycles of: 94⁰C 20s, 64⁰C 30s, 70⁰C 45s),

(2 cycles of: 94⁰C 20s, 61⁰C 30s, 70⁰C 45s), (30 cycles of: 95⁰C 15s, 59⁰C 30s, 60⁰C 45s).

50⁰C 2mins 95⁰C 10mins, (30 cycles 95⁰C 15s, 57⁰C 1min).

94⁰C 2 mins, (3 cycles of : 94⁰C 20s, 68⁰C 30s, 72⁰C 30s), (3 cycles of: 94⁰C 20s, 67⁰C 30s, 72⁰C 30s),

(3 cycles of : 94⁰C 20s, 66⁰C 30s, 72⁰C 30s), (30 cycles of: 94⁰C 20s, 64⁰C 30s, 72⁰C 30s).

3-67

Each run used either 10 minutes at 95°C (to activate the DNA Taq polymerase), or 2 minutes at 94°C

and 6-9 cycles of a modified touchdown PCR, before 30 amplification cycles. Each PCR cycle

comprised denaturing, annealing, and extension. Fluorescence measurements were automatically

collected at the end of each annealing phase, with baseline measurements collected during cycles 3-

15. At the end of each run a final melt curve cycle was performed to exclude the presence of primer-

dimer artefacts.

The line 1 79 bp and Alu 115 bp fragments have both been used to quantify circulating DNA levels

before137, 149. With the Plasma XS nucleospin collecting DNA fragments > 20bp, the Line 1 79 bp

fragment is referred to as “Total DNA” reflecting the additional <100 bp fragments being collected

and measured with this technique for the first time. Alu 115 is used as a comparator and as part of

the Alu 115/247 bp fragment short : long ratio comparison.

3.1.4.3 DNA quantity calculation.

The qPCR analyser used for all experiments was the AB7500 (Applied Biosystems inc. California).

Manufacturer default analysis settings were retained. The Ct value and standard curves were

calculated automatically on the 7500 software.

To maximise comparative results across all samples, mean values for standard curve intercept and

slope were calculated from the nine experimental runs (Table 11).

Table 11 Mean standard curve values for each primer.

Line 1 79 Line 1 300 Alu 115 Alu 247 Mito

Slope CT slope CT slope CT slope CT slope CT

-3.40 14.77 -3.57 15.27 -3.49 16.78 -4.56 11.34 -3.50 18.92

0.16 0.53 0.09 0.63 0.12 1.08 0.19 0.62 0.15 1.00

3-68

These were then used to manually calculate quantities for each individual sample against a single

standard curve for each amplicon following the AB7500 manufacturer calculation below. The

standard linear regression equation Y = mX +c was used.

Ct = m [log (Qty)] + c

The slope of the line is m, X is log (Qty), c is the y intercept, and Y is the Ct value.

Rearranging the equation to solve for DNA quantity:

Qty = 10

Standard deviation for each triplicate sample quantity was calculated according to:

SD =

X = sample value, x~ = the sample mean, and n = number of samples.

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3.1.4.4 DNA dilution correction factor.

Previously purified high concentration healthy volunteer lymphocyte DNA was measured in triplicate

using a nanodrop 1000 spectrophotometer following the manufacturer protocol. The mean value

was 140.79 ng/μL. This sample was diluted 1:29 and used to make a 4.69 ng/μL standard curve

solution. This was aliquoted into 10µl samples and stored at -20°C just prior to starting the

experiments.

For the experimental standard curves this solution was further diluted 3:85, and then 1:3, and then

lastly 1:4. The calculated value of 0.1998 ng when compared to the standard curve value used (0.25)

requires application of a 0.79996 correction factor to the DNA quantity calculated by the AB7500

data.

A final dilution factor was calculated incorporating the above standard curve correction. This allows

conversion of the dilute DNA quantity calculated by the AB7500 into an accurate DNA concentration

for each mL of whole plasma.

This factor = 2.083 (correction to millilitres of whole plasma) *40 (1:40 dilution) * 23 (DNA

purification dilution) * 0.79666 (STD curve quantity correction) = 1533.01.

3.1.4.5 PCR assay co-efficient of variation results.

Coefficient of variation % is a measure of assay accuracy, and gives a measure of reliability for the

results.

It was calculated for each sample triplicate by:

The mean of the samples coefficient of variation is also known as the plate inter-assay coefficient of

variation.

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To calculate the intra-assay coefficient of variation identical mid-point standard curve triplicate CT

values across all plates were used to calculate DNA quantities as above. These values were then used

in the coefficient of variation equation to calculate the intra-assay coefficient of variation (Table 12).

Table 12 Intra-assay coefficient of variation experimental results.

Line 1 79 Line 1300 Alu 115 Alu247 Mito

inter-assay 5.59% 12.80% 13.76% 7.98% 14.83%

Range 0.26-26.05% 0.86-29.34% 0.76-76.33% 0.38-17.59% 0.01-38.48%

intra-assay 8.60% 9.19% 11.97% 6.23% 10.98%

3.1.5 CEA analysis.

CEA as the only circulating marker with any diagnostic role in gastrointestinal cancers makes an

interesting diagnostic comparator51. Testing for CEA in the colorectal neoplasia populations was

therefore undertaken.

CEA analysis was performed using a Beckman Coulter Unicel DXL 800 machine (CEA2 assay) with a

CEA2 kit (CEA Access, Beckman Coulter ref 33200). This utilised an ELISA assay with Lumi-Phos 530

(Lumigen PPD (4-methoxy-4-(3-phosphatephenyl)spiro[1,2-dioxetane-3,2’-adamantane], disodium

salt) as the fluorescent marker, and alkaline phosphatase as the linked enzyme.

The assay contains two mouse anti-bodies (MAb) with differing epitopes to the CEA protein, one

linked to a magnetic bead, and the other to alkaline phosphatase. The plasma (35 μL) is aspirated

from the sample placed in the machine, and incubated together with the MAb before washing and

separating with a magnetic field. Lumi-Phos 530 substrate is added to the separated magnetic beads

solution, and a fluorescent reading at 530 nm taken. A stored multipoint callibration curve (0, 1, 10,

100, 500 and 1000 ng/mL CEA, re-calibrated every 28 days), and daily quality control testing, allow

accurate “random access” quantification of the CEA level from 0.1 – 1000 ng/mL with 95%

confidence.

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3.1.6 Patient choice, pathological grading and disease staging.

Normal patients had undergone normal upper and/or lower endoscopy without biopsies that were

otherwise well. This represented a population with symptomatic gastro-intestinal disease that were

otherwise well.

Barrett’s oesophagus surveillance patients were included in the group when controlling the

oesophageal neoplasia study.

Patients with biopsy proven colorectal or oesophageal neoplasia undergoing endoscopic or surgical

resection had plasma samples taken pre-procedure. Oesophageal cases planned for neo-adjuvant or

palliative chemotherapy had plasma samples taken prior to commencing treatment.

Paired samples were taken from previously consented patients post-resection, when the neoplasia

was thought to have been resected back to normal. The average follow up period prior to sampling

was 330 days in colon cases (range 85-753 days) and in oesophageal cases 276 days (range 81-480

days. All paired samples were endoscopically or biopsy proven to be normal or incompletely

resected.

Radiological computed tomography staging was performed by three experienced gastro-intestinal

radiologists supplemented with endoscopic, laparoscopic and positron emission tomography (PET)

scanning results. It was the multi-disciplinary team accepted staging. Pathological staging and

grading was performed by three experienced gastro-intestinal pathologists, who reported in tandem

for endoscopically resected specimens. The pathological report of the resected specimen was used

as the definitive stage and grade. The staging system used for all cases was the Tumour Node

Metastasis (TNM) system.

3.1.7 Statistical analysis.

SPSS version 19.0 was used for all statistical analysis.

Mean and median values were calculated for all variables.

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The population values were analysed for normality of distribution graphically and with a Shapiro-

Wilk test, and this was not found. Therefore statistical tests for non-parametric data were used.

To test the statistical significance of the un-paired normal and neoplasia populations the Mann-

Whitney U test was used.

Using multiple markers increases the probability of statistically significant findings occurring by

chance. To correct for this a Bonferroni correction factor of 10 was applied raising the significant cut-

off to P= 0.005.

To evaluate the possibility of diagnostic testing with these markers logistic regression was

undertaken with receiver operating curves drawn to demonstrate the tests diagnostic possibilities.

Logistic regression predicts a dichotomous categorical variable from a set of predictor variables. It

does not require a normally distributed data set. The predicted dependent variable is a function of

the probability that a particular subject will be in one of the categories, in this thesis that the patient

has neoplasia. Logistic regression calculates the probability that a specific point is a true member of

a population by regression of the data to a best fit linear relationship. Multiple markers can be

analysed and used to improve the model. This analysis uses the default SPSS logistic regression cut-

off, predicting at a probability of ≥ 0.5.

Receiver operating curves (ROC) curves are used to scrutinise diagnostic probability. They plot the

false positive rate on the X axis and 1 - the false negative rate on the Y axis, and show the trade-off

between the two. Effectively this plots sensitivity against specificity at every single cut off. The area

under the curve measures the discrimination, or the ability of the test to correctly classify those with

and without the disease. ROC values 0.90 - 1.0 represent an excellent test, 0.80 - 0.90 a good test,

0.70 - 0.80 a fair test, 0.60 - 0.70 a poor test, and 0.50 - 0.60 a failed test.

3-73

3.2 Experimental protocols.

All methods were carried out in accordance with the control of substances hazardous to health

(COSHH) regulations, following good laboratory practice. All blood and DNA processing followed

universal precautions, and were carried out in cleaned class II biological safety cabinets.

All patients were consented for the study following ethical committee approved protocols and

national guidelines. Patient information leaflets were supplied.

3.2.1 Venepuncture.

Venous blood was taken from a peripheral vein after application of a tourniquet using a 20 millilitre

(mL) BD Plastipak syringe and a 21 gauge BD needle. Blood was subsequently inserted into a 4.5 mL

EDTA BD vacutainer. Blood was stored at room temperature for 2-3 hours maximally prior to

processing.

3.2.2 Cell and plasma separation.

A 5 mL aliquot of blood was added to 5 mL of Hanks balanced salt solution (GIBCO 14175, –MgCl2, -

CaCl2. [Potassium Chloride (KCl 5.33mM), Potassium Phosphate monobasic (KH2PO4 0.441mM),

Sodium Bicarbonate (NaHCO3 4.17mM), Sodium Chloride (NaCl 137.93mM, Sodium Phosphate

dibasic (Na2HPO4 anhydrous 0.338mM), D-Glucose (Dextrose) 5.56mM])

in a 30mL polypropylene universal container and inverted 3 times to mix. The mixture was layered

carefully over 10 mL of histopaque 1077 (Sigma Aldrich 10771) in a new 30 mL polypropylene

universal container using a sterile 3 mL polypropylene Pasteur pipette before centrifuging at 600g

for 30 minutes (1830rpm – Harrier 15/80 MSE centrifuge MSB080.CX 1.5).

3-74

After centrifuging the blood had been separated into 4 layers: half strength plasma (“plasma”) top

layer; white cells (“buffy coat”) layer; Histopaque 1077 layer; and bottom red cell layer. The top

“plasma” layer was carefully removed from the underlying “buffy” coat layer with a new sterile 3 mL

polypropylene Pasteur pipette, watching for any disturbance to the “buffy” coat layer. The “plasma”

was re-centrifuged at 400g (Harrier 15/80 MSE centrifuge MSB080.CX 1.5) for 7 minutes, before

removal of the top part of the “plasma” using a new sterile 3mL polypropylene Pasteur pipette

without disturbing the bottom pellet.

The “plasma” was stored in a 2 mL nuclease-free polypropylene container at -80°C (Sanyo Ultralow

VIP series -86), before batch processing at the end of the collection period.

3.2.3 Nucleospin DNA purification.

Prior to processing “plasma” was re-centrifuged at 11,050g (Eppendorf microfuge) for 3 minutes in a

final manufacturer recommended step to remove all contaminating cellular debris.

Two aliquots of 480 μL of “plasma” were mixed (inverting 3 times, and briefly vortexed for 3 s with

720 μL of binding buffer in two 1.7 mL nuclease-free polypropylene micro-centrifuge tubes, and

spun down briefly. 600 µl of the mixture was aliquoted onto a nucleospin column before spinning

at 2000g for 30 s, and 5 s at 11,000g (Eppendorf microfuge). The flow through was discarded. The

spin column was re-loaded until all 2400 μL was processed.

The spin column was put into a new collection tube and 500 μL of washing buffer was added, prior

to spinning at 11,050g for 30 s. The spin column was put into another new collecting tube and 250

μL of washing buffer was added, prior to spinning at 11,050g for 3 minutes. Finally the spin column

was put into a 1.7 mL nuclease-free micro-centrifuge tube, and 30 μL of elution buffer added to the

spin column, prior to spinning at 11,050g for 30 s.

3-75

Figure 7 Plasma XS spin protocol.

1. Processed Plasma 2. Repeated plasma spins 3. Elution and wash spins 4.PurifedDNA

Excess ethanol was driven off from the eluted purified DNA sample by heating at 90°C for 8 minutes

in a class II cabinet, without laminar airflow.

DNA was stored at -20°C (Liebherr Pro line freezer), in the micro-centrifuge tubes, defrosting only

once for processing. A 1 in 40 dilution of the DNA samples was used to maximise residual DNA

available for further experimentation.

3.2.4 qPCR.

All pipette tips and plastic-ware was certified DNase- and RNase-free by the manufacturer. All

solutions were carefully mixed by vortexing or flicking before pipetting.

In the clean “reagent only” laboratory and class II biological hood, a 10 μM solution of forward and

reverse primers was made for each amplicon (100 μM stock solution). The working solutions were

stored at 4o for up to 2 weeks at which point they were discarded and fresh working primers were

made.

Five master mixes were made containing the following: AB X 2 SYBR Green Master mix™(Applied

Biosystems; S4438- Sigma, UK), 12.5 μL per well; forward and reverse primers (1μL per well of 10

μM primer for reactions using 400 nM concentrations, and 0.5 μL per well for 200 nM

concentrations); and DNase-free water to a total volume of 20 μL per well. The master mixes were

pipetted into each 96 well plate, checked and sealed.

3-76

In a separate “template only” laboratory and class II biological hood, 5 μL of 1:40 diluted template

was added to each reaction well, making a total of 25 μL in each well .

Control wells were incorporated into the plate design; instead of template, these contained 5 μL

water and 20 μL of master mix only (NTC - no template controls).

A 5 point 1:4 dilution standard curve was constructed using a standardised, batched and previously

prepared aliquot of genomic DNA, with known concentration. This was pipetted in triplicate

sequentially across the plate.

All five amplicon plates were made up at the same time using the same genomic DNA sample for

each standard curve. The plates were stored at 4°C prior to sequential analysis on the ABI 7500

machine.

Continual melt curve fluorescence analysis was carried out by cooling to 60°C, and raising the

temperature to 95°C with a 1% ramp rate.

3.2.5 CEA analysis.

Plasma samples were thawed and mixed thoroughly before being placed in a 35 μL Coulter

autosampler and aspirated. Daily laboratory quality controls checks were confirmed and results

printed off.

4-77

Chapter 4 Colon results.

4.1 Demographics.

The study population comprised 85 patients, including 35 patients without endoscopic abnormality,

a group of 26 patients with benign colorectal adenomas, and 24 patients with colorectal carcinomas.

The reference ‘normal’ group of 35 patients, included 15 men, and was chosen as representing a

typical population attending for endoscopic investigation but with no detectable abnormality (Table

13). The mean age of this group was 54.1 years (range 24 - 80 years). Significant intercurrent

diagnoses in this group included: osteoarthritis; ischaemic heart disease; pernicious anaemia; and

peptic ulcer.

Table 13 Normal population indication for endoscopy.

Endoscopy indication Patient

numbers Dysphagia 5

Dyspepsia 10

Ulcer follow up 1

Haematemesis 2

Abdominal pain 8

Change in bowel habit 7

Family history of cancer 1

Anaemia 5

Polyp follow up 1

Melaena 1

4-78

4.2 Patient groups.

The benign colorectal polyp group of 26 patients, included 14 men, and had a mean age of 70.2

years, (range 56 - 85 years).

Twenty-nine polyps were removed from these patients, mean size 54.3 mm (range 5 - 200 mm).

Eighteen of these polyps showed low grade dysplasia (LGD), and had a mean size of 56.1 mm (range

5 - 200 mm), while 11 polyps showed high grade dysplasia (HGD) mean size 52.5mm (range 28 - 100

mm). Significant residual polyp remained in 3 patients, and a further blood sample taken from these

patients is included in the analysis.

The group of cancer patients included 19 men and had a mean age of 71.5 years, (range 49 - 87

years). There were four patients with polyp cancers, mean size 41.7 mm (range 25 - 40 mm), but the

majority had biopsy proven colorectal carcinomas, pathological TNM T staging (pT) : pT0 x 5 (4

polyps, 1 post radiotherapy); pT2 x 4; pT3 x 14; pT4 x 1.

In total, both polyp and cancer populations include 50 patients, and 53 plasma samples, with a mean

age of 71.1 years, (49 - 87 years).

4.3 Mean and median marker values.

Table 14 Mean values for all markers.

Mean values

Age years

CEA ng/ml plasma

Total DNA ng /ml plasma

Mito ng/ml plasma

Alu 115 ng/ml plasma

Line 300 ng/ ml plasma

Alu 247 ng/ml plasma 79/300 115/247

normal 54.1 0.5 8.6 0.9 24.6 1.6 2.8 6.4 8.3

Polyps 71.7 0.7 16.1 2.9 74.4 4.4 5.5 6.4 18.0

Cancer 71.4 1.1 29.7 3.8 122.1 4.5 8.7 7.0 14.3

LGD polyps 73.4 0.46 15.8 3.5 78.3 5.0 5.4 7.11 20.1

HGD polyps 69.1 0.94 16.5 1.91 68.4 3.4 5.5 5.27 14.1

4-79

Table 15 Median values for all markers.

Median values Age years

CEA ng/ml plamsa


Mito ng/ml plasma



Alu 247 ng/ml plasma 79/300 115/247

Normal group 55.0 0.4 7.2 0.7 24.1 1.6 2.7 5.0 8.4

Polyps group 71.0 0.6 12.5 1.4 62.4 3.2 4.9 4.3 16.0

Cancer group 72.5 0.5 14.9 1.6 73.1 2.2 4.8 6.1 11.7

LGD polyps 73 0.6 12.7 1.5 64.3 3.5 4.8 4.7 18.0

HGD polyps 69 1.1 11.9 1.2 34.8 3.1 4.9 4.2 11.0

Each marker was analysed between normal, polyp and cancer populations (Tables 14 and 15). There

was an increase in the mean values of all markers with increasing pathological grade, though not all

these differences were statistically significant from the control population (Table 16).

Looking at the polyp group separately these patterns were not mirrored in the transition from LGD

to HGD, and statistically there were no significant differences (Table 16). There was a single patient

with a LGD polyp represented an extreme outlier, with a total DNA level of 206 ng/ml. This patient

was removed from the mean and median data, but is included in all other analyses.

4.4 Statistical significance - Mann Whitney U-tests.

Applying a 10 fold Bonferroni correction, the significant P value = 0.005.

Table 16 Statistical significance testing.

Mann Whitney-U test P values

CEA ng/ml plamsa


Mito ng/ml plasma



Alu 247 ng/ml plasma 79/300 115/247

Polyps vs. normal groups 0.288 <0.001 0.001 <0.001 <0.001 0.003 0.189 0.002

Cancer vs. normal groups 0.425 0.002 0.002 <0.001 0.015 0.001 0.087 0.001

Polyps and cancer vs. normal 0.267 <0.001 <0.001 <0.001 <0.001 <0.001 0.908 <0.001

LGD vs. HGD polyps 0.906 0.269 0.557 0.796 0.981 0.655 0.438 0.906

4-80

As compared with normal controls, patients with benign polyps had significantly raised levels of total

DNA (p<0.001), mitochondrial DNA (p=0.001), Alu 115bp (P<0.001), Line 1 300bp (P<0.001) Alu

247bp fragment (p=0.003, and Alu 115/247 ratio (P=0.002) Table 16. Further investigating the polyp

populations there were no significant differences comparing LGD polyps to HGD polyps.

As compared with normal controls, patients with colon cancer had significantly raised levels of total

DNA (P=0.002), Mitochondrial DNA (p=0.002), Alu 115bp (P<0.001), Alu 247bp (p=0.001), and the

Alu 115/247 ratio (P=0.001) Table 16.

Comparison of normal with all neoplasia patients showed that all markers except the Line 1 79/300

ratio were highly significantly different between the populations (P<0.001).

4.5 Logistic regression and ROC curve values.

Table 17 ROC curve characteristics.

ROC values

CEA ng/ml plamsa


Mito ng/ml plasma



Alu 247 ng/ml plasma 79/300 115/247

polyps vs normal 0.578 0.767 0.742 0.856 0.756 0.719 0.596 0.732

cancer vs normal 0.562 0.738 0.743 0.838 0.687 0.764 0.632 0.760

polys and cancer 0.571 0.754 0.742 0.848 0.725 0.74 0.507 0.745

The best single marker in the polyp population was the Alu 115 (ROC=0.856), with the combination

DNA marker showing an excellent diagnostic ability (ROC = 0.921) Table 17.

The best single marker in the colon cancer population was the Alu 115 (ROC=0.838), with the

combination DNA marker showing a very good diagnostic ability for distinguishing cancer from

normal in this series (ROC = 0.854).

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The best single marker for diagnosis of all neoplasia in this population was Alu 115bp (ROC = 0.848),

with the combination DNA marker showing a very good diagnostic ability (ROC=0.888). This had a

sensitivity of 81.1%, specificity of 75.8%, positive predicitve value of (PPV) of 84.3%, and negative

predictive value (NPV) of 71.4% (Figure 8).

CEA is shown once again to be a poor diagnostic marker for colonic neoplasia, with no significant

difference between the populations. The ROC values indicating failure to discriminate between the

populations (ROC = 0.578).

Its addition with the 4 marker regression did increase the test performance in all categories (ROC =

0.929, 0.875, 0.901). This final test had a sensitivity of 81.1%, specificity of 79.4%, PPV of 86.0%, and

NPV of 72.9% (Figure 9). Perhaps this demonstrates a role for combination marker types in future

testing.

Figure 8 Combined DNA marker ROC curve.

(Sensitivity = 81.1%, specificity = 75.8%, PPV = 84.3%, NPV = 71.4%).

4-82

Figure 9 Combined DNA marker plus CEA ROC curve.

(Sensitivity = 81.1%, specificity 79.4%, PPV = 86.0%, NPV = 72.9%).

4.6 Box plots and whisker analysis.

Boxes represent 25th to 75th centiles and the internal black line the median value. The top whisker is

the maximum SPSS accepted population value (< 1.5 Inter-quartile range (IQR) from the edge of the

box). The bottom whisker is the minimum SPSS accepted population value (<1.5 (IQR) ). “O” is an

outlier in population between 1.5 and 3.0 x IQR from the edge of the box. “*” is an extreme outlier

greater than 3.0 x inter-quartile range from edge of box. The scale on the Y axis is logarithmic to

allow easier evaluation of the main populations. There are normal, polyp and cancer groups.

Figure 10 CEA box plot analysis.

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Figure 11 Total Nuclear DNA box plot analysis.

Figure 12 Mitochondrial DNA box plot analysis.

4-84

Figure 13 Alu 155bp DNA box plot analysis.


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Figure 15 Line 1 300bp DNA box plot analysis.

Figure 16 Line 1 79:300 DNA ratio box plot analysis.

4-86

Figure 17 Alu 115:247 DNA ratio box plot analysis.

4.7 Correlation of Total cfDNA with age.

Analysing the most uniform population (normal group), there was no significant correlation with age

using both Spearman’s rho (P=0.07), and Kendalls tau b (P=0.09).

Figure 18 Correlation of total cfDNA with age.

4.8 Conclusions.

This study demonstrates highly significant differences between a “normal” population, a

population with adenomatous polyps, and a population with colonic cancer.

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Quantities and patterns of circulating DNA differ significantly between the groups, with total

quantities of nuclear and mitochondrial DNA increasing with pathological grade, and

fragmentation patterns changing between normal, adenomatous and cancer populations.

Individual markers demonstrate good diagnostic tests, and in combination provide an

improved test for both polyps and cancer (ROC = 0.888).

Although adding CEA levels to the combination DNA marker did improve the final diagnostic

test for polyps and early cancer, ROC curve = 0.901. It did not add greatly and the results are

included largely to show that this marker, even in combination with others, has little

diagnostic value.

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Chapter 5 Oesophago-gastric results.

5.1 Demographics.

The study population comprised 115 patients, with 120 samples and includes: 35 patients without

endoscopic abnormality; 9 patients with Barrett’s oesophagus; 13 patients with high grade dysplasia;

12 patients with invasive cancers restricted to the mucosa (IMC); 3 patients with invasive cancers

restricted to the sub-mucosa (SMC); and 43 patients with advanced cancers.

The reference “normal” group consisted of nine biopsy proven benign Barrett’s oesophagus cases,

and 35 patients representing a typical population attending for endoscopic investigation but with no

detectable abnormality (Table 18). The mean age of the combined normal group was 56.1 years

(range 24 - 80 years), and included 22 men (7 Barrett’s, and 15 normal endoscopy patients).

Significant inter-current diagnoses in this group included: osteoarthritis; ischaemic heart disease;

pernicious anaemia; and peptic ulcer.

Table 18 Normal population indication for endoscopy.

Endoscopy indication Patient

numbers Dysphagia 5

Dyspepsia 10

Ulcer follow up 1

Haematemesis 2

Abdominal pain 8

Change in bowel habit 7

Family history of cancer 1

Anaemia 5

Polyp follow up 1

Melaena 1

Barrett’s surveillance 9

5-89

5.2 Patient groups.

Due to well documented difficulties accurately identifying Barrett’s dysplasia grades, the groups

were combined into clinically relevant groups. These groups have a higher concordance for accurate

differentiation pathologically319, 320. When pathological samples showed mixed grades the highest

grade of dysplasia was used. The combined groups were “HGD and IMC”, and “SMC and cancer”.

The HGD and IMC group consisted of 25 patients, 20 men, with a mean age of 70.0 years (range 54-

89 years). There were 30 samples, 15 each of HGD and IMC, six from patients with recurrent disease.

The SMC and cancer groups consisted of 43 patients, 18 men, with a mean age of 68.9 years (range

50-82 years). There were 46 samples, 3 from patients with superficial sub-mucosal invasive disease.

Forty samples were from advanced cancers, staged by CT, EUS, and PET scanning as: 5 x T1N0M0, 16

x T2/3N0M0, 4 x T2N1M0, 5 x T3N1M0, 1 x T4N1M1, 10 had metastatic disease and 2 had unclear

staging prior to receiving palliative chemotherapy. The T0 and T1 cancers were deep sub-mucosal

invading tumours.

In total, both dysplasia and cancer populations include 68 patients, and 76 plasma samples, with a

mean age of 69.5 years, (39 - 89 years).

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5.3 Mean and median marker values.

Table 19 Mean value for all markers.

Mean values Total Mito 115 300 247 79/300 115/247

"Normal" 10.8 1.1 35.6 1.8 3.1 6.3 11.0

HGD + IMC 14.1 4.2 42.6 3.2 4.6 6.1 10.3

SMC & Cancer 19.2 6.2 76.9 5.3 4.9 8.8 16.3

Table 20 Median value for all markers.

Median values Total Mito 115 300 247 79/300 115/247

"Normal" 7.3 0.7 24.9 1.5 2.7 5.1 9.5

HGD + IMC 11.0 1.5 30.7 2.4 3.2 5.3 10.3

SMC & Cancer 12.2 1.5 39.5 2.8 2.3 6.3 12.4

Each marker was analysed between normal, HGD and IMC, and SMC and cancer populations (Tables

19 and 20). There was an increase in the mean values of all markers with increasing pathological

grade except for the fragment ratios. Not all of these differences were statistically significant (Table

21).

5.4 Statistical significance - Mann Whitney U-tests.


Table 21 Statistical significance testing.

Mann Whiteny U-test P values Total Mito 115 300 247 79/300 115/247

“Normal” vs HGD + IMC 0.010 0.016 0.121 0.014 0.076 0.939 0.779

“Normal” vs SMC & Cancer 0.013 0.000 0.003 0.004 0.448 0.180 0.180

“Normal” vs all abnormal 0.003 0.000 0.006 0.001 0.715 0.121 0.086

As compared with normal controls, patients with HGD and IMC did not have significantly raised

levels of all markers, Table 21.

As compared with normal controls, patients with SMC and oesophageal cancer had significantly

raised levels of mitochondrial DNA (p=0.000), Alu 115bp (P<0.003), Line 1 300bp (P=0.004) Table 21.

5-91

Comparison of normal with all neoplasia patients showed that Total DNA (p=0.003), mitochondrial

DNA (p=0.000), and Line 1 300bp (p=0.001) were highly significantly different between the

populations.

5.5 ROC curve values and Logistic regression.

Table 22 ROC curve characteristics.

ROC curve values Total Mito 115 300 247 79/300 115/247

"Normal" vs HGD + IMC 0.678 0.665 0.607 0.67 0.622 0.505 0.481

"Normal" vs SMC & cancer 0.652 0.713 0.68 0.677 0.454 0.646 0.646

"Normal" vs all abnormal 0.662 0.694 0.651 0.674 0.674 0.585 0.595

The best single marker in the HGD and IMC population was Total DNA (ROC=0.678), with the

combination DNA marker showing a fair diagnostic ability (ROC = 0.718) Table 22.

The best single marker in the SMC and oesophageal cancer population was mitochondrial DNA

(ROC=0.713), with the combination DNA marker showing a good diagnostic ability for distinguishing

cancer from normal in this series (ROC = 0.847). The combination test had a PPV for invasive cancer

patients of 81.1% and a NPV of 70.6% (Sensitivity = 61.3%, specificity = 83.7%) (Figure 19).

The best single marker for diagnosis of all neoplasia in this population was Alu 115 mitochondrial

DNA (ROC = 0.694), with the combination DNA marker showing a fair diagnostic ability (ROC=0.778).

The combination test had a PPV for dysplasia and cancer patients of 78.2%, and a NPV of 65.0%

(Sensitivity = 81.3%, specificity = 60.5%) (Figure 20).

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Figure 19 Normal group vs. all invasive cancer group.

Sensitivity = 61.3%, specificity = 83.7, PPV = 81.1%, NPV = 70.6%.

Figure 20 Normal group vs. all abnormal oesophagus group.

Sensitivity = 81.3%, specificity = 60.5%, PPV = 78.2%, NPV = 65.0%.

5-93

5.6 Box plots and whisker analysis.

Boxes represent 25th to 75th centiles and the internal black line the median value. The top whisker is

the maximum SPSS accepted population value (< 1.5 Interquartile range (IQR) from the edge of the

box). The bottom whisker is the minimum SPSS accepted population value (<1.5 (IQR) ). “O” is an

outlier in population between 1.5 and 3.0 x IQR from the edge of the box. “*” is an extreme outlier

greater than 3.0 x inter-quartile range from edge of box. There are normal, mucosal neoplasia and

cancer groups.

Figure 21 Total Nuclear DNA box plot analysis.

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Figure 22 Mitochondrial DNA box plot analysis.

Figure 23 Alu115bp DNA box plot analysis.

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Figure 25 Line 1 300bp DNA box plot analysis.

5-96

Figure 26 Line 1 79:300 DNA fragment ratio box plot analysis.

Figure 27 Alu 115:247 DNA fragment ratio box plot analysis.

5.7 Conclusions.

This study demonstrates highly significant differences in cfDNA between a “normal”

population, a population with dysplasia and early cancer, and a population with invasive

cancers.

Quantities of circulating DNA differ significantly between the groups, with total quantities of

nuclear and mitochondrial DNA increasing with pathological grade. Mean and median

fragmentation ratios increase through grades, which although not significant, may reflect a

true change in populations.

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Although most individual markers demonstrate only fair diagnostic tests, in combination

they provide an improved test for both neoplastic groups and in particular provide a good

test for advanced invasive cancers (ROC = 0.847).

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Chapter 6 Paired colonic and oesophageal results.

6.1 Introduction.

This chapter looks at the feasibility of using cfDNA markers to judge whether a colonic or

oesophageal lesion has been completely removed endoscopically. Current practice involves

repeating the endoscopic assessment at 3, 6 and 12 monthly intervals.

The markers will be measured before and after resection in pairs from the same patient. The paired

samples will allow the statistically more powerful Wilcoxon signed rank test to be used.

The mean pre and post-resection levels will also be compared to the control population using the

Mann-Whitney test. This will analyse whether pre-resection patient levels and later post-resection

levels are different from background control results.

6.2 Colon results.

6.2.1 Demographics .

The paired samples came from 15 patients with an average age of 72 y, 7 men.

6.2.2 Lesions.

Pre-resection lesions included one polyp cancer, nine HGD polyps, and five LGD polyps. After follow

up over 2 years 14 patients had been resected back to normal, and 1 patient had a small area of

residual LGD polyp at the time of the last sampling.

Only the fourteen samples with complete resection were analysed for statistical purposes. However,

in the line graphs below, results included the sample with residual LGD dysplasia and an

intermediate sampling result in a patient with residual LGD who went on to complete resection.

These are included for comparison purposes.

6.2.3 Mean results.

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Table 23 below shows mean values for the patients with polyps before and after resection. To allow

comparison the polyps group (pre-resection equivalent) and normal (post-resection equivalent) are

included.

Although the absolute values are difficult to interpret due to the small numbers, there is a consistent

trend mirroring the differences between normal and polyp groups.

Table 23 Paired means pre and post resection, and normal and polyps.

Mean values

CEA ng/ml plamsa


Mito ng/ml

plasma




79/300 115/247

Polyps group 0.7 16.1 2.9 74.4 4.4 5.5 6.4 18.0

Normal group 0.5 8.6 0.9 24.6 1.6 2.8 6.4 8.3

Pre-resection 0.80 33.58 8.27 101.07 26.06 20.53 3.63 14.96

Post-resection 0.75 12.89 6.55 51.49 1.78 5.36 7.09 10.96

6.2.4 Significance testing.

Paired sample values pre and post-resection were tested with the Wilcoxon signed rank test, non-

paired comparisons against the control group and were tested with the Mann-Whitney statistic.


Table 24 Significant P values.

P values

CEA ng/ml plamsa


Mito ng/ml

plasma

Alu 115 ng/ml

plasma


Alu 247 ng/ml

plasma 79/300 115/247

pre vs post-resection 0.637 0.551 0.433 0.331 0.005 0.875 0.001 0.272

pre-resection vs normal 0.048 0.025 0.004 0.000 0.001 0.049 0.013 0.046

post-resection vs normal 0.064 0.138 0.347 0.028 0.507 0.020 0.127 0.785

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6.2.5 Individual markers.

6.2.5.1 CEA analysis.

There are no significant differences between the polyp CEA levels, before and after resection (P >

0.048). Moreover the trend does not decrease towards normal levels and the illustration below

shows this (black lines represent cases resected back to normal, and red broken lines cases not

resected back to normal). This confirms the opinion that CEA is a poor diagnostic marker.

Figure 28 Paired CEA values pre and post resection.

6.2.5.2 Line 1 79bp - total cfDNA.

There are no significant differences between the polyp total DNA levels, before and after resection

(P > 0.025). However, the trend does decrease towards normal levels and the illustration below

shows this.

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Figure 29 Paired total cfDNA values pre and post resection.

6.2.5.3 Mitochondrial cfDNA.

There are no significant differences between the patients’ mitochondrial DNA levels before and after

resection. However, when comparing pre-polyp levels to the control population there is a significant

difference ( p=0.004). Post resection levels are not significantly different to the normal population

(p=0.347). The illustration below shows this, although not all cases follow this pattern.

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Figure 30 Paired mitochondrial cfDNA values pre and post resection.

6.2.5.4 Alu 115bp fragment.

There are no significant differences between the patients’ Alu 115bp DNA levels before and after

resection. However, when comparing pre-polyp levels to the normal population there is a significant

difference (p=0.000). Post resection Alu 115bp levels are not significantly different to the normal

population (p=0.028). The illustration below shows this, although not all cases follow this pattern.

Figure 31 Paired Alu 115bp values pre and post resection.

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6.2.5.5 Line 1 300bp fragment.

There are significant differences between the patients’ Line1 300bp DNA levels before and after

resection of their polyps (p=0.005). When comparing pre-polyp DNA levels to the normal population

there is also a significant difference (p=0.001). Post resection levels are not significantly different to

the normal population (p=0.507).The illustration below shows this, although not all cases follow this.

Figure 32 Paired Line 300bp values pre and post resection.


There are no significant differences between the polyp total DNA levels, before and after resection

(P > 0.020). However, the trend does decrease towards normal levels and the illustration below

shows this.

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6.2.6 Marker ratios.

6.2.6.1 Line 1 79:300.

There are significant differences between the patients’ Line 1 79/300bp ratio levels before and after

resection of their polyps (p=0.001). However, when comparing pre-polyp Line 1 79/300bp ratio

levels to the normal population there is no significant difference (p=0.013), and therefore the

analysis of this marker is uncertain.

Post resection Line 1 79/300bp ratio levels are not significantly different to the normal population

(p=0.127), and therefore the mixed results reflect the poor diagnostic usefulness of this marker

when used alone.

The illustration below does show a clear change in ratio with resection, which would be in keeping

with the theories of circulating DNA origins reflecting the fall in the amount of apoptotic long

fragment DNA. Perhaps this explains in part why this marker is helpful in combination with other

markers.

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Figure 34 Paired Line 1 79:300 ratio values pre and post resection.

6.2.6.2 Alu 115:247.

There are no significant differences between the polyp Alu 115/247bp ratio levels, before and after

resection (P > 0.046). However, the trend does decrease towards normal levels and the illustration

below shows this.

The illustration below shows a mixed picture but there are only a few cases that rise. Interestingly,

the fall in ratio is the exact opposite of the Line 1 ratio above, but does mirror the trend in the polyp

and cancer groups compared to normal patients. Looking at the results the large Alu 247bp fragment

levels do fall.

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Figure 35 Paired Alu 115:247 ratio values pre and post resection.

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6.3 Oesophageal results.

6.3.1 Demographics .

The paired samples came from 14 patients, 12 men, with an average age of 66.3 years.

6.3.2 Lesions.

Pre-resection lesions included 4 SM1 invasive cancers, 3 IMC lesions, and 7 HGD.

After follow up over 2 years 8 patients had been resected back to normal Barrett’s, 1 patient later

developed carcinomatosis of unknown primary, 1 patient had a residual IMC lesion, 3 patients had

residual HGD lesions, and 1 patient developed a LGD lesion.

Post resection diagnoses were made by an experienced endoscopist using specialist dye spray

techniques and gold standard biopsy practice. Biopsy specimens and resection samples were

reviewed by two experienced pathologists.

Only the eight samples with complete resection back to normal Barrett’s were analysed for

statistical purposes. However, in the line graphs below results included the other samples for

comparison purposes. The sample pair with residual LGD dysplasia sample was represented as

normal due to the difficulties discriminating LGD from inflammation in specimens.

6.3.3 Mean results.

Table 25 below shows mean values for patients with neoplastic Barrett’s lesions before and after

resection. To allow comparison the mucosal neoplasia group (pre-resection equivalent) and control

group (post-resection equivalent) are included.

Although the absolute values are difficult to interpret due to the small numbers, there is little if any

pattern or difference visible.

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Table 25 Paired means pre and post resection, and control and mucosal neoplasia.

Mean values Total DNA

ng /ml plasma

Mito ng/ml

plasma

Alu 115 ng/ml

plasma


Alu 247 ng/ml

plasma 79/300 115/247

Mucosal neoplasia group 14.1 4.2 42.6 3.2 4.6 6.1 10.3

Control group 9.88 0.99 34 1.7 2.83 6.3 11.1

Pre-resection 12.36 1.57 32.17 2.23 3.57 7.94 9.36

Post-resection 14.04 0.98 37.08 2.57 6.41 6.42 6.35

6.3.4 Significance testing.

Paired sample values pre and post-resection were tested with the Wilcoxon signed rank test, non-

paired comparisons against the control group and were tested with the Mann-Whitney statistic. In

keeping with the mucosal neoplasia group in chapter 5 there are no markers that are significantly

different to the control population results.


Table 26 Significant P values.

P values


Mito ng/ml

plasma

Alu 115 ng/ml

plasma


Alu 247 ng/ml

plasma 79/300 115/247

pre vs post-resection 0.859 0.515 0.314 0.260 0.594 0.110 0.678

pre-resection vs normal

0.041 0.251 0.522 0.293 0.339 0.586 0.748

post-resection vs normal

0.032 0.679 0.517 0.422 0.006 0.312 0.101

6.3.5 Individual markers.

6.3.5.1 Line 1 79bp - total cfDNA.

There are no significant differences between the patients’ total DNA levels before and after

resection (P > 0.032). The illustration below shows a general reduction in levels but unfortunately

total DNA is not a good measure of completeness of resection.

(Black lines represent cases resected k to normal, and red broken lines cases not resected to normal)

6-109

Figure 36 Paired total cfDNA values pre and post resection.

6.3.5.2 Mitochondrial cfDNA.

There are no significant differences between the patients’ mitochondrial DNA levels before and after

resection (P > 0.251). The illustration below shows a mixed picture.

Figure 37 Paired mitochondrial cfDNA values pre and post resection.

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6.3.5.3 Alu 115 fragment.

There are no significant differences between the patients’ Alu 115bp fragment DNA levels before

and after resection (P > 0.314). The illustration below shows a mixed picture.


6.3.5.4 Line 1 300bp fragment.

There are no significant differences between the patients’ Line 1 300bp fragment DNA levels before

and after resection (P > 0.260). The illustration below shows a mixed picture.

Figure 39 Paired Line 1 300bp values pre and post resection.

6-111


There are no significant differences between the patients’ Alu 247bp fragment DNA levels before

and after resection, although it is the best test (P > 0.006). The illustration below shows mainly an

increased level of the “apoptotic” fragment post resection, which doesn’t fit the explanations given

for the origin of circulating DNA. This result needs to be treated cautiously.


6.3.6 Marker ratios.

6.3.6.1 Line 1 79:300.

There are no significant differences between the patients’ Line 1 79/300bp fragment ratio before

and after resection (P > 0.110). However, the illustration below shows a mainly decreased level

regardless of resection, which may illustrate why it adds diagnostically as part of the multi-marker

model.

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6.3.6.2 Alu 115:247.

There are no significant differences between the patients’ Alu 155/247bp fragment ratio before and

after resection (P > 0.101). The illustration below shows a mixed picture.

Figure 42 Paired Alu 115:247 ratio values pre and post resection.

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6.4 Conclusions.

The number of paired samples resected to normal in colonic and oesophageal neoplasia

cases is small, making interpretation difficult. There are occasional trends, and these data

warrant larger studies in both cancer types.

In colonic neoplasia the Line 1 300bp marker is significant different in the paired and non-

paired analysis. This appears to confirm its diagnostic role in colonic neoplasia and suggests

it may be feasible to use this as a measure of endoscopic polyp resection.

The other markers mirror the results in chapter 4, but are not significant. This pattern is

confirmed by the graphical illustrations which suggest that the total DNA, mitochondrial

DNA, Alu 115bp fragment, Alu 247bp fragment, and Line 1 79/300bp fragment ratio may be

helpful in the diagnosis of colonic neoplasia.

In oesophageal neoplasia the best marker is the Alu 247bp marker which approaches

significance (P=0.006), but the result appears to confound current understanding of cfDNA

origins. The graphical illustrations suggest that the Line 1 300bp and Line 1 79/300bp

markers may be helpful in a larger oesophageal neoplasia study.

Overall the oesophageal results reflect those in chapter 6 where no marker was significantly

different when comparing mucosal neoplasia to the control group. It seems unlikely that

these markers will help to judge complete endoscopic removal.

114

Chapter 7 Discussion and conclusions.

Colorectal and oesophageal cancer carry a high mortality in the UK, and efforts to diagnose the

disease at an earlier stage are regarded as key to reduce associated deaths54. Current diagnostic

approaches have problems with poor sensitivity and low acceptability. This study was undertaken to

try and find markers to identify early cancer and thereby address these issues.

cfDNA shows potential as an early cancer marker with Alu 115bp, Alu 247bp, Line 1 79bp, Line 1

300bp and mitochondrial DNA sequences previously shown to be raised in populations with

advanced cancer.

Using improved techniques, this study tested the hypothesis that patients with pre-cancerous

change and early cancers also have significantly different cfDNA levels and ratios when compared to

control or “normal” populations. As part of this study, levels of the markers were tested before and

after endoscopic resection to act as a marker for completeness of endoscopic resection.

This chapter reviews the individual results and discusses them in the context of the circulating

biomarker literature, the origins of cfDNA, and the potential role as a diagnostic test for colonic and

oesophageal cancers. The current evidence based approaches to the collection and processing of

specimens will also be reviewed.

7.1 Control population results.

The patients in the control group have symptomatic benign gastrointestinal disease, and normal

investigation. With a mean age of 54.1 years they represent an “at risk” group for colorectal and

oesophageal cancer and therefore are a good clinical control group.

The total amount of cfDNA in the control population in this study was 10.8 ng/ml of plasma ,

comparable to a study of normal population baselines reporting 16 ng of cfDNA/ml of whole blood.

Age does not seem to affect cfDNA levels 124.

115

Included in the oesophageal control group were patients with Barrett’s oesophagus. This is

important if the test is to have a clinical role. Although the number of these patients was small there

was no significant difference in the individual marker results, except the ratio marker Alu 115/247bp.

In the context of non-significant individual markers this is suspicious for a type I error. Further

testing will be needed to confirm this.

Reservations regarding the utility of cfDNA markers in discriminating the normal population have

been expressed. An ovarian cancer study included a population of patients admitted to hospital for

treatment of benign diseases and these formed part of the control group. Results from this control

group showed raised cfDNA levels reaching 16 ng/ml of plasma. It was therefore concluded that

cfDNA has reduced diagnostic accuracy170. However, these ‘benign’ conditions included patients

admitted to hospital with infections, autoimmune disease, post organ transplant, post-trauma, AIDS,

and asthma. These conditions are likely to be associated with active inflammation and increased

apoptosis which cause proven increases in circulating DNA103, 107, 113, 164. Inflammation in a study of

circulating DNA nucleosomes shows a clear correlation with DNA levels174. Such severely ill patients

are unlikely to be represented in large numbers within the population attending screening visits and

would be rapidly removed from the test pool. We therefore believe that in our group of patients

confounding factors such as inflammatory lesions would be picked up through the clinical history

and treated separately and that cfDNA could still be a good marker for differentiating between

normal and cancer groups.

In a single patient with Barrett’s oesophagus the cfDNA level was surprisingly high with a total DNA

of 78ng/ml. The reason for this patient’s results are not clear; the patient was losing weight and had

transfusion dependent anaemia. A similar statistically outlying patient with a high DNA level of

47ng/ml had an unexpected pancreatic cancer found.

116

It may be that in the 1st patient a similar diagnosis will eventually be made. These cases

demonstrate both the strength of measuring cfDNA as a test for cancer, and the problems that

clinicians may face when a positive cfDNA result leads to a negative clinical test.

For the purposes of this study the first patient was included but the second patient was excluded.

7.2 Colonic results.

Screening for colorectal cancer is challenging with the diagnosis of pre-cancerous adenomatous

polyps regarded as essential for the prevention of cancer, and yet they represent a more difficult

diagnostic target.

This study demonstrated highly significant differences between the control population, the

population with adenomatous polyps, and the population with colonic cancer.

Quantities and patterns of circulating DNA differed significantly between the groups, with total

quantities of nuclear and mitochondrial DNA increasing with pathological grade, and fragmentation

patterns changing between normal, adenomatous and cancer populations. Test results showed that

the markers individually acted as good markers of disease and that in combination they provided an

improved test for both polyps and cancer with a ROC = 0.888. To our knowledge this is the first study

to demonstrate significant differences in circulating DNA in colonic polyps and cancer patients and

offers the prospect of improved diagnosis of neoplasia in colorectal cancer screening with a simple

blood test.

However, overlapping results with false positives in normal patients, and false negatives in

neoplastic patients needs some work. The false positive patients with an excess of cfDNA seem likely

to result from initial storage and processing issues, artefactual haemolysis and white cell lysis, or

confounding sub-clinical disease.

117

A stricter approach to sampling, a visual and microscopic assessment for haemolysis and careful

patient questioning with prospective CRP testing to exclude patients with inflammation (CRP>5) 321

may help avoid this. False negative testing may prove more difficult but prospective testing with

limited storage and careful processing without cell separation or filtration may help. Tumour sizes,

pathological differentiation, and proliferation measures were not used in this study and may

influence the results e.g. small and well differentiated tumours with low proliferation rates may

produce low cfDNA levels. Further studies looking at oxidised or methylated 98, 182, 211 cfDNA levels

may help sensitivity and specificity for cancer, with assessment of hypermethylated foetal cfDNA in

the maternal blood circulation showing the possibilities322.

Although total cfDNA levels differed significantly between the polyps and cancer groups within the

polyp population neither size, nor estimated surface area showed significant correlation with cfDNA

in both the total polyp population (p=0.28) and the larger category of LGD polyps (p=0.24). Perhaps

surprisingly there was also no significant difference in total cfDNA levels (p=0.926) or size between

the LGD (52.5mm) and HGD (51.9mm) polyps. Accurate sizing and grading of polyps is difficult with

lateral and outward growth contributing to an overall volume, and small foci of HGD dictate polyp

categories. Therefore although we have found no differences in cfDNA with size or grade of polyp it

is difficult to be entirely confident that they are not relevant to cfDNA levels. It seems reasonable to

group polyps together, and it will be difficult to get a clearer answer.

Whilst a larger population would have been desirable, these results arise in a clinically relevant

population. Results from other studies also provide good support for our findings. A study published

in 2009 in abstract form only, confirmed the significance of the 115bp and 247bp Alu marker

changes in early and late colorectal cancer patients in plasma. However, results in patients with

polyps were not significantly different to the “clean-colon healthy subject” control population 323.

118

No details of sample processing methodology were included and so interpretation of the true

negativity of this study is difficult. Perhaps our results reflects the small fragment cfDNA and spin

protocols used. The polyps in our population were also unusually large and although in our analysis

size was not found to correlate with DNA levels, this may have contributed to our positive findings.

A paper published in 2006 also demonstrated strikingly similar DNA levels to our findings, although

in serum. The study looked at more advanced colon cancers and showed a healthy control group

with an almost identical median total DNA level of 7.7 ng/ml (current study - 6.86 ng/ml). CfDNA

levels in cancer patients was 35.8 ng/ml compared to 14.58 ng/ml) in this study. A combined

diagnostic ROC score discriminating cancer cases was 0.92. 242.

In other cfDNA studies the fragment ratio was the most sensitive diagnostic marker89, 94, 115, 149, 165,

however it is unclear whether DNA integrity of all genes is significant as two studies found negative

results166, 324. In our colonic results there were significant differences in the colonic ALU 115/247bp

fragment ratio, and the ratio results did add to the 4 marker DNA regression analysis. However, the

fragmentation ratio was not the most sensitive marker in our study possibly suggesting that it acts as

a more sensitive marker in advanced cancers.

Mitochondrial DNA, with multiple copies in each cell, has been proposed to provide a more accurate

marker in the analysis of low quantities of circulating DNA. In agreement with this, our colonic

results showed highly significant differences between the groups with a rise in benign and cancer

groups. However, in logistic regression analysis it did not add to the accuracy obtained with nuclear

cfDNA markers, and other studies have reported both high and low levels in cancer325-327. We did not

use it as a marker for our final analysis because of this.

119

cfDNA quantification and Alu 247 fragment results have now been demonstrated to be consistent

across serum studies in both non-inflammatory normal patients, three types of gastro-intestinal

cancers149, 157, 328 and breast cancer115. We believe that they offer the prospect of a broad neoplasia

screening test in non-inflammatory cancers. This approach would be highly attractive to patients,

although greater specificity for cancer type would be required. Differentiating between cancers

however may be possible from other tests, even using the same blood sample. For example, in our

study the 300bp marker did not perform well, but this fragment did show promise in breast cancer147.

The different DNA patterns may help identify gastrointestinal cancers and differentiate them from

other cancer types e.g when the Alu 247 bp fragment is raised. This interesting finding may relate to

the biological handling of cfDNA from the digestive system before it enters the normal circulation329,

330, and may give the Alu 247 bp fragment a degree of specificity for colorectal neoplasia.

Alternatively specificity may be achieved by using additional PCR markers such as methylated Septin

9, or ELISA based assays with Colon Cancer Specific-2, or TIMP 1 proteins177, 187, 268, 278. This may allow

combinations similar to the CEA results in our study offering the prospect of a more specific blood

test screen for colorectal cancer. In our study, CEA did not add greatly to the ROC, and the results

are included to show that this marker, even in combination with others, has little diagnostic value.

Mitochondrial fragments with highly significant results across both studies and high individual ROC

values (0.742, 0.694) may also offer a broad neoplasia screen. Previous studies have shown

significant findings discriminating testicular and breast cancer 173, 212, 326, and mitochondrial cfDNA

also has promise as a universal neoplasia marker.

Alternatively, cfDNA may be a useful triage test for patients with a positive faecal occult blood test.

Those with cfDNA below a threshold defined by the ROC curve could be spared the risk and cost of

colonoscopy

120

In 2010 a study sequenced all circulating cfDNA in breast cancer patients. Alu and line were

significant markers which in combination with others gave sensitivity (90%) and specificity (95%) in

breast cancer patients208. This should give a more complete answer and looks a promising approach

for the future.

7.3 Oesophageal results.

Screening for oesophageal cancer is especially challenging even in those diagnosed with Barrett’s

oesophagus. The diagnosis of pre-cancerous dysplasia and early cancer is essential to improve the

prognosis and represents a very challenging diagnostic target.

This study demonstrates highly significant differences between the control population, a population

with invasive cancers, and a combined population with mucosal neoplasia and invasive cancers.

Quantities and patterns of circulating DNA differ significantly between the groups, with total

quantities of nuclear and mitochondrial DNA increasing with pathological grade, and fragmentation

patterns changing between control and invasive cancer populations.

Although most individual markers are only moderately successful as diagnostic tools, in combination

they provide an improved test. In particular they provide a good test for advanced invasive cancers

(ROC = 0.859), although of course the clinical problem is to detect patients before they reach this

stage.

Comparing cfDNA levels from oesophageal neoplasia patients to those form colorectal patients it is

apparent that all sample levels are broadly lower except for mitochondrial DNA. With at least some

of the blood by-passing the liver circulation this is surprising. Why oesophageal cancers should

produce less cfDNA is unclear. Technically some of the plasma samples were stored for over 2 years

reflecting the relative rarity of mucosal Barrett’s neoplasia samples.

121

A previous study has shown that cfDNA levels decrease with time in storage even at -80 °C331, and

some of the neoplasia samples may have suffered because of this. The Low levels of cfDNA in the

cancer group however remain unexplained. They were collected towards the end of the study and

stored for less than 1 year. As discussed in the colon results above, false positives and false negatives

will need to be addressed. The strategies suggested may also help with the low DNA levels found

with the oesophageal samples.

When the results were analysed without including both normal Barrett’s oesophagus outlier samples,

suspicious for underlying cancers, the mean results for line 1 79 bp, Line 1 300 bp, Alu 115 bp and

Alu 247 bp fragments were all significant at p = 0.001. The ROC value, discriminating controls from

all abnormalities was also 0.809, perhaps showing more clearly the potential for this type of testing.

To our knowledge this is the first study to demonstrate these circulating DNA changes in

oesophageal dysplasia and oesophageal adenocarcinoma, offering the prospect of improved

diagnosis of neoplasia with a simple blood test. While the test lacks specificity for oesophageal

cancer, it could be particularly useful as a triage test for patients with Barrett’ s oesophagus. Those

with cfDNA below a threshold defined by the ROC curve could be spared the risk and cost of

oesophagogastroscopy.

Alternatively specificity for oesophageal adenocarcinoma may be available from other tests, which

could be performed on the same blood sample following an abnormal result. Methylated APC or

LOH at microsatellite markers202, 332 may begin to offer this.

Sequencing and analysis of all cfDNA may be the ultimate diagnostic approach in oesophageal cancer,

giving both sensitivity and specificity. Further studies are required however.

122

Whilst a larger population would be desirable, these results arise in a clinically relevant population

with other results providing good support for our findings. Similar DNA levels in plasma from a study

of more advanced mainly oesophageal adenocarcinomas show a healthy control group with an

almost identical median total DNA level of 10 ng/ml (current study 7.3 ng/ml), and resectable

cancer patients with 25 ng/ml (current study 12.2 ng/ml) 333. In the above study cfDNA was

compared retrospectively to CEA (cut off level of 5 ng/ml) as a detector of recurrent disease, cfDNA

detecting 100% of recurrences and CEA detecting only 33%. CEA continues to show little clinical use.

Mitochondrial DNA in our study rises in dysplastic and cancer groups showing highly significant

differences in the cancer group. In logistic regression analysis it is the best single marker, although

once again it adds little to the 4 DNA marker in common with colon cancers. Mitochondrial DNA,

Total DNA and Line 1 300 fragments are significant in our colonic and oesophageal results as well as

breast cancer. Results in other cancers and our colonic series with the Alu 247 bp suggests these 4

markers offer the best cfDNA prospect of a broad neoplasia screening test in cancers.

The oesophageal fragment ratio results are not significantly different between the groups, which is

disappointing and surprising. A study of squamous oesophageal cancers showed a significant

difference (P=0.001), albeit against healthy controls, and other studies with adenocarcinoma have

also been positive. Perhaps the long DNA storage once again contributed to the loss of DNA

fragments in particular. It is noteworthy however that on removing the 2 outlier cases of normal

Barrett’s oesophagus disease the results showed significant differences between the groups and this

does need to be examined further.

In conclusion cfDNA markers offer an interesting prospect for a blood test screen in oesophageal

cancer, with our results showing the possibilities in early oesophageal cancer and pre-cancerous

dysplasia. We have identified a combination marker in our population able to discriminate control

patients with common medical problems from patients with dysplasia and invasive cancers, though

this requires validation in a larger independent series.

123

7.4 Paired results.

The number of cases with paired samples resected to normal in colonic and oesophageal neoplasia

patients is small, making statistical interpretation difficult.

In colon paired cases there is fall in Alu 247 bp fragment quantity and 115 bp / 247 bp ratio which

although non-significant is similar to a study of patients who responded to radiotherapy for rectal

cancer157. In colonic neoplasia the Line 1 300 bp marker is significantly different in the paired and

non-paired analysis. This appears to confirm its changing level in colonic neoplasia and suggests even

without a second validating series that the statistically significant results are correct.

The oesophageal paired data showed little change, and were not significantly different to normal.

Clinically this reflects the fact that only mucosal disease is resectable and mirrors the results from

the larger unpaired mucosal neoplasia data (P > 0.10). Disappointingly the resected samples did not

return to normal values. Whether this is a sample size issue, a reflection of underlying abnormal

tissue remaining, or whether there is ongoing inflammation post resection is unclear. There are no

studies available to clarify this.

Statistically the Alu 247 bp marker is the only marker approaching significance (P=0.006), but with

the levels rising after resection and confounding current understanding of cfDNA origins this

significance should be treated cautiously.

Graphical analysis of the samples resected shows that nearly all of the Line 1 300bp and Line 1

79/300 bp markers change in similar ways, and these may be helpful in a larger colonic neoplasia

study. (Black lines = specimens resected back to normal, red broken lines = not fully resected to

normal)

124


Figure 44 Paired Line 1 300bp values pre and post resection.

Overall, the colonic and oesophageal paired results are disappointing, and it seems unlikely these

markers will help to judge complete endoscopic removal.

125

7.5 Sample processing.

Within the study timescale, new research and equipment has been developed. Increasing evidence

of the need to collect smaller DNA fragments137, 334 has led to the industry creating new specialist kits

with Quiagen and nucleospin being the first two. With fast, reproducible, and standardized

techniques these kits should be considered the gold standard.

Studies looking at the effects of storage times on plasma samples have shown increased DNA levels

after 2-6 hours, presumably from haemolysis at the time of sampling. Processing of samples in less

than 2 hours is optimal and should prevent genomic DNA contamination153, 308. Haemolysis of

samples is not entirely time related and it is important to avoid this through careful blood sampling.

A double spin protocol to separate cells from plasma without the use of filtration and histopaque is

optimal 153, 309, 310. A further fast spin on thawing plasma prior to separating cfDNA is recommended

by the manufacturer, making a triple spin protocol comprehensive.

DNA degradation occurs at a fast rate even at -80°C. An extended collection period over 2 years is

not recommended331. Testing prospectively is recommended however where this is not possible

storage in optimal buffers at temperatures < -80°C, with minimal freeze thaw cycles is best 210, 335.

7.6 Implication for the origins of circulating DNA.

In control patients the cfDNA level of the Alu 115 bp sequence is much greater than would be

expected. When we observe the results of the Line 1 79 bp product (8-10ng/ml) in comparison to

the Alu 115 bp product (25-35 ng/ml), both measured against the same genomic standard curve, we

see that the Alu product is 3 times more abundant. If cfDNA was to arise from apoptosis and

necrosis of normal cells then the relative quantities of Alu and Line 1 sequences should be 1:1.

Therefore simple apoptosis and necrosis leading to release of cfDNA cannot explain this

phenomenon.

126

Previous comparisons of Alu to β-globin gene sequences in cfDNA have shown similar results and

active secretion of Alu sequences was therefore proposed336. DNA excretion has also been observed

in differing in vitro-experiments and the phenomenon seems likely to play a significant role in cfDNA

generation 337, 338.

Quantities of both Line 1 79 bp and Alu 115 bp cfDNA sequences increase in cancer and pre-

cancerous patients but the reason for this unclear. If as traditionally proposed increased apoptosis

and necrosis of neoplastic cells occurs then the ratio of Alu 115 bp to Line 1 79 bp should be lower or

unchanged. Apoptosis or necrosis of normal cells should reduce the ratio. However, further

increases in the Alu 115 bp : Line 1 79 bp sequence (4:1) in both the oesophageal and colorectal

cancer series suggests that the phenomenon leading to this unexpected ratio increases.

Paired results also demonstrate sudden changes in cfDNA levels in early cancers in the same patient.

This seems unlikely to be secondary to increased apoptosis and necrosis of the tumour or

surrounding normal cells (figure 45).

Figure 45. DNA quantities in a patient with progressive oesophageal cancer.

127

With an oesophageal cancer study showing little change in cfDNA cancer levels until metastatic

disease is present , it is hard to see how increased apoptosis and necrosis can explain cfDNA

levels339.

Perhaps these changes reflect a loss of cellular control as well as active secretion, with increased

retro-transposon activity reflecting and even contributing to the cancer process340, 341 . Line 1 and

Alu sequences are both produced by retro-transposon activity, although only the long Line 1

sequence is able to self replicate. Alu relies on Line 1 activity but is much shorter and therefore

quicker to replicate. Increased retro-transposon activity may explain both the overall increasing

quantities of Alu and Line 1 cfDNA sequences, and the increasing ratio of Alu to Line 1.

Alternatively this may reflect an increased immune response to the cancer cells, with evidence for

white cell activation leading to increasing DNA excretion. However, why the DNA excreted should be

high in Alu sequences is unclear 342.

Finally although a phenomenon such as secretion of DNA is necessary to explain the observed ratios

and types of cfDNA sequences in these experiments, apoptosis and necrosis of neoplastic and

normal cells may still contribute. This complex system would explain the observed increases in

cfDNA, the “ladder phenomenon”, and the changing Alu and Line 1 levels.

7.7 Diagnostic testing.

While there is undoubtedly a rise of cfDNA in cancer patients, current results are not clinically

sensitive or specific enough. Improved processing may help sensitivity and allow a screening

approach to be developed, but the problems raised by lack of specificity for cancer sub-type remain.

Validation of these results in a second population is required. In particular the ability to

prospectively select normal patients from their given test result needs to be proven especially in

light of the confounding presence of inflammatory conditions.

128

If the results prove reliable then a clinical use for this test may arise. The most obvious role for

quantitative cfDNA testing is in screening for cancers, where a negative result may avoid further

expensive and invasive tests. Importantly our results show changes in cfDNA in dysplasia samples

compared to control groups which is an important and new finding, and a pre-requisite for screening

an asymptomatic population. Barrett’s patients in particular with negative results may be able to

avoid the unpleasant bi-annual endoscopy and biopsy.

Identification of new additional markers, or a combination of old and new markers may provide a

more specific and clinically useful test. Perhaps quantitative study of oxidised or hyper-methylated

cfDNA will add sensitivity, or perhaps next generation sequencing techniques looking at all cfDNA

sequences will provide a breakthrough.

7.8 Conclusions.

In conclusion, this study has shown that nuclear and mitochondrial cfDNA sequences are clearly

raised in colonic and oesophageal dysplasia and cancer, which are new and important findings.

We have identified that a combination of markers used in our population could discriminate control

patients with common medical problems from patients with pre-cancerous changes and operable

cancers. The ROC curves show that these markers present a good to excellent discriminating test,

though this requires validation in a larger independent series.

These results demonstrate the possibility of using cfDNA to screen for asymptomatic and

unrecognized cancers using a simple blood test although further specificity or a negative test

approach will be required.

Our results seem incongruous with the established hypothesis that apoptosis and cellular necrosis

causes escape of cfDNA. The exact mechanism is still unclear but perhaps loss or change in the cells

usual control processes allows increased DNA replication and subsequent overspill into the

circulation.

129

Addendum

Reflections after Viva Voce examination

If cfDNA testing is to have a diagnostic or prognostic role it is important to understand the intra-

individual test variability in control and cancer patient cfDNA levels over time. The body has clear

diurnal changes in hormone and metabolic profiles and the processes leading to cfDNA are also likely

to be dynamic. Although in a steady state situation the cfDNA level appears homeostatically

controlled118, 119, perhaps reflecting the situation in the normal population. In cancer patients the

cfDNA levels are clearly changed and therefore homeostatic regulation of cfDNA cannot be assumed.

Studies with repeat sampling at the same point in time and hourly sampling over a day are needed

to further understand this. Work is also needed to understand the best time of day to sample cfDNA.

With these results it should be possible to determine the number and timing of samples required for

testing and the possible variation of individual cfDNA levels.

Finding a role for cfDNA in staging cancers and predicting prognosis is hopeful, in what is a clinically

challenging area. There are some promising studies already published98, 106, 114, 159, 178, 273, 343-346.

However, in common with the diagnostic caveats above, further systematic study of cfDNA patterns

in patients with pre-cancerous changes through to invasive and metastatic disease is required.

Analysis of individual cfDNA levels at multiple time points through the disease stages will be needed

to confirm and validate this. The confirmation of these findings will also serve to validate the cfDNA

diagnostic approach.

Oesophageal dysplasia is a relatively rare finding with agreement on pathological grading and

staging being challenging319, 320. Particularly in this disease longitudinal follow up over 5-10 years will

be important to confirm the initial grade and stage which informed the cfDNA studies. The average

follow up in these patients for this thesis is 276 days and this may not be long enough.

130

Perhaps even before these studies are undertaken work to confirm processing outcomes and

process timings is needed so that standardisation of techniques can occur. There has been some

work towards this153, 309, 310.

131

Example of experimental amplification curves - Line 1 79bp

7-132

Raw data - sample set up line 1 79bp

Block

Type

96fast

Chemistry

SYBR_GREEN

Experiment File Name

C:\Applied Biosystems\7500\experiments\experiments\Line 1 79\experiment results.eds

Experiment Run End Time

2010-03-08 04:58:43 AM GMT

Instrument Type

sds7500fast

Passive Reference

ROX

7-133

Well

Sample

Name Sample Color Target Name Target Color Task Quantity

A1

Line 1 79 RGB(244,165,230) NTC

A2

Line 1 79 RGB(244,165,230) NTC

A3

Line 1 79 RGB(244,165,230) NTC

A4

Line 1 79 RGB(244,165,230) STANDARD 0.25

A5


A6


A7


A8


A9


A10


A11


7-134

A12


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B2


B3


B4


B5


B6


B7 Sample 1 RGB(132,193,241) Line 1 79 RGB(244,165,230) UNKNOWN






7-135

C1 Sample 3 RGB(223,221,142) Line 1 79 RGB(244,165,230) UNKNOWN












D1 Sample 7 RGB(253,138,88) Line 1 79 RGB(244,165,230) UNKNOWN

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E1 Sample 11 RGB(168,255,222) Line 1 79 RGB(244,165,230) UNKNOWN


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F1 Sample 15 RGB(255,204,153) Line 1 79 RGB(244,165,230) UNKNOWN



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G1 Sample 19 RGB(132,193,241) Line 1 79 RGB(244,165,230) UNKNOWN




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H1 Sample 23 RGB(180,255,0) Line 1 79 RGB(244,165,230) UNKNOWN





7-140








7-141

Raw data - results Line 1 79bp

Block Type

96fast

Chemistry

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Experiment File Name

C:\Applied Biosystems\7500\experiments\experiments\Line 1 79.eds

Experiment Run End Time

2010-03-08 04:58:43 AM GMT

Instrument Type

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ROX

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Well

Sample

Name

Target

Name Task Reporter Cт Cт Mean Cт SD Quantity

Quantity

Mean Quantity SD

Automatic

Ct

Threshold

Ct

Threshold

Automatic

Baseline

Baseline

Start

Baseline

End Tm1

A1

Line 1

79 NTC SYBR Undetermined

TRUE 0.035783 TRUE 3 29 71.80394

A2

Line 1


TRUE 0.035783 TRUE 3 29 71.6244

A3

Line 1


TRUE 0.035783 TRUE 3 29 71.6244

A4

Line 1

79 STANDARD SYBR 16.56781197 16.62403 0.13666 0.25

TRUE 0.035783 TRUE 3 14 70.18819

A5

Line 1

79 STANDARD SYBR 16.77983665 16.62403 0.13666 0.25

TRUE 0.035783 TRUE 3 14 70.18819

A6

Line 1

79 STANDARD SYBR 16.52445221 16.62403 0.13666 0.25

TRUE 0.035783 TRUE 3 14 70.36771

A7

Line 1

79 STANDARD SYBR 18.64361763 18.71006 0.057687 0.0625

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A8

Line 1

79 STANDARD SYBR 18.73918533 18.71006 0.057687 0.0625

TRUE 0.035783 TRUE 3 16 70.36771

7-143

A9

Line 1

79 STANDARD SYBR 18.7473793 18.71006 0.057687 0.0625

TRUE 0.035783 TRUE 3 16 70.18819

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Line 1

79 STANDARD SYBR 20.71775818 20.80297 0.077126 0.015625

TRUE 0.035783 TRUE 3 18 70.00866

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Line 1

79 STANDARD SYBR 20.86799622 20.80297 0.077126 0.015625

TRUE 0.035783 TRUE 3 18 70.00866

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Line 1

79 STANDARD SYBR 20.82315445 20.80297 0.077126 0.015625

TRUE 0.035783 TRUE 3 18 70.00866

B1

Line 1

79 STANDARD SYBR 22.94385529 22.85642 0.078897 0.003906

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Line 1

79 STANDARD SYBR 22.79053688 22.85642 0.078897 0.003906

TRUE 0.035783 TRUE 3 20 70.00866

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Line 1

79 STANDARD SYBR 22.83488083 22.85642 0.078897 0.003906

TRUE 0.035783 TRUE 3 20 70.18819

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Line 1

79 STANDARD SYBR 24.94140053 24.93026 0.009655 0.000977

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Line 1

79 STANDARD SYBR 24.92507553 24.93026 0.009655 0.000977

TRUE 0.035783 TRUE 3 22 70.18819

7-144

B6

Line 1

79 STANDARD SYBR 24.92430687 24.93026 0.009655 0.000977

TRUE 0.035783 TRUE 3 23 70.36771

B7

Sample

1

Line 1

79 UNKNOWN SYBR 21.09013176 21.09132 0.007957 0.012741 0.012731317 6.76101E-05 TRUE 0.035783 TRUE 3 19 70.36771

B8

Sample

1

Line 1


B9

Sample

1

Line 1


B10

Sample

2

Line 1

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Sample

2

Line 1


B12

Sample

2

Line 1


C1

Sample

3

Line 1


C2

Sample

3

Line 1


7-145

C3

Sample

3

Line 1


C4

Sample

4

Line 1


C5

Sample

4

Line 1


C6

Sample

4

Line 1


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Sample

5

Line 1


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Sample

5

Line 1


C9

Sample

5

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Sample

6

Line 1


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Sample

6

Line 1


7-146

C12

Sample

6

Line 1


D1

Sample

7

Line 1


D2

Sample

7

Line 1


D3

Sample

7

Line 1


D4

Sample

8

Line 1


D5

Sample

8

Line 1


D6

Sample

8

Line 1


D7

Sample

9

Line 1


D8

Sample

9

Line 1


7-147

D9

Sample

9

Line 1


D10

Sample

10

Line 1


D11

Sample

10

Line 1


D12

Sample

10

Line 1


E1

Sample

11

Line 1


E2

Sample

11

Line 1


E3

Sample

11

Line 1


E4

Sample

12

Line 1


E5

Sample

12

Line 1


7-148

E6

Sample

12

Line 1


E7

Sample

13

Line 1


E8

Sample

13

Line 1


E9

Sample

13

Line 1


E10

Sample

14

Line 1


E11

Sample

14

Line 1


E12

Sample

14

Line 1


F1

Sample

15

Line 1


F2

Sample

15

Line 1


7-149

F3

Sample

15

Line 1


F4

Sample

16

Line 1


F5

Sample

16

Line 1


F6

Sample

16

Line 1


F7

Sample

17

Line 1


F8

Sample

17

Line 1


F9

Sample

17

Line 1


F10

Sample

18

Line 1


F11

Sample

18

Line 1


7-150

F12

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18

Line 1


G1

Sample

19

Line 1


G2

Sample

19

Line 1


G3

Sample

19

Line 1


G4

Sample

20

Line 1


G5

Sample

20

Line 1


G6

Sample

20

Line 1


G7

Sample

21

Line 1


G8

Sample

21

Line 1


7-151

G9

Sample

21

Line 1


G10

Sample

22

Line 1


G11

Sample

22

Line 1


G12

Sample

22

Line 1


H1

Sample

23

Line 1


H2

Sample

23

Line 1


H3

Sample

23

Line 1


H4

Sample

24

Line 1


H5

Sample

24

Line 1


7-152

H6

Sample

24

Line 1


H7

Sample

25

Line 1


H8

Sample

25

Line 1


H9

Sample

25

Line 1


H10

Sample

26

Line 1


H11

Sample

26

Line 1


H12

Sample

26

Line 1


References

153

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December 2012 Circulating DNA markers of Gastro-intestinal … · 2017. 1. 19. · gastro-intestinal cancer biomarker literature reporting the strengths, weaknesses and successes