A comprehensive proteome analysis of hepatitis b virus associated hepatocellular carcinoma

SUMMARY Hepatocellular carcinoma HCC is the most common primary liver cancer with more than half of the cases attributed to persistent viral infection by the hepatitis B or C virus.. Th

A COMPREHENSIVE PROTEOME ANALYSIS OF

HEPATITIS B VIRUS-ASSOCIATED HEPATOCELLULAR CARCINOMA

ZUBAIDAH BTE MOHAMED RAMDZAN

(B Sc (Hons.), NUS)

A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF BIOCHEMISTRY NATIONAL UNIVERSITY OF SINGAPORE

2009

I am also indebted to all the members of the laboratory, endearingly named

“MaxProteomics” which I have since called my second family Dr Sandra Tan for her continuous encouragement readily provided throughout the entire project Dr Lin Qingsong for imparting his knowledge and the late evenings spent patiently explaining technical details A very big thank you to Cynthia, Gek San and Teck Kwang, the best research assistants a student could wish for, for sharing their secrets for the perfect 2-D gel and LC runs I am also grateful to Siaw Ling and Eric for their bioinformatics support and for kindly teaching me the basics

I am deeply heartened by the many friendships forged in the lab, especially to Xuxiao, Vincent and Hendrick for the many brainstorming (bickering) sessions, lab jokes and their ever available support I would also like to thank Hwee Tong and Yihao and ex-labmates, Jason, Justin, Lifang, Jiayi and Hong Qing for their company, friendship, many invaluable help and laughter that we have shared

I would like to acknowledge Dr Lim Seng Gee, for kindly providing the tissue samples for this project I wish to also extend my thanks and gratitude to Shashi, Say Tin and staff

of Proteins and Proteomics Centre for their continuous support and the use of various equipments I am also deeply appreciative of the administrative assistance and numerous professors from the Dept of Biochemistry for their care and concern towards my well-being I am especially grateful to Special Programme in Science as well as Prof Alex Ip, Prof Teo Tian Seng, Dr Kuldip Singh and Prof Michael David (UCSD) for instilling my passion and love for research I am also thankful to the students I had the privilege to mentor for their constant “hows and whys” which kept me on my toes

My friends Fazli, Xinyuan, Daryl, Jumilia, Pei Chin, Siew Ping, Zack, Aaron and Gerard who spent the last many years listening to my complains and sharing my joys I could not have completed this thesis without their encouragements Lastly my family; my father for reminding me that no adversity will take me away from my dreams, my mother for her constant prayers that I will not be swayed from my faith, my sisters: Nazimah, Yasmin and Nisha for their unwavering love (and long distance phone calls) that never fails to cheer me up and in ensuring that I continue to achieve my goals in life

TABLE OF CONTENTS

ACKNOWLEDGEMENTS I

TABLE OF CONTENTS II

SUMMARY X

INDEX OF TABLES XII

INDEX OF FIGURES XIII

INDEX OF ABBREVIATIONS XVIII

1 LITERATRUE REVIEW 1

1.1 Hepatocellular Carcinoma (HCC) 1

1.1.1 Epidemiology 1

1.1.2 Aetiological factors 1

1.1.2.1 Hepatitis B virus 2

1.1.2.2 Hepatitis C virus 4

1.1.2.3 Aflatoxins 5

1.1.2.4 Inherited disorders 6

1.1.3 Morphological changes in Hepatocarcinoma 6

1.1.3.1 Preneoplasia and Dysplasia 9

1.1.3.2 Neoplasia 10

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1.1.4 Staging of HCC 11

1.1.5 Diagnosis and treatment 14

1.1.5.1 Screening tests 15

1.1.5.2 Treatment 16

1.1.5.3 Prevention 17

1.1.6 Common molecular themes in HCC 18

1.1.6.1 Challenges in understanding HCC 18

1.2 “Omics” based biology in Hepatocarcinogenesis 20

1.2.1 Functional genomics in HCC 21

1.2.1.1 Chromosomal instabilities 21

1.2.1.2 Epigenetic alterations 22

1.2.2 Transcriptomics: gene expression profiling in HCC 23

1.2.3 MicroRNAs involvement in HCC 24

1.2.4 Proteomics 25

1.2.4.1 Proteome analysis of HCC cell lines 28

1.2.4.2 Proteomic approaches using animal models 29

1.2.4.2 Proteome analysis of HCC tissues 31

1.2.4.3 Proteome analysis of serological markers for HCC 38

1.3 Current perspective in HCC studies 40

1.3.1 Advantages and limitations of proteomic platforms 41

1.3.1.1 Proposed multiple proteomic approaches 42

2 AIMS OF THE STUDY 44

3 MATERIALS AND METHODS 46

3.1 Materials 46

3.1.1 Hepatocellular Carcinoma Tissues 46

3.1.2 Cell lines 48

3.1.2.1 Cell culture media and reagents 48

3.1.3 Instruments and Equipments 48

3.1.3.1 Isoelectric Focusing (IEF) 48

3.1.3.2 SDS-PAGE 49

3.1.3.3 Liquid Chromatography 49

3.1.3.4 Mass Spectrometry 49

3.1.3.5 Transblotter 49

3.1.3.6 Centrifuges 50

3.1.3.7 Spectrophotometer 50

3.1.3.8 Scanners 50

3.1.4 General Chemicals and Reagents 50

3.1.5 Western Blot reagents 53

3.1.5.1 Antibodies 53

3.1.5.2 Detection System 54

3.1.6 Softwares and Databases 54

3.1.6.1 Image Analysis 54

3.1.6.2 MS Data Analysis 55

3.2 Sample preparation 56

3.2.1 Tissue sample preparation 56

3.2.2 Cell line sample preparation 56

3.2.2.1 Cell culture 56

3.2.2.2 Cell lysate preparation 56

3.3 2-dimensional gel electrophoresis (2-DE) 57

3.3.1 Isoelectric Focusing on IPG (Immobilized pH gradient) Strips 57

3.3.2 Second Dimension Sodium Dodecyl Sulphate – Polyacrylamide Gel Electrophoresis (SDS – PAGE) 58

3.3.3 Vorum Silver Staining 59

3.3.4 Difference Gel Electrophoresis 59

3.3.4.1 Labeling with CyDye Flours 59

3.3.4.2 Protein visualization 63

3.3.4.3 Decyder image analysis 63

3.3.5 In-gel tryptic digestion 64

3.3.6 Mass Spectrometry Analysis and Database Search 65

3.4 Quantitative Proteomics using stable-isotope labeling technologies 66

3.4.1 cleavable Isotope Coded Affinity Tag (cICAT) labeling 66

3.4.2 isobaric Tag for Relative and Absolute Quantification (iTRAQ) labeling 67

3.4.3 Two-Dimensional Liquid Chromatography separation of labeled peptides 67 3.4.4 Mass spectrometry analysis and Database search 68

3.4.4.1 cICAT-labeled samples 69

3.4.4.2 iTRAQ-labeled samples 69

3.4.4.3 Determination of cut-off threshold for fold change 70

3.4.4.4 Estimation of false positive rate to determine cut-off score 71

3.5 Bioinformatics annotation tools 72

3.6 FUBP siRNA transfection 73

3.6.1 Cell proliferation assay 73

3.7 Immunoblotting 74

4 RESULTS 76

4.1 Differential proteome analysis of HCC tissues 76

4.1.1 Difference gel electrophoresis (2-D DIGE) 76

4.1.2 Decyder Analysis and MALDI TOF/TOF MS 78

4.1.2 Stable-isotope labeling techniques 81

4.1.2.1 cICAT coupled to 2-D LC and MALDI TOF/TOF MS 81

4.1.2.2 iTRAQ coupled to 2-D LC and MALDI TOF/TOF MS 83

4.2 Identification of differentially expressed proteins 85

4.2.1 Summary of proteins identified 109

4.2.2 Common proteins identified in different techniques 110

4.3 Protein physiochemical and biological properties 112

4.3.1 Molecular weight and Isoelectric point 112

4.3.2 Hydrophobicity plot 115

4.3.3 Localization and biological functions 117

4.3 Biological functions of regulated proteins 119

4.4 Verification of protein regulation 123

4.4.1 Western blotting 123

4.4.2 Over expression of FUSE binding protein (FUBP) 128

4.4.2.1 Mass spectra of FUSE binding protein (FUBP) 129

4.4.2.2 Over expression of FUSE binding protein (FUBP) 133

4.4.2.3 c-myc validation by western blot 134

4.5 In vitro study on FUBP 135

4.5.1 Optimization of concentration of siRNA 135

4.5.2 Effects of FUBP knockdown on c-myc levels 138

4.5.3 Effects of FUBP knockdown in cell viability 139

5 DISCUSSION 140

5.1 Protein expression of moderately- and poorly-differentiated HCC 140

5.1.1 Alterations of proteins from common pathways 141

5.2 Dysregulation of Metabolic Proteins 142

5.2.1 Glucose metabolism and oxidative phosphorylation 142

5.2.1.1 Alterations in glycolytic pathway 143

5.2.1.2 Over-expression of aldolase A but not liver specific aldolase B 146

5.2.1.3 Elevated levels of alpha-enolase 147

5.2.1.4 Tri-carboxylic acid (TCA) cycle 148

5.2.2 Lipid metabolism 151

5.2.3 Down-regulation of methylation cycle proteins 152

5.3 Oxidative stress in HCC 154

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5.3.1 Perturbation in iron homeostasis results in oxidative stress 154

5.3.2 Inactivation of scavenging mechanisms 155

5.3.3 Elevated heat shock protein and inflammatory response 156

5.3.3.1 Dysregulations of heat shock proteins 156

5.3.3.2 Up-regulation of glucose regulated proteins (GRPs) 157

5.3.3.3 Over expression of S100 proteins in only poorly differentiated HCC 159

5.4 Dysregulation of c-myc associated proteins 161

5.4.1 Central role of c-myc in HBV-associated HCC 161

5.4.2 Differentially expressed proteins governed by c-myc 166

5.4.2.1 Dysregulation of hnRNP protein family 166

5.4.2.2 Over-expression of nucleotide diphosphate kinase 167

5.4.2.3 Nucleophosmin 168

5.4.3 Far-upstream binding proteins (FUBPs) 169

5.4.3.1 Mechanism of FUBPs in regulating c-myc’s expression 169

5.4.3.2 Over-expression of FUBPs 170

5.4.3.3 Loss of FUBPs display preferential response 171

5.4.3.4 Other possible roles of FUBPs 172

6 CONCLUSION 175

REFERENCES 178

APPENDIX I 212

APPENDIX II 217

APPENDIX III 220

LIST OF PUBLICATIONS 226

SUMMARY

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with more than half of the cases attributed to persistent viral infection by the hepatitis B or C virus To date, the exact molecular pathogenesis of HCC remains ambiguous In this study, proteomic based approaches were used to identify protein targets with the aim to

unravel the molecular pathogenesis of HCC Three quantitative approaches, viz, 2-D

DIGE, cICAT and iTRAQ coupled with 2-D liquid chromatography were used to analyze tumour lysates from moderately- and poorly-differentiated HBV-related HCC To our knowledge, this is the first study using 3 proteomic techniques to analyse HCC tissues belonging to two different stages of differentiation

In this study, a total of 163 and 181 proteins were found to be dysregulated in moderately- and poorly-differentiated HCC tissues respectively, among which only 12 proteins were common between all three techniques Disparity among identified proteins was expected as it is known that each method has its inherent bias and limitation A subset of these proteins was also verified using western blots to independently confirm the presence of the proteins identified by 2-D DIGE, cICAT and / or iTRAQ These proteins were further grouped according to their function as annotated by Gene Ontology for the ease of analysis The majority of affected proteins were those involved in metabolism Most significantly, we are able to observe increasing abrogation of these metabolic pathways from moderately- to poorly-differentiated HCC In addition, proteins related to iron homeostasis and defense mechanisms were also shown to be severely impaired These indicate that the tumourigenic liver had lost its ability to perform its basic metabolic and detoxification functions

Among the proteins identified, further analysis was conducted on a novel protein family, far upstream binding proteins (FUBPs) that were identified by 2-D DIGE The over-expression of FUBPs in both stages of HCC are of particular interest due to their transcriptional activity on the oncogene, c-myc Interestingly, a large number of dysregulated proteins identified were also c-myc associated proteins In addition, c-myc was also observed to be elevated in the tissues used in this study It has generally been accepted that c-myc plays an important role in HCC progression, especially in a viral associated carcinogenesis The exact activators and functions of c-myc, however remain poorly understood It is possible that FUBPs over-expression is responsible for elevated c-myc levels Preliminary experiment using FUBP siRNA transfection on Hep3B, a HBV antigen positive liver cell line showed a decrease in cell viability This effect was however not observed in HepG2, a HCC cell line without HBV in the genome We therefore propose that the FUBP family of proteins may be one of the possible upstream players that are involved in HBV-related HCC tumourigenesis

INDEX OF TABLES

Table 1.1: The Okuda staging system for HCC (adopted from Okuda et al., 1985) 12

Table 1.2: The TNM staging system adapted from Sobin et al., 1997 13

Table 1.4: Summary of current HCC literature review that employed proteomic

techniques

35

Table 3.1: Clinical characteristics of patient samples used in this study 47

Table 3.2: DIGE experimental design for moderately-differentiated HCC liver

tissues A total of five paired samples were used For each sample pair

triplicate gels were run, and which are represented by A, B and C

61

Table 3.3: DIGE experimental design for poorly-differentiated HCC liver tissues

A total of seven paired samples were used For each sample pair

triplicate gels were run, and which are represented by A, B and C

62

Table 3.4: Concentration of primary and secondary antibody used 75

Table 4.1 Significantly regulated proteins in moderately-differentiated HCC as

identified by 2-D DIGE, cICAT and iTRAQ are summarized according

to their respective biological functions Expression levels are

summarized by arrows; down and up arrows indicate down-regulation

or up-regulation respectively

86

Table 4.2 Significantly regulated proteins in poorly-differentiated HCC as

identified by 2-D DIGE, cICAT and iTRAQ are summarized according

to their respective biological functions Expression levels are

summarized by arrows; down and up arrows indicate down-regulation

or up-regulation respectively

97

Table 4.3 Total number of proteins identified from each technique 109

Table 4.4 Summary of FUBP1 and FUBP2 protein spots based on DeCyder

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INDEX OF FIGURES

Figure 1.1: Illustration of the chronological sequence of hepatocellular lesions

leading to the development of HCC

8

Figure 1.2: Summary of the various sample, analysis and platforms available in a

proteomic study

27

Figure 4.1: Representative images of 2D-DIGE analysis (A) moderately differentiated

HCC and (B) poorly differentiated HCC tissues

77

Figure 4.2: Differentially expressed protein spots in the 2D gels are marked with

master numbers in (A) moderately-differentiated tumour tissues and (B) poorly-differentiated tumour tissues *Protein spots that are down-regulated are in green and up-regulated proteins are in red

79

Figure 4.3: Representative mass spectra of 60kDa heat shock protein (HSP60)

analyzed by MALDI-TOF/TOF MS upon tryptic digestion (A) MS spectrum with tryptic peptides of HSP60 labelled by the cICAT heavy and

light reagents; arrow indicates ion at m/z = 1864.035 selected for MS/MS

(B) MS/MS spectrum of the peptide AAVEEHIVLGGGCALLR (m/z = 1864.035), where y- and b-ions are denoted along with immonium ions (V, valine; L, leucine); y- and b-fragmentations are also indicated with tilted dotted lines above and below the sequence, respectively

82

Figure 4.4: Representative iTRAQ mass spectra of 60kDa heat shock protein (HSP60)

analyzed by MALDI-TOF/TOF MS upon tryptic digestion (A) MS

spectrum with tryptic peptides of HSP60; arrow indicates ion at m/z =

1503.8 selected for MS/MS (B) MS/MS spectrum of the peptide

NAGVEGSLIVEK (m/z = 1503.8), where y- and b-ions are denoted along

with immonium ions (V, valine; L, leucine); y- and b-fragmentations are also indicated with tilted dotted lines above and below the sequence,

respectively (C) Reporter ion region of MS/MS m/z 1503.8

84

Figure 4.5: Venn diagram of proteins identified from 2-D DIGE, cICAT and iTRAQ

approaches in (A) moderately differentiated HCC and (B) poorly

111

differentiated HCC * denotes the presence of additional 2 proteins identified in 2-D DIGE and iTRAQ techniques but are differentially regulated

Figure 4.6: Scatter plot of molecular weight (kDa) and isoelectric point (pI) of all the

proteins identified by (A) 2-D DIGE; yellow, (B) cICAT; green and (C) iTRAQ ; red

114

Figure 4.7: Plots of the GRAVY values of all the proteins identified via (A) 2-D

DIGE, (B) cICAT (C) iTRAQ Bars in darker grey hue indicate proteins with positive GRAVY scores The numbers in brackets indicate the total number of proteins identified by each technique

116

Figure 4.8: Distribution of the identified proteins according to (A) cellular

localization and (B) biological functions based on GO consortium (A)

moderately-differentiated HCC tissues and (B) poorly-differentiated HCC

tissues

118

Figure 4.9: Distribution of the moderately and poorly differentiated proteins (Proteins

that were identified in more than one technique or with multi-isoforms are counted as a single / unique protein)

119

Figure 4.10: Distribution of the biological functions of the common dysregulated

proteins from moderately- and poorly-differentiated HCC

120

Figure 4.11: Distribution of selected moderately and poorly differentiated proteins

according to KEGG and Gene Ontology biological functions (Grey bars represent proteins that are down-regulated; Black bars represents are up-regulated proteins.)

122

Figure 4.12: Verification of selected proteins using 1D western blot; (A)

down-regulated proteins, (B) up-down-regulated proteins and (C) housekeeping proteins

124

Figure 4.13: Western blot images of nucleotide diphosphate kinase A (NDKA) of 6

representative tissues for moderately- and poorly-differentiated HCC tissue lysates

126

Figure 4.14: Silver colloidal membrane of 1D western blots representative 127

Figure 4.15: Representative image of ImageQuant and DeCyder analysis of (A)

moderately-differentiated HCC – 3 protein spots of FUBP1 977, 985 and

992 with average ratios of 3.65, 1.60 and 1.99 respectively, (B) differentiated HCC – FUBP1 882 and 895 with average ratio of 1.68 and 1.90; FUBP2 681 and 694 with average ratios of 1.86 and 2.34 (N: non-tumour, T: tumour ; Image view and 3-D view obtained from DeCyder)

poorly-129

Figure 4.16 : Representative mass spectra of FUBP1 and FUBP2 analyzed by

MALDI-TOF/TOF MS upon tryptic digestion (A) MS spectrum with tryptic

peptides of FUBP1; arrow indicates ion at m/z = 1336.70 selected for

MS/MS (B) MS/MS spectrum of the peptide IGGNEGIDVPIPR (m/z = 1336.69), where y- and b-ions are denoted along with immonium ions (G, glycine; P, proline; V, valine; I, isoleucine; R, arginine); y- and b-fragmentations are also indicated with tilted dotted lines above and below the sequence, respectively (C) MS spectrum with tryptic peptides of FUBP2; arrow indicates ion at m/z 1184.72 selected for MS/MS and (D) MS/MS spectrum of the peptide IINDLLQSLR (m/z = 1184.72), where y- and b-ions are denoted along with immonium ions (I, isoleucine; L, leucine; Q, glutamine; R, arginine); y- and b-fragmentations are also indicated with tilted dotted lines above and below the sequence, respectively

130

Figure 4.17: Western blot images of FUSE binding protein (FUBP1/2) of the 6

representative tissues for moderately- and poorly-differentiated HCC tissue lysates

133

Figure 4.18: 1-D Western blot images of c-myc of the 6 representative tissues for

moderately- and poorly-differentiated HCC tissue lysates Equal loading

of proteins were confirmed in a colloidal stained membrane (data not shown)

134

Figure 4.19: 1-D Western blot images of FUBP 1 / 2 / 3 and GAPDH on HepG2 whole

cell lysates following addition of 3 different concentration of siRNA, NTC

136

: non-targeting control, GAPDH, FUBP 1 / 2 / 3

Figure 4.20: 1-D Western blot images of FUBP 1 / 2 / 3 and GAPDH on HepG2 whole

cell lysates following addition of 3 different concentration of siRNA, NTC : non-targeting control, GAPDH, FUBP 1 / 2 / 3

137

Figure 4.21: 1-D Western blot images of (A) HepG2 and (B) Hep3B upon FUBP 1 / 2 /

3 siRNA treatment GAPDH levels are used as loading controls

138

Figure 4.22 : Cell viability were measured using absorbance value at 550nM upon

treatment of FUBP siRNA All absorbance values were normalized to the respective non-targeting controls (NTC) X-axis indicates the concentration of siRNA used Grey bars represent HepG2 cells and black bars represent Hep3B cells

139

Figure 5.1 : Diagrammatic representation of glycolysis and the various dysregulated

enzymes Enzymes that were identified in moderately- and differentiated HCC are boxed in blue and red respectively In addition, enzymes that were over-expressed are in red and those that are down-regulated are in black (KHK, ketohexokinase; FBP, fructose-1,6 bisphosphatase; ALDO A / B, aldoalase A / B; TKT, transketolase; PGM, phosphoglucomutase; ENO, alpha-enolase.)

poorly-144

Figure 5.2 : Schematic representation of tri-carboxylic acid (TCA) cycle and

dysregulation of enzymes observed in both stages of HCC Proteins identified in moderately- and poorly-differentiated are in blue and red boxes respectively All the enzymes identified were down-regulated

(ACO1, aconitate dehydrogenase; IDH1, isocitrate dehydrogenase;

SUCLG, succinyl-CoA ligase; SDHA, succinate drhydrogenase; FH, fumarate hydratase)

150

Figure 5.3 : Schematic illustration of the methylation cycle Proteins identified in

moderately- and poorly-differentiated are in blue and red boxes respectively All the enzymes identified were down-regulated (MAT, methionine adonesyltransferase; BHMT, betaine homocysteine N-methyltransferase; Adomet, adenosylmethionine)

152

Figure 5.4: Schematic diagram illustrating the different proteins identified that

interacts with c-myc as annotated by Ingenuity Pathway Analysis (IPA)

Arrows are used to describe increasing expression and inhibitory interactions are described using T-shape connectors Protein-protein bindings are represented by solid grey lines In addition up-regulated proteins that are identified in this study are in red and those down-regulated are in green

164

Figure 6.1: Schematic diagram illustrating the possible mechanisms that may be

involved in the amplification of the proto-oncogene, c-myc and HCC tumourigenesis

177

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INDEX OF ABBREVIATIONS

2-DE Two-dimensional electrophoresis

2-D DIGE Two-dimensional difference gel electrophoresis

Acc No Accession number

aCGH Array-based comparative genomic hybridization

CHCA α-cyano-4-hydroxy-cinnamic acid

cICAT cleavable Isotope-coded affinity tag

CyDye Cyanine fluorescent dye

DMSO Dimethyl sulfoxide

DNA Deoxyribonucleic acid

EDTA Ethylenediaminetetraacetic acid

FAP Familial adenomatous polyposis

FISH Fluorescence in situ hybridization

FUBP Far upstream binding protein

IEF Isoelectric focusing

iTRAQ isobaric Tag for Relative and Absolute Quantitation

IPG Immobilized pH gradient

IPI International Protein Index

KEGG Kyoto Encyclopedia of Genes and Genomes

LC-MS Liquid chromatography-mass spectrometry

LOH Loss of heterozygosity

Mr Relative molecular mass

MS/MS Tandem mass spectrometry

MMTS Methyl methane-thiosulfonate

MTT 3-4,5-dimethylthiazolyl-2,5-diphenyl-tetrazolium bromide

NH4HCO3 Ammonium bicarbonate

PCR Polymerase chain reaction

PBS Phosphate buffered saline

pI Isoelectric point

PTM Post-translational modification

ROS Reactive oxygen species

rpm Revolutions per minute

SAGE Serial analysis of gene expression

SDS-PAGE Sodium dodecyl sulphate-polyacrylamide gel electrophoresis

SELDI Surface enhanced laser desorption and ionization

SEREX Serological analysis of antigens by recombinant expression cloning

SILAC Stable isotope labeling by amino acid in cell culture

siRNA Small interfering RNA

TBS-T Tris buffered saline – Tween

TCEP Tris-(2-carboxyethyl) phosphine

TFA Trifluoroacetic acid

UniProt Universal Protein Resorce database (http://www.expasy.uniprot.org)

death with an estimated one million death annually (Bosch et al., 2004; Cha and DeMatteo,

2005) It has a high fatality ratio with most patients who develop liver cancer dying within a

year (Yuen et al., 2009)

HCC has a wide geographical variability with the highest incidence in developing nations, such as Asia and sub-Saharan Africa, accounting for 80% of new cases (Parkin, 2006; Wong and Ng, 2008) In Southeast Asia, HCC is the second most fatal cancer since the 1970s with a male predominance of 2 to 4 times more than for females (El-Serag and Rudolph, 2007) Over the last 2 decades, there has also been a noticeable increase of HCC in developed countries such as Europe and the United States (El-Serag and Mason, 1999; Zucman-Rossi and Laurent Puig, 2007)

In Singapore, HCC is the fourth most frequently occurring cancer in men with an overall incidence of 18.9 per 100 000 person-year (Singapore Cancer Society) The incidence is

highest among the Chinese population compared to the other ethnic groups (Yuen et al., 2009)

1.1.2 Aetiological factors

The risk factors of HCC have been well established These include persistent hepatitis B

or C virus (HBV or HCV) infection, cirrhosis and aflatoxin B1 which accounts for almost 80%

of all HCC cases (Bosch et al., 2005; Thorgeirsson et al., 2006) Other aetiological factors include severe alcohol abuse leading to cirrhosis (Stickel et al., 2002; Morgan et al., 2004), smoking, as well as abnormal levels of oestrogen and androgen (Seow et al., 2001) In addition,

metabolic abnormalities such as hereditary haemochromatosis (Kowdley, 2004), α-1-antitrypsin defiency, hereditary tyrosinaemia and Wilson’s disease can also lead to HCC

In recent years, diabetes and obesity have also been identified as probable risk factors

for HCC (Polesel et al., 2009) HCC, due to obesity, most likely progresses through steatohepatitis disease to cirrhosis and eventually carcinogenesis (Calle et al., 2003) It should

also be noted that the risk of HCC increases in the event of multiple risk factors

1.1.2.1 Hepatitis B virus

Hepatitis B virus (HBV) is a 3.2kb partially double stranded DNA virus which can cause an acute and chronic inflammatory response from the liver The estimated number of

HBV chronic carrier world-wide is 400 million, of which 75% reside in Asia (Lai et al., 2003)

This incident level correlates strongly with the incidence of HCC HBV is the first human virus proven to cause cancer (Parkin, 2001; Beasley, 2009) In Singapore alone, a third of HCC

patients are HBV positive (Yuen et al., 1999) Further evidence has shown that patients who are

seropositive with chronic HBV infection are about 70-fold more likely to develop HCC (Rabe

et al., 2001) Numerous human HCC studies have also shown integrated HBV DNA sequences and expression of viral proteins from these sites (Matsubara and Tokino, 1990; Su et al., 1998)

HBV infection consists of a replicative phase which results in prolonged cycles of liver cell damage followed by regeneration of the hepatocytes and inflammatory processes This

leads the development of chronic hepatitis, liver fibrosis and cirrhosis that may eventually

develop to HCC (Li et al., 2004; Kao et al., 2005) HBV-related HCC has also been observed in

non-cirrhotic liver background In addition the viral genome has also been identified in the early stages of tumourigenesis, suggesting that its integration precedes HCC development (Brechot, 2004) This implies the possibility that the HBV viral genome may have some intrinsic hepatocarcinogenic properties The genomic alterations that occur as a result of viral integration may therefore be an additional mechanism that causes HCC (Matsubara and Tokino, 1990)

The viral integration is a dynamic process and rearranges as hepatocytes proliferate This integration can cause cis- or trans-activiation and has been observed to affect a variety of

genes involved in proliferation, cell viability and cell signalling (Rabe et al., 2001) Possible

transactivators of the virus include truncated preS2/S, hepatitis B spliced protein and HBV-X

protein (Caselmann, 1995; Brechot, 2004; Tang et al., 2006) At least one of these sequences has been identified in more than 80% of HBV-related HCC (Schluter et al., 1994)

The most studied candidate, HBV-X protein transactivating function was first

hypothesized by Miller and Robinson (1986) In vivo studies have shown that HBV-X is able to

transactivate a large number of promoters related to inflammation and cell proliferation through protein-protein binding This thus improves the cellular environment for further viral replication

(Tang et al., 2006) HBV-X is also able to increase sensitivity to possible carcinogens or directly affect cellular oncogene levels such as c-myc (Yang et al., 2008) or inactivate tumour suppressors such as p53 (Ueda et al., 1995) It can also modulate protein degradation via

proteosome regulation (Brechot, 2004) and stabilization of hypoxia inducible factor (HIF)

(Moon et al., 2004) Elevated levels of HIF have been observed during hypoxia conditions

induced by tumourigenesis Although its direct interactions are not clear, concerted effects of HBV-X and other players such as c-myc and HIF can potentially encourage survival and invasion of tumour cells HBV is the best investigated aetiological agent with overwhelming evidence in numerous animal models and human samples However, the exact mechanism of its malignant transformation has yet to be fully understood

1.1.2.2 Hepatitis C virus

Hepatitis C virus (HCV), a 9.6 kb single stranded RNA virus, with an estimate of 170 million chronically infected individuals worldwide HCV has increasingly been the major cause

of HCC (McGlynn and London, 2005), predominantly in the developed nations such as Europe,

United States and Japan (Bosch et al., 1999) Due to its great sequence heterogeneity, HCV is

classified according to its various subtype, with 1b having the greatest risk of chronic infections resulting in severe liver damage (Purcell, 1997; Colombo, 1999)

HCV also induces liver inflammation followed by a continuous cycle of hepatocyte death and regeneration Analogous to HBV infection, this provides a context of genetic mutations and aberrations Though HCV, made up of RNA, is unable to integrate to its host genomes, it is hypothesized that immune responses against the virus could substantially promote tumourigenesis Studies have also suggested that the HCV RNA and core proteins are

able to impair dendritic cell functions that are important for T-cell activation (Pachiadakis et al.,

2005) and evade immune-mediated cell death by interactions with interferon-α and tumour

necrosis factor-α (Melen et al., 2004; Gale and Foy, 2005)

HCV core proteins, for example, have been shown to interact with MAPK signalling pathways such as ERK, MEK and Raf, to modulate cell proliferation Another protein, NS5A of the HCV, is also able to sequester p53 thereby affecting p53-regulated pathways Studies using mouse models also show that HCV core proteins are able to induce hepatic steatosis as well as reactive oxygen species (ROS) and oxidative stress which may be responsible for HCC

development (Moriya et al., 1998; 2001) All these results provide evidence that viral proteins

are capable of directly inducing tumourigenesis

1.1.2.3 Aflatoxins

Aflatoxins are naturally occurring mycotoxins that are produced by many species of

Aspergillus fungus Aflatoxin contamination in food remains a serious problem and may

account for more than half of the HCC in Africa, China and South-East Asia (Bosch et al.,

2005) High levels of exposure to aflatoxins can result in acute hepatic necrosis, and cirrhosis

and possibly liver carcinoma Aflatoxin B1 (AFB1), produced by Aspergillus flavus and Aspergillus parasiticus, is the most commonly occurring and potent of the aflatoxins (Mc Lean

and Dutton, 1995)

AFB1 is metabolised predominantly in the liver to an AFB1-8,9-exo-epoxide which in turn forms a promutagenic AFB1-N7-guanine DNA adduct This results in a guanine to

thymine transversion mutation (Bressac et al., 1991) Fifty percent of human HCC with high

levels of AFB1 exposure has been shown to harbour a specific point mutation in guanine to thymine of codon 249 (AGG to AGT) in the tumour suppressor, p53 (Moradpour and Blum, 2005; Wild and Montesano, 2009) This loss of p53 function may affect apoptosis and eventually promote carcinogenesis

1.1.2.4 Inherited disorders

Hepatocellular carcinoma may also arise from several inborn errors of metabolism For example hereditary haemochromatosis results in high iron levels in the liver that leads to increased oxidative stress and probable DNA damage Approximately 40-60% of patients with

hemochromatosis will develop HCC (Niederau et al., 1985) Other autosomal recessive diseases

such as α-1 antitrypsin defiency and tyrosinaemia can lead to inflammations and excessive liver

necrosis and regeneration In both cases, about 40% of patients will develop HCC (Eriksson

1985; Cha and DeMatteo, 2005)

It has also been proposed that HCC can be familial, due to occurrences of HCC in children It has also been shown that HBV infected males with a family background of HCC have 84% chance of getting HCC in comparison to just 9% of individuals without a HCC family history Similarly, females have 46% chance of HCC compared to 1% in those with no

HCC family history (Shen et al., 1991) However, due to confounding environmental factors,

the possibility of genetic factors that may contribute to HCC has yet to be defined

1.1.3 Morphological changes in Hepatocarcinoma

Hepatocarcinogenesis, like other cancers are multistage and multifactorial and involves various genetic alterations that ultimately lead to malignant transformation of normal cells However unlike other cancers, notably colorectal cancer, no significant progress has been made

in elucidating the various molecular players, sequence of events and their interactions in HCC This is not surprising considering the heterogeneity of numerous aetiological factors that are implicated in HCC

Despite the complexities presented, the pathogenesis of HCC is believed to develop through a series of possible common events which leads to morphological changes This consists of a preneoplastic phase that may take about 10 – 30 years to develop, dysplasia and eventually neoplasia (Thorgeirsson and Grisham, 2002) These proposed chronological sequence of events that culminated in the development of HCC are separable and can be studied

by well-established histocytological criteria (Fig 1.1)

Figure 1.1 : Illustration of the chronological sequence of the chronological sequence of hepatocellular lesions leading to the development of HCC

8

to the development of HCC

1.1.3.1 Preneoplasia and Dysplasia

Hepatocarcinogenesis is believed to have a long preneoplastic phase This often begins with chronic hepatitis as a result of various insults that leads to liver inflammation The liver is hence exposed not just to the aetiological agents, but pronounced levels of inflammatory cytokines, matrix degrading enzymes and mitogenic growth factors (Brechot, 2004) These sustained chronic inflammation and cytokine induced hepatocyte death results in remodelling of liver matrix and rapid liver regeneration

Formation of excess fibrous connective tissue during its reparative process leads to liver fibrosis Cirrhotic liver is another common phenomenon which is characterized by replacement

of liver tissue by abnormal regenerative nodules surrounded by collagen and scarring It may occur independently or as a result of fibrosis It is an irreversible process which leads to progressive loss of liver functions More than 80% of HCC is formed on a cirrhotic background

(Simonetti et al., 1991) Only a small portion of HCC develops from normal looking liver and

others display fibrosis or steatosis or liver cell dysplasia

Rapid cell proliferation without the proper genetic checks and control mechanisms may also lead to a series of lesions such as dysplastic foci or dysplastic nodules Dysplastic foci consist of abnormal monoclonal hepatocytes that differ from adjacent cells Microscopic examination of the liver is able to differentiate these cells via cytoplasmic staining, nuclear size

or degree of nuclear atypia (Hytiroglou, 2004) Dysplastic foci can be characterized by either

small cell change (SCC) or large cell change (LCC) (Watanabe et al., 1983) SCC bears similar

cytological similarities to HCC with small cell size and a high nucleus to cytoplasm ratio has been suggested to be precancerous

Dysplastic nodules, is another characteristic lesions that can be observed in 20-30% of liver biopsies in HCC-virus associated cirrhosis These nodules, categorized as low or high grade dysplasia, have been established by clinicopatholical studies as precancerous lesions

(Takayama et al., 1990) Both grades of dysplasia can be characterized by abnormal cytological

lesions, such as clear cell changes and nuclear crowding High grade dysplastic nodule manifests nuclear hyperchromasia, mild nuclear contour irregularities with 1.3 – 2 times greater

in cell density as compared to surrounding hepatic tissues (Theise et al., 2002) Structural

changes also include thickening of the trabeculae with up to 3 cells, areas of increased fibrosis and “nodule-in-nodule lesions” which indicates abnormal liver architecture (Hytiroglou, 2004) The subnodules may demonstrate definite features of HCC such as fatty change or iron resistance or higher vascular supply

1.1.3.2 Neoplasia

Pronounced hepatic lesions, dysplastic nodules and continued genomic alterations may result in changes in the molecular pathways that drive the hepatocytes to evolve into a malignant phenotype, leading to the eventual hepatocarcinoma Microscopic examination of tumours describes critical increase in cell density or nuclear crowding of at least 2 times compared to the surrounding hepatocytes (Kojiro, 1998) The nucleus may demonstrate further contour irregularities, prominent nuclei or mitotic figures Tumour cells often arrange in irregular trabeculae forming thicker plates of cells, with clear cell change, fatty accumulation or Mallory bodies and are accompanied by increased vascularisation (Hytiroglou, 2004)

This neoplastic progression of HCC continues to occur in a multi-step histological process that can be broadly classified into well differentiated (where the tumour cells are often

with indistinct margins from the surrounding hepatocytes), followed by moderately differentiated and poorly differentiated The latter being the most severe stage with high vascular networks and metastatic potential Although the exact differences in the molecular events is not clear, these stages of HCC can be differentiated by pathological examinations

1.1.4 Staging of HCC

It is well established that staging systems provide a common language that provides guided patient assessment as well as facilitates the exchange of information in cancer research and clinical trials A perfect staging system will be advantageous in the prognosis, therapeutic interventions and the overall treatment of the disease

The vast heterogeneity in risk factors, development and progression HCC has presented

a unique challenge in creating a prefect staging system The most commonly used staging

systems for HCC is the Okuda system (see Table 1.1) (Okuda et al., 1985; Gannon and Curley,

2008) and the pathological tumour node metastasis (TMN) staging that was adopted by the International Union Against Cancer (UICC) in 1988 (see Table 1.2) (Sobin and Wittekind, 1997)

Table 1.1 : The Okuda staging system for HCC (adapted from Gannon and Curley, 2008)

Table 1.2 : The UICC TNM staging system (adapted from Gannon and Curley, 2008)

Classification Morphology

Primary tumours

TX: Primary tumour cannot be assessed

T0: No evidence of primary tumour

T1: Solitary tumour without vascular invasion

T2: Solitary tumour 2 cm or less in greatest dimension with vascular invasion, OR

multiple tumors limited to one lobe, none greater than 2 cm and without vascular invasion, OR a solitary tumor more than 2 cm in greatest dimension without vascular invasion

T3: Solitary tumor more than 2 cm in greatest dimension with vascular invasion,

OR multiple tumors limited to one lobe, none more than 2 cm with vascular invasion, OR multiple tumors limited to one lobe, any more than 2 cm in greatest dimension, with or without vascular invasion

T4: Multiple tumors in more than one lobe or tumor(s) involve(s) a major branch

of the portal or hepatic vein

Regional Lymph Node

NX Regional lymph nodes cannot be assessed

N0 No regional lymph node metastasis present

N1 Regional lymph node metastasis present

Distant Metastasis

MX Presence of distant metastasis cannot be assessed

M0 No distant metastasis present

M1 Distant metastasis present

Both staging systems have their own weaknesses The Okuda system, for instance, lack sensitivity, while TNM staging on the other hand, relies solely on tumour morphology and does not account for other aspects such as cirrhosis of the liver Both also lack prognostic value for patients that are diagnosed at an earlier asymptomatic stage (Yan and Yan, 2003) Thus in recent years, numerous other staging systems have been introduced by various countries such as

CLIP (Cancer of the Liver Italian Programme) (CLIP investigators, 1998), BCLC (Barcelona

Clinic Liver Cancer staging) (Llovet et al., 1999) and China Classification System (Yan and

Yan, 2003)

To-date, there has been no consensus as to which staging system should be applied to HCC This is mainly due to insufficient knowledge in the development and factors involved in predicting its prognosis These limitations and the lack of proper staging methodology, continues to hamper efforts to not only study HCC but the inability to properly assign the best therapeutic approach to the patients

1.1.5 Diagnosis and treatment

Due to the asymptomatic features during the course of neoplastic development and the lack of reliable biomarkers, it is not surprising that HCC patients are often diagnosed at very late stages The disease thus has very poor prognosis with more than 50% of the patients dying within 1 year and less than 6% have a 5 year survival rate (Hoofnagle, 2004)

1.1.5.1 Screening tests

Successful screening and diagnosis of HCC is complicated by the lack of reliable biomarkers Alpha-fetoprotein (AFP) is the best possible marker today for HCC with sensitivity and specificity that varies in the range of 40 to 60% and 76 to 96% respectively (Spangenberg

et al., 2006) In recent years, a fucosylated AFP (AFP-L3) has been found to be a specific

indicator of poorly-differentiated and unfavourable diagnosis (Tateishi et al., 2006) Most

importantly, AFP levels seem to have a better predictive value in detecting HCC patients without viral hepatitis (94% in non-viral HCC versus 70% in viral-induced HCC) (Spangenberg

et al., 2006)

Another commonly used biomarker is an abnormal prothrombin, des-gamma-carboxy

prothrombin (DCP) (Liebman, 1989; Aoyagi et al., 1996) It is found to be elevated in 62% of HCC patients and 84% of patients with HCC recurrence (Ikoma et al., 2002) DCP is associated with the risk of portal vein invasion in one of the main end-stage observations (Hamamura et al., 2000) Hence it is more commonly used as a diagnostic marker rather than for surveillance

Though a combination of AFP and DCP would markedly improve its sensitivity, its usefulness for early detection of HCC is controversial Other promising biomarkers reported include

Golgi-protein 73 (Kladney et al., 2000; Marrero et al., 2005), glypican-3 (Nakatsura et al., 2003; Capurro et al., 2003), hepatocyte-growth factor (Yamagamim et al., 2002), insulin growth factor 1 (Mazziotti et al., 2002), vascular endothelial growth factor (Poon et al., 2004; Schoenleber et al., 2009) and transforming growth factor β-1 (Song et al., 2002; Yao et al.,

2007) However due to the lack of studies and confounding factors, more work will be needed

to confirm their effectiveness

In addition to tumour markers, other screening strategies used in HCC screening depend

on imaging studies These include ultrasonography, which is reportedly very dependant It is also biased for detection of large tumour masses, which typically indicates late stages of cancer Combined use of AFP and ultrasonograpy has been proposed to potentially

operator-increase detection rates although this may also operator-increase cost and false-positive rates (Zhang et al., 2004) Furthermore, confirmatory diagnosis often requires computed tomography (CT) or

magnetic resonance imaging (MRI) which may also be costly

1.1.5.2 Treatment

To date, the only curative treatments for HCC are surgical resection or liver

transplantation (Poon et al., 2000) However, even for the patients who undergo resection, the recurrence rate can be as high as 50% at 2 years, especially in cirrhotic patients (Belghiti et al., 1991; Figueras et al., 1999) The main cause of recurrence is the development of new tumours

in the remnant liver which are often in a preneoplastic state (Imamura et al., 2003) Hence for

such cases, a liver transplantation is often a better option The scarcity of donor liver continues

to be a universal problem and many succumb to death while waiting due to tumour advancement

In some cases when HCC lesions are unresectable, there is a need for non-surgical treatment Radiofrequency ablation (RFA), for example, utilises a needle with an electrode is placed percutaneously into targeted tumour using ultrasound guidance and tumour tissues are heated to cause necrosis (Gough-Palmer and Gedroyc, 2008) Other treatments include conventional or molecular targeted chemotherapy and radiotherapy

1.1.5.3 Prevention

Since chronic HBV and HCV are amongst the predominant cause of HCC, infection control is a priority in primary prevention of HCC The first nationwide HBV vaccination at birth programme was launched in Taiwan in 1984 (Beasley, 2009) Twenty years after its launch, the number of chronic HBV infections dropped from 10 – 17% before the vaccination programme to 0.7 – 1.7% after its launch This corresponded to a lowered childhood HCC incidence rate (Chang, 1998; Chang, 2009) The study also reported cases of vaccine failure, due to vaccine escape mutants, genetic hyporesponsiveness and poor compliance

Nevertheless, universal vaccination has since been implemented by the World Health Organisation (WHO), especially for high risk population groups such as healthcare workers,

neonates of HBV carrier mothers and patients with immuncompromised states (Zanetti et al.,

2008) HBV vaccination success in preventing HCC will however be better evaluated in several years to come On the other hand, development of an effective vaccination against HCV has been severely hampered by the high genetic variability of the virus HCV spread can however

be better managed through awareness of blood-borne infection and better screening in blood banks (Lavanchy, 2009)

Secondary prevention includes interferon therapy and anti-HBV drugs (oral nucleotides analogs) to prevent deterioration of liver damage by suppressing hepatitis Although interferon effects are still debatable, anti-HBV nucleotides analogs suppresses viral duplication and viral DNA integration into the host genome (Patterson and Angus, 2009) Secondary prevention is also necessary for patients following successful resection to reduce the probability of recurrence

1.1.6 Common molecular themes in HCC

Biological research on HCC to-date had focused on deciphering the various aetiologies

of HCC and the common pathological pathways that preceded tumourigenesis Inflammatory processes and oxidative stress are in particular a common occurring event in viral-induced hepatocarcinogenesis This often is a consequence of the continuous cycle of liver necrosis and regeneration (Farazi and DePinho, 2006)

This continued uncontrolled growth also led to numerous genetic and epigenetic alterations (Thorgeirsson and Grisham, 2002) resulting in numerous chromosomal aberrations

such as loss of heterozygosity and aberrant DNA methylation patterns (Saito et al., 2001; Lin et al., 2001) p53 mutations and inactivation is another event observed in the molecular

pathogenesis of viral- and AFB1-induced HCC (Feitelson et al., 2002; Zhang et al., 2006;

Farazi and DePinho, 2006) Other molecular targets of specific pathways such as cyclin

dependent kinase, oncogenes (cyclin D1, c-myc and c-met) (Fietelson et al., 2002) and several

signalling molecules (MAPK) have also been described in HCC Ability to identify of these common molecular targets holds great importance in understanding the molecular processes involved in HCC and the promise of creating better drugs or preventive measures

1.1.6.1 Challenges in understanding HCC

Despite the continued success of identifying numerous affected genes in tumourigenesis, no “true specific” genetic changes have been identified in HCC (Tannapfe and Wittekind, 2002) The heterogeneity of genetic aberrations as documented by numerous independent HCC studies suggested that distinct molecular players but related genetic pathways are likely to be affected during hepatocarcinogenesis (Thorgeirsson and Grisham, 2002) Thus,