Desmoplastic stromal cells modulate tumour cell behaviour in pancreatic
cancer
Kadaba, Raghunandan
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Desmoplastic stromal cells modulate tumour cell behaviour in pancreatic cancer Mr. Raghunandan Kadaba This thesis is submitted for the degree of Doctor of Medicine (Research) University of London July 2012 (Revised Jan 2013) Supervisor: Mr. Hemant M Kocher QUEEN MARY UNIVERSITY, LONDON BARTS CANCER INSTITUTE CENTRE FOR TUMOUR BIOLOGY BARTS AND THE LONDON SCHOOL OF MEDICINE AND DENTISTRY CHARTERHOUSE SQUARE, LONDON EC1M 6BQ 1 ABSTRACT ......................................................................................................................................................4 ACKNOWLEDGEMENTS ..................................................................................................................................6 LIST OF FIGURES.............................................................................................................................................8 LIST OF TABLES.............................................................................................................................................10 LIST OF ACRONYMS......................................................................................................................................11 LIST OF PRESENTATIONS .............................................................................................................................. 13 1. INTRODUCTION........................................................................................................................................14 1.1 ANATOMY AND PHYSIOLOGY OF THE NORMAL PANCREAS .........................................................................................14 1.2 PANCREATIC NEOPLASMS ..................................................................................................................................15 1.3 EPIDEMIOLOGY ...............................................................................................................................................15 1.4 ENVIRONMENTAL AND GENETIC RISK FACTORS .......................................................................................................16 1.5 PATHOPHYSIOLOGY AND GENETIC ALTERATIONS IN PANCREATIC CANCER .....................................................................18 1.5.1 Pathophysiology..................................................................................................................................18 1.5.2 Genetic alterations..............................................................................................................................20 1.6 PANCREATIC CANCER MICRO-­‐ENVIRONMENT .........................................................................................................26 1.6.1 Pancreatic cancer stroma and tumour-­‐stromal interaction................................................................26 1.6.2 Pancreatic stellate cell ........................................................................................................................27 1.6.3 Pancreatic cancer stem cells ...............................................................................................................38 1.7 PANCREATIC CANCER MODELS ............................................................................................................................41 1.7.1 In vivo mouse models of pancreatic cancer ........................................................................................41 1.7.2 Three dimensional (3D) in-­‐vitro models ..............................................................................................47 1.8 AIM ........................................................................................................................................................ 53 2. MATERIALS AND METHODS...................................................................................................................... 54 2.1 OVERVIEW .....................................................................................................................................................54 2.2 TISSUE CULTURE ..............................................................................................................................................54 2.2.1 Cell lines, media and culture reagents................................................................................................54 2.3 REAGENTS......................................................................................................................................................57 2.3.1 Antibodies ...........................................................................................................................................57 2.4 ORGANOTYPIC CULTURE....................................................................................................................................57 2.5 IMMUNOHISTOCHEMISTRY AND IMMUNOFLUORESCENCE STAINING ...........................................................................61 2.6 LASER CAPTURE MICRO-­‐DISSECTION AND RNA EXTRACTION .....................................................................................61 2.7 GENE EXPRESSION MICROARRAY ANALYSIS ............................................................................................................63 2.8 QUANTITATIVE REAL-­‐TIME PCR (QRT-­‐PCR ) ........................................................................................................65 2.9 PATHWAY ANALYSIS .........................................................................................................................................67 2.10 QUANTIFICATION ..........................................................................................................................................67 2.10.1 Gel length..........................................................................................................................................67 2.10.2 Gel thickness .....................................................................................................................................67 2.10.3 Cancer cell fold change .....................................................................................................................67 2.10.4 Proliferation, apoptosis and invasion................................................................................................68 2.10.5 Ezrin, E-­‐cadherin, β-­‐catenin, PIGR expression...................................................................................70 2.11 STATISTICAL ANALYSIS ....................................................................................................................................74 3. RESULTS -­‐ I ...............................................................................................................................................76 3.1 THE CANCER CELL-­‐STROMAL CELL INTERACTION LEADS TO EXTRA-­‐CELLULAR MATRIX GEL CONTRACTION .............................76 3.2 INCREASE IN STELLATE CELLS CAUSES AN INCREASE IN TOTAL NUMBER OF CANCER CELLS. ................................................76 2 3.3 EFFECTS OF STROMAL CELLS ON CANCER CELL PROLIFERATION, APOPTOSIS AND INVASION ..............................................85 3.4 EFFECT OF STROMAL CELLS ON EXPRESSION OF WELL-­‐VALIDATED MOLECULAR (E-­‐CADHERIN, Β-­‐CATENIN AND EZRIN) CHANGES WITHIN CANCER CELLS ............................................................................................................................................92 3.5 CHANGES IN CANCER CELL GENE EXPRESSION PROFILE IS MEDIATED BY STELLATE CELLS ...................................................97 3.7 POLYMERIC IMMUNOGLOBULIN RECEPTOR..........................................................................................................104 4. DISCUSSION ........................................................................................................................................... 110 4.1 STELLATE CELL – CANCER CELL INTERACTION IN 3D ORGANOTYPIC CULTURE MODEL. ...................................................110 4.2 STELLATE CELLS ALTER THE GENE EXPRESSION PROFILE OF CANCER CELLS ...................................................................115 5. CONCLUSION.......................................................................................................................................... 118 FUTURE WORK........................................................................................................................................... 119 APPENDIX .................................................................................................................................................. 134 APPENDIX TABLE 1: STATISTICAL SUMMARY ANALYSIS FOR INDIVIDUAL OUTCOMES AND CORRESPONDING ‘N’
VALUES ...........................................................................................................................................................134 APPENDIX TABLE 2: 146 PROBES DEMONSTRATING DIFFERENTIALLY EXPRESSED GENES (N=126) ........................................136 APPENDIX FIGURE 1: STR PROFILING OF CAPAN1 CELL LINE .........................................................................................140 APPENDIX FIGURE 2: STR PROFILING OF ASPC1 CELL LINE ...........................................................................................141 APPENDIX FIGURE 3: STR PROFILING OF DECHTERT CELL LINE ....................................................................................142 APPENDIX FIGURE 4: STR PROFILING OF HUVECHTERT CELL LINE ...............................................................................143 3 Abstract Pancreatic ductal adenocarcinoma (PDAC) is characterised by an intense desmoplastic stromal response that can comprise 60 to 80% of tumour volume and has been implicated to be a factor in promoting tumour invasiveness and the poor prognosis associated with this cancer type. It is now well established that pancreatic stellate cells, which are vitamin A storing cells found in the periacinar spaces of the stroma in the normal gland, are primarily responsible for this desmoplastic reaction. Studying the interaction between stellate cells and cancer cells could provide for a better understanding of the disease process. During the evolution of PDAC, the stromal proportion increases from 4% in the normal gland to up to 80%. We hypothesised that there is an optimal proportion of stellate cells and cancer cells that modulates tumour behaviour and we attempted to dissect out this probable ‘tipping point’ for stromal composition upon cancer cell behaviour using a well-­‐established in vitro organotypic culture model of pancreatic cancer. The cancer cell-­‐stromal cell interaction led to extra-­‐cellular matrix contraction and stiffening; and an increase in cancer cell number. The stromal stellate cells conferred a pro-­‐survival and pro-­‐
invasive effect on cancer cells which was most pronounced at a stellate cell proportion of 0.66-­‐0.83. The expression of key molecules involved in EMT and metastasis such as E-­‐Cadherin and β-­‐catenin showed a reduction and this was found to be most significant again at a stellate cell proportion of 0.66-­‐0.83. Stellate cells altered the genetic profile of cancer cells leading to differential expression of genes involved in key cellular pathways such as cell-­‐cycle and proliferation, cell movement and death, cell-­‐cell signalling, and inflammatory response. qRT-­‐PCR confirmed the differential expression of the top differentially expressed genes and protein validation by immunofluorescence staining using PIGR as a candidate molecule confirmed the experimental finings in human PDAC specimens. 4 This study demonstrates that the progressive accumulation of desmoplastic stromal cells has a tumour progressive (pro-­‐survival, pro-­‐invasive) effect on cancer cells in addition to stiffening (contraction) of the extracellular matrix (maximum effect when the stromal cell proportion is 60-­‐
80%). This is mediated through a number of signalling cascades and molecular targets. Dampening this tumour-­‐promoting interaction between cancer and stromal cells by ‘multi-­‐targeting’ agents may allow traditional chemo-­‐ and/or radiotherapy to be effective. 5 Acknowledgements As a clinician, stepping into the world of basic science was undoubtedly a daunting task at the beginning. I would not have achieved what I had set out to, without the help of all the people whom I met during my time at the Centre for Tumour Biology, Barts Cancer Institute. First of all I would like to thank my supervisor Mr. Hemant Kocher for providing continuous guidance, direction and motivation throughout my course; for teaching, providing constant support and inspiration, be it in the laboratory or during my clinical activities. I would like to thank Mr. Satyajit Bhattacharya, lead for the hepatobiliary and pancreatic surgery unit at the Royal London Hospital for giving me the opportunity to pursue this course by finding me creditable and offering the post of Lewin International Fellow in surgical oncology; and for providing excellent surgical training while attending operating theatre sessions. I am grateful to Prof Kairban Hodivaala-­‐Dilke, my co-­‐supervisor whose critical questioning and guidance kept me focussed; and to Prof Ian Hart, recently retired as the head of Centre for Tumour Biology -­‐ his inspirational and endearing personality has left a lasting impression on me. I cannot thank enough my colleagues whose friendship and intellect made my time at the Institute both enjoyable and stimulating. Thanks to Fieke who patiently taught me most of the techniques I was to use in my project and for being a wonderful friend and a great colleague to work with. Thanks to Mo for keeping the mood always light and Stacey for being a very pleasant neighbour; to Abasi and Sab for their friendly banter. I am most grateful for all your support and well wishes. Thanks to our newly joined colleagues Jennifer, Francesco and Elisabeta for helping me out with some of my experiments – I wish you all the best and I am sure you will enjoy your time at the institute. Special thanks to colleagues from other teams in the lab – Mike, Sally, George, Bernardo, Sylvia, Tanguy who were always ready to help when I needed it. 6 Then, to my parents, whom I will always be grateful, for the affection and support they have given me all through my life and career. And to my wife Vidya, whose unconditional love, support and understanding has made this journey all the more pleasant. Thank you. 7 List of Figures Figure 1.1 Figure 1.2 Figure 1.3 Pancreatic cancer "Paninogram" showing genetic alterations Pancreatic cancer micro-­‐environment Schematic illustration depicting the mediation and regulation of stellate cell function Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure 3.1 Organotypic culture model 51 Submerged organotypic model used for laser capture microdissection 52 Formula to calculate total number of cancer cells per gel 60 Quantification of proliferation in organotypic gels 61 Quantification of apoptosis in organotypic gels 61 Quantification of invasion in organotypic gels Increasing the proportion of stellate cells in organotypic 3D co-­‐culture caused 69 extra-­‐cellular matrix gel contraction (Capan1/PS1 ratios) Figure 3.2 Increasing the proportion of stellate cells in organotypic 3D co-­‐culture caused extra-­‐cellular matrix gel contraction (AsPc1/PS1 ratios 71 Figure 3.3 Figure 3.4 H&E sections of PS1 only and DEChTERT/PS1 organotypics H&E sections of organotypics consisting of Capan1/PS1 cells in specific ratios 72 73 Figure 3.5 H&E stain of organotypic sections consisting of AsPc1 and PS1 cells in specific ratios 73 Figure 3.6 Box and whisker plot of raw data for gel length and fractional polynomial regression graph 74 Figure 3.7 Box and whisker plot of raw data for gel thickness and fractional polynomial regression graph 75 Figure 3.8 Box and whisker plot of raw data for cancer cell fold change and fractional polynomial regression graph 76 Figure 3.9 Figure 3.10 Stellate cells have pro-survival effect on cancer cells.
78 79 Figure 3.11 Figure 3.12 Stellate cells have an anti-apoptotic effect on cancer cells.
Figure 3.13 Figure 3.14 Figure 3.15 Stellate cells have pro-invasive effect on cancer cells
Figure 3.16 Figure 3.17 Figure 3.18 Figure 3.19 Figure 3.20 Quantification of E-­‐Cadherin expression Quantification of β-­‐catenin expression Quantification of ezrin expression RNA sample quality determination Heat map of Gene-­‐expression micro-­‐array analysis in pancreatic and skin cancer models 86 87 88 91 92 Figure 3.21 Heat map of Gene-­‐expression micro-­‐array analysis in oesophageal cancer model 93 Box and whisker plot of raw data for proliferation quantification and fractional polynomial regression graph Box and whisker plot of raw data for apoptosis quantification and fractional polynomial regression graph Quantification of single cell and cohort invasion Expression of Ecadherin, β-catenin and Ezrin in cancer cells on exposure to
stellate cells
17 23 35 80 81 82 83 85 8 Figure 3.22 Ingenuity pathway analysis of differentially expresed genes in pancreatic, skin and oesophageal cancer models 94 Figure 3.23 Correlation plot of differentially expressed genes in pancreatic and skin cancer
subtypes
95 Figure 3.24 Figure 3.25 qRT-­‐PCR validation of topmost differentially expressed genes Immunofluorescence co-­‐staining of organotypics for PIGR and E-­‐Cadherin Figure 3.26 Figure 3.27 Quantification of PIGR expression Immunofluorescence co-­‐staining of human PDAC specimens for PIGR and E-­‐
Cadherin 100 101 102 9 List of Tables Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 1.5 Table 1.6 Table 1.7 Table 1.8 Table 1.9 Table 1.10 Table 1.11 Table 1.12 Table 1.13 Table 2.1 Table 2.2 Table 2.3 Classification and incidence of pancreatic neoplasms Pancreatic cancer associated genetic syndromes Precursor lesions in pancreatic cancer Selected genes that are altered in pancreatic cancer Core signalling pathways and processes altered in pancreatic cancer Characterisation of quiscent and active stellate cells List of genes differentially expressed between HSCs and PSCs Summary of mediators influencing Panreatic stellate cell (PSC) function List of stellate cell / stromal targeting agents Mouse models of pancreatic cancer Types of organotypic 3D models Types of 3D models used in the study of different organs Key advantages and limitations of 3D models List of Antibodies used for the experiments Ratios of cancer cell and PS1 cells on pancreatic organotypic gels List of topmost differentially expressed genes and their primer sequences for qRT-­‐PCR 11 13 15 18 21 25 27 31 35 42 43 44 46 50 52 58 Table 3.1 Appendix table 1 Samples of extracted RNA used for microarray analysis Statistical summary analysis for individual outcomes and corresponding ‘n’ values 93 128 Appendix table 2 List of differentially expressed genes 132 10 List of Acronyms APC
ATRA
BSA
CAF
CCK
CDK
COX-2
CSC
DAB
DAPI
DEPC
DMEM
DMSO
ECM
EGF
EMMPRIN
EMT
ESA
FBS
FGF
GEMM
GEO
GFAP
HCC
HSC
IGF
IL
IPA
IPMN
LCM
LOH
MAP
MCN
MMP
PanIN
PBS
PCR
PDAC
PDGF
PEN
PENK
PIGR
PSC
qRT-PCR
RPMI
Adenomatous polyposis coli
All trans retinoic acid
bovine serum albumin
Carcinoma Associated Fibroblast
cholecystokynin
Cyclin dependent Kinase
Cyclo oxygenase-2
Cancer stem cell
3,3-diaminobenzidine tetrahydrochloride
4',6-diamidino-2-phenylindole
Diethylpyrocarbonate
Dulbecco's modified Eagle Medium
DiMethyl Sulphoxide
Extra cellular matrix
Epidermal Growth Factor
Extracellular matrix metalloproteinase inducer
Epithelial Mesenchymal Transition
Epithelial-specific antigen
Foetal bovine serum
Fibroblast Growth Factor
Genetically Engineered Mouse Model
Gene Expression Omnibus
Glial Fibrillary Acidic Protein
Hepatocellular Carcinoma
Hepatic Stellate cell
Insulin like Growth Factor
Interleukin
Ingenuity Pathway Analysis
Intraductal papillary mucinous neoplasm
LaserCapture Microscopy
Loss of Heterozygosity
Mitogen activated protein
Mucinous cystic neoplasm
Matrix Metalloproteinase
Pancreatic intra-epithelial neoplasm
Phosphate Buffered Saline
Polymerase Chain Reaction
Pancreatic Ductal Adenocarcinoma
Platelet Derived Growth Factor
Polyethylene naphthalate
preproenkephalin
polymeric immunoglobulin receptor
Pancreatic stellate cell
Quantitative real-time PCR
Roswell Park Memorial Institute
11 SHH
SMA
TGF
TIMP
TNF
TOP2A
VIP
sonic hedgehog
Smoth Muscle Actin
Transforming Growth Factor
Tissue Inhibitor of Metalloproteinase
Tumour Necrosis Factor
Topoisomerase 2 alpha
Vasoactive intestinal polypeptide
12 List of presentations •
The proportion of pancreatic stellate cells plays a critical role in modulating pancreatic cancer cell behaviour (Poster presentation). June 2012; AACR special conference – Pancreatic cancer: Progress and Challenges, California, USA •
The ratio of pancreatic stellate cells to pancreatic cancer cells plays a critical role in modulating carcinoma cell behaviour (Poster presentation). November 2012; NCRI conference, Liverpool, UK •
The ratio of pancreatic stellate cells to pancreatic cancer cells plays a critical role in modulating carcinoma cell behaviour (poster of distinction). May 2011; Digestive Diseases Week (DDW), Chicago, Illinois, USA •
The ratio of pancreatic stellate cells to pancreatic cancer cells plays a critical role in modulating carcinoma cell behaviour (Oral presentation). Pancreatic Society of Great Britain and Ireland annual scientific meet, Birmingham, UK 13 1. Introduction 1.1 Anatomy and physiology of the normal pancreas The pancreas gland is an elongated organ, situated in the upper part of the abdominal cavity behind the stomach and extends in an oblique fashion. It is covered by a connective tissue capsule which invaginates into the gland and divides it into lobules. The main bulk of the gland consists of exocrine secretory cells which are arranged in clusters called acini. The enzyme rich secretions from the acinar cells drain into the lumen of the acinus and subsequently into series of ducts (intercalated, intralobular and interlobular) which coalesce to form the main pancreatic duct and eventually drains into the first part of the duodenum. The ductal cells secrete bicarbonate rich fluid, and, are initially cuboidal, progressively becoming tall and columnar in the larger ducts. Embedded within the substance of the gland are islands of cells (islets of Langerhans) which secrete hormones and form the endocrine part of the gland. These islets consists of α cells which secrete the hormone glucagon, β cells which secrete insulin, δ cells which secrete somatostatin, δ2 cells that secrete vasoactive intestinal polypeptide (VIP) and the PP cells that secrete pancreatic polypeptide (4). Functionally, the exocrine portion of the gland secretes enzymes such as proteases (trypsin, chymotrypsin, and carboxypeptidase), amylase (pancreatic amylase), lipase (pancreatic lipase) and nucleases which help in the digestion of proteins, carbohydrate, lipids and nucleic acids respectively. The ductal cells mainly secrete bicarbonate rich fluid that helps to neutralise the acidic contents entering into the duodenum from the stomach, while the acinar cells secrete enzyme rich fluid. Pancreatic enzymes are secreted in their inactive form as zymogen granules (trypsinogen, chymotrypsinogen) which get activated after entering the duodenum by activating enzymes such as enterokinase. 14 1.2 Pancreatic neoplasms Pancreatic neoplasms can be classified based on the gross pathological appearance (solid, cystic, intraductal) or based on the lines of cellular differentiation (ductal, acinar, and endocrine) (Table 1.1). By far the most common type of pancreatic neoplasm is the infiltrating ductal adenocarcinoma (Pancreatic Ductal Adenocarcinoma or PDAC)(5). Neoplasm Incidence (%) Ductal adenocarcinoma 85 – 90% Serous cystadenoma 1-­‐2 Mucinous cystic neoplasm 1-­‐2 Intraductal papillary mucinous neoplasm 3-­‐5 Acinar cell carcinoma 1-­‐2 Pancreatoblastoma <1 Pancreatic endocrine neoplasm 3-­‐4 Solid-­‐pseudopapillary neoplasm 1-­‐2 Table 1.1. Classification and incidence of pancreatic neoplasms(5) 1.3 Epidemiology Cancer of the pancreas is generally regarded as a disease with an extremely poor outcome. It is the tenth most common cancer with a crude incidence rate of about 13 cases per 100,000 population in the United Kingdom(6). Incidence rates increase with increasing age with rates of about 9 per 100,000 in patients aged 50-­‐54 to about 57 per 100,000 in the age group of 70-­‐
75 with a slight male preponderance(7). However, it is the fourth leading cause of cancer-­‐related death in the western world with mortality rates closely following incidence rates. Five-­‐year survival rates are one of the lowest for pancreatic cancer, ranging from 20% for localised disease to about 3% in advanced disease with distant metastases(6). Majority of the patients have advanced disease at diagnosis and curative surgery is only possible in 15-­‐20% of the patients. Median survival after 15 surgical resection is about 11 to 20 months and five-­‐year survival ranges from 7-­‐25%(8). Analysis of age-­‐standardised mortality rates around the world has shown a slightly decreasing trend in some parts of the world (Europe, North America), whereas there was an increase in mortality rates in regions of southern Europe and North-­‐east Asia(9). 1.4 Environmental and genetic risk factors A number of environmental risk factors have been associated with the causation of pancreatic cancer including smoking, chronic pancreatitis, diabetes mellitus and dietary factors. Of these, smoking has been shown to have a strong and consistent association and is a suspected aetiological factor for 20-­‐30% of all pancreatic cancers. A recent meta-­‐analysis showed that in cigarette smokers, there was a 75% increased risk of developing pancreatic cancer when compared to non-­‐smokers (relative risk of 1.74 (95% CI 1.61-­‐1.87)). This risk persisted for up to ten years after cessation of smoking(10). Definite association between different types of pancreatitis (chronic, hereditary, tropical pancreatitis) and pancreatic cancer has been demonstrated in various case-­‐control (11-­‐13) and cohort studies (14-­‐18). A meta-­‐analysis of the relevant studies showed that the relative risk for developing pancreatic cancer on the background of pancreatitis ranged from 13.3 (95% CI 6.1–28.9) for chronic pancreatitis to 69 (95% CI 56.4–84.4) for hereditary pancreatitis(19). Studies have also shown a link between diabetes and pancreatic cancer. A meta-­‐analysis of 20 epidemiological studies in 1995 (20) postulated that the pooled relative risk was 2.1 (95% CI 1.6 – 2.8) for diabetes with a duration of at least one year prior to cancer diagnosis or death and 2.0 (95% CI 1.2 – 3.2) for diabetes with a duration at least 5 years. Other risk factors that have been implicated include body mass index and obesity(21, 22), low intake of fruit and vegetable(23, 24) and reduced folate intake(25). However, these studies have been conflicting and inconsistent and therefore attributable or relative risk cannot be ascertained. In addition to the above, pancreatic cancer also has a known familial risk factor which comprises 5-­‐10% of all pancreatic cancers(26). A pooled analysis of 16 prospective cohort studies showed an increased risk of first-­‐degree relatives with pancreatic cancer (odds ratio 1.75, 95% CI 1.19-­‐2.61)(27). A number of hereditary syndromes with germline mutations have been associated with increased risk of pancreatic cancer (Table 1.2.) Genetic syndrome Genes Cancer risk –
fold Histopathologic features Hereditary breast and BRCA 1 ovarian cancer syndrome BRCA 2 3.5–10 Ductal adenocarcinomca Peutz-­‐Jeghers syndrome STK11/LKB1 132 Intraductal papillary mucinous neoplasm Hereditary pancreatitis PRSS1 53 Ductal adenocarcinomca Increased Medullary carcinoma Familial atypical multiple CDKN2A mole melanoma 13–22 Ductal adenocarcinomca Familial polyposis Up to 4 Ductal adenocarcinoma 2 SPINK1 Hereditary non-­‐polyposis colorectal cancer syndrome Mismatch repair genes -­‐ hMSH2, hMLH1, hPMS1, hPMS2, and hMSH6/GTBP adenomatous APC Intraductal papillary mucinous neoplasm Pancreatoblastoma Familial pancreatic cancer Palladin, and other as 9-­‐32 yet Unknown genes Table 1.2: Pancreatic cancer associated genetic syndromes. Adapted from Shi C, Hruban RH,Klein AP. Familial pancreatic cancer. Arch Pathol Lab Med 2009;133:365-­‐74(26) 17 1.5 Pathophysiology and genetic alterations in pancreatic cancer 1.5.1 Pathophysiology Pancreatic adenocarcinoma harbours a cellular phenotype of ductal cell. However, the cell of origin has not been defined. In theory, PDAC may arise from a poorly differentiated ductal cell, a de-­‐differentiated acinar or islet cell, a progenitor cell, or a stem cell(28). Invasive ductal adenocarcinoma develops from three histologically distinct precursor (pre-­‐neoplastic) lesions termed pancreatic intra-­‐epithelial neoplasia or PanIN. Other lesions of the pancreas whixh predispose to development of invasive PDAC include intra-­‐ductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) (table 1.3). PanINs are microscopic lesions which arise in smaller pancreatic ducts usually less than 0.5cm in size(29). Their prevalence increases with increasing age and are more common in the head than in the tail of the pancreas(30). They are classified morphologically into three types. PanIN 1 lesions are composed of columnar epithelial cells with basally oriented nuclei and can be flat (PanIN 1A) or papillary (PanIN 1B). PanIN 2 lesions are slightly more complex and have more nuclear changes such as loss of nuclear polarity, nuclear crowding, and variations in size, hyperchromasia and nuclear pseudostratification. PanIN 3 lesions show the highest form of dysplasia and are architecturally complex, forming papillae and cribriform structures. The nuclei are enlarged, pleomorphic, and form prominent mitotic figures(29). It is difficult to assess the frequency and duration with which PanINs progress to invasive carcinoma. Based on the estimate of the prevalence of PanIN lesions and known prevalence of pancreatic cancer, it has been proposed that there is a probability of 1% for a single PanIN lesion to develop into invasive carcinoma(31). Intraductal papillary mucinous neoplasms (IPMN) are larger than PanINs and predominantly involve the larger pancreatic ducts (main or interlobular ducts) and tend to have longer and more mucinous papillae than PanINs. They tend to arise in the head more frequently than in the tail and can be classified grossly based on the type of ducts involved into “main-­‐duct 18 type” and “branch-­‐duct type”. They can also be categorised based on the degree of cytological differentiation in to IPMN with low-­‐grade, IPMN with moderate dysplasia and IPMN high grade dysplasia (carcinoma-­‐in-­‐situ)(32). Approximately one-­‐third of IPMNs have invasive adenocarcinoma, however, there are significant differences in the prevalence of invasive cancer in main duct IPMN and branch duct IPMN ranging from 57 to 92% and 6-­‐46% respectively(33, 34). Most lesions are more than 1cm and can be readily detected on radiographic imaging, in contrast to the smaller PanINs(29). Mucinous cystic neoplasms (MCNs) are defined as mucin-­‐producing, cyst forming epithelial neoplasms of the pancreas with a distinctive ovarian-­‐type stroma in contrast to PanINs or IPMNs(30). Most MCNs arise in the body or tail of the pancreas, usually measure 1-­‐3cm in size and do not communicate with larger pancreatic ducts. Similar to IPMN they can be classified into MCNs with low, moderate or severe dysplasia and about one-­‐third are associated with invasive carcinoma(30). Feature Pancreatic Intraepithelial Neoplasia Intraductal Mucinous Cystic Papillary Mucinous Neoplasm Neoplasm Predominant Age Increases with age In the 60's 40-­‐50 years Gender Male=Female Male>female Female=male Head vs. body/tail Head >Body/Tail Head Body/tail Relation of the cysts to large ducts N/A Always connected Usually not connected Cyst Contents N/A Mucoid Mucoid Mucin oozing from ampulla No Yes No Stroma Collagen-­‐rich Collagen-­‐rich Ovarian-­‐type Multifocal disease Often In 20-­‐30% Very rare Table 1.3. Common lesions in the pancreas which prediscpose to pancreatic ductal adenocarcinomas. Adapted from Hruban, R.H., et al., Precursors to pancreatic cancer. Gastroenterol Clin North Am 2007;36:831-­‐49(30). 19 1.5.2 Genetic alterations Genetic and molecular analysis has shown that most of the genetic alterations identified in invasive PDAC are also found in precursor lesions suggesting that invasive cancer is a result of progressive accumulation of distinct genetic events (Figure 1.1)(35). Table 1.4 lists a selected set of genes that are altered in PDAC. Activating point mutations in KRAS oncogene is found in approximately 90-­‐95% of cases of PDAC(36). KRAS oncogene encodes a member of RAS family of GTP-­‐binding proteins that mediate a number of cellular functions like proliferation, survival, cytoskeletal re-­‐modelling, and motility. The active protein is bound to GTP and the intrinsic GTPase activity help to attenuate and, hence, regulate KRAS activity by dephosphorylation of GTP bound state. In the presence of activating mutations of KRAS, the protein remains in the active GTP bound state and constitutively executes down-­‐stream effects. Mutations of the KRAS gene are one of the earliest genetic abnormalities observed in the progression model of pancreatic cancer, demonstrable in approximately 10-­‐30% of PanIN-­‐1, 45% of PanIN-­‐2 and 85% of PanIN-­‐3 lesions(37). Inactivating mutations of tumour suppressor genes are also found in later stages of pancreatic cancer and the most common, found in 85-­‐90% of cases is the cell cycle regulator gene p16/CDKN2A(38). The encoded protein is a member of cyclin dependent kinase (CDK) inhibitor family and inhibits cell cycle progression through G1-­‐S transition, and hence loss of this gene leads to unchecked proliferation. Inactivating mutations of TP53 tumour suppressor gene are seen in about 50-­‐75% of PDACs. The p53 protein regulates cell cycle (G1-­‐S transition, maintenance of G2-­‐M arrest, and induction of apoptosis)(38). Mutations in SMAD4 gene are also seen in approximately 55% of cases. The Smad4 protein plays a critical role via the TGF-­‐β pathway, which in normal conditions has a growth inhibitory effect. Hence loss or inactivation of SMAD4 leads to a growth advantage(39). 20 Fig ure 1.1. A “paninogram” illustrating the various genetic and molecular alterations in precursor lesions of pancreatic cancer. [Modified from Feldmann G, Beaty R, Hruban RH, Maitra A. Molecular genetics of pancreatic intraepithelial neoplasia. J Hepatobiliary Pancreat Surg. 2007;14(3):224-­‐32. (35) 21 Gene Genetic altercation Chromosome site Known or predicted function CDKN2A/p16 KRAS2 Inactivation 9p21 Activation 12p12.1 TP53 Inactivation 17p13.1 SMAD4/DPC4 Inactivation 18q21 Cyclin dependent kinase inhibitor Signal transduction, proliferation, cell survival, and motility Cell cycle arrest, apoptosis, senescence, DNA repair, metabolism change Signal transmission AKT2 Activation 19q13.1–q13.2 MLH1 Inactivation 3p21.3 BRCA2 Inactivation 13q12.3 STK11/LKB1 Inactivation 19p13.3 BRAF Activation 7q34 TGFBR2 Inactivation MAP2K4 Inactivation Alteration in primary PDAC, % 95 >90% 50-­‐70 55 AKT pathway, 10-­‐20 hormone metabolism DNA mismatch repair 3-­‐15 DNA repair, proliferation, differentiation Apoptosis regulation 7 5 3p22 Signal transduction, cell growth Signal transduction 17p11 MAPK pathway 2 5 4 Table 1.4: List of selected genes that are altered in pancreatic cancer. Adapted from Hong SM, Park JY, Hruban RH, Goggins M. Molecular signatures of pancreatic cancer. Arch Pathol Lab Med. 2011 Jun;135(6):716-­‐27 (40) Genomic instability and telomere alterations have also been shown to be involved in early precursor lesions in pancreatic cancer (35).Chromosomal aberrations detected by allelotyping of PanIN lesions have shown multiple regions with high frequency of LOH (loss of heterozygosity), particularly on chromosomes 9p, 18q, and 17p, which are regions commonly altered in invasive 22 ductal adenocarcinoma(41, 42). Telomere length abnormalities are one of the earliest genetic abnormalities demonstrated during pancreatic cancer progression and >90% of early PanIN lesions show telomere shortening when compared to normal ductal epithelium(43). It has been hypothesised that telomere shortening is associated with an increased susceptibility to chromosomal aberrations, the accumulation of which ultimately leads to frank invasive cancer(35). In addition to genetic abnormalities, epigenetic aberrations have also been described in PDAC. The main epigenetic mechanisms that affect gene expression are DNA methylation, histone modification and microRNA expression (40, 44). The tumour suppressor gene CDKN2A/p16 has been shown to undergo DNA hypermethylation and silencing in pancreatic cancer (45). Other genes that are hypermethylated in pancreatic cancer include MLH1 associated with microsatellite instability(46), CDH1 which encodes for E-­‐cadherin; and SPARC which affects cellular migration, proliferation, angiogenesis, cell-­‐matrix adhesion and tissue remodelling (47). In one genome wide study, the gene PENK (preproenkephalin) was found to be aberrantly methylated in >90% of pancreatic cancers (46, 48). Conversely, DNA hypomethylation which can result in loss of genetic regulation and over expression has also been described in PDAC. A few of the genes over expressed by this mechanism include S100A4, CLDN4, LCN2, TFF2, S100P and SERPIN85 (49, 50). Genes of the mucin family such as MUC1, MUC2, MUC4 have been shown to undergo histone alterations associated with over expression in pancreatic cancer (44). MicroRNAs (miRNAs) are a recently described family of small non-­‐protein coding RNA molecules that regulate expression of target messenger RNAs and more than 400 microRNAs have been described that are involved in regulating cellular differentiation, proliferation and apoptosis (51). miRNAs are believed to function primarily as negative regulators of gene expression following binding to conserved sequences within the 3′ untranslated region of target mRNAs (52). PDACs have been shown to aberrantly express number of miRNAs such as miR-­‐200, miR-­‐34, miR-­‐21, miR-­‐155, miR-­‐221, and miR-­‐222 (53-­‐55). Aberrant expression of some of these micro-­‐RNAs are also evident in PanINs such as miR-­‐155 has been shown to be over-­‐expressed in PanIN II and aberrant miR-­‐21 expression is evident in PanIN III lesions (56). 23 These microRNAs are believed to maintain cellular fate in a manner similar to transcription factors (57) by regulating developmental timing and differentiation (58). There is also growing evidence in favour of re-­‐activation of developmental signalling pathways such as the Hedgehog (59) and Notch pathway (60)which have critical role in embryological development and are found to be de-­‐regulated in pancreatic cancer. Of the hedgehog (Hh) ligands, activation of the sonic hedgehog (SHH) has been identified as a key mediator in the tumour desmoplasia of PDAC (61). Binding of SHH ligands to the patched1 receptor relieves suppression of the 12-­‐transmembrane domain protein Smoothened (SMO), resulting in activation of the Gli family of transcription factors. SHH has been shown to be overexpressed in neoplastic cells of human pancreatic tumours (59). Notch signalling, in the mature pancreas, is active principally in the centroacinar cells, located at the junction of acini and ducts (60). Expression of Notch receptor, ligands, and gene targets is elevated in PanIN lesions as well as in invasive cancer (60). Moreover, expression of the Notch gene target Hes1 is seen in precursor lesions arising in mouse models of pancreatic cancer (62), suggesting a role for this pathway in pancreatic cancer initiation. A comprehensive study by Jones et al of the pancreatic cancer genome characterised and profiled the genetic abnormalities of PDAC after sequencing 20,661 protein-­‐coding genes in samples from 24 patients (63). They demonstrated an average 48 nonsilent mutations, 6 amplifications, and 8 homozygous deletions per pancreatic cancer and these alterations were associated with 12 core signalling pathways (Table 1.5). 24 Regulatory process or pathway Number of genetically altered genes detected Fraction of tumours with genetic alteration of at least one of the genes Representative altered genes Apoptosis 9 100% CASP10, VCP, CAD, HIP1 DNA damage control 9 83% ERCC4, ERCC6, EP300, RANBP2, TP53 Regulation of G1/S phase transition 19 100% CDKN2A, FBXW7, CHD1, APC2 Hedgehog signalling 19 100% TBX5, SOX3, LRP2, GLI1, GLI3, BOC, BMPR2, CREBBP Homophilic cell adhesion 30 79% CDH1, CDH10, CDH2, CDH7, FAT, PCDH15, PCDH17, PCDH18, PCDH9, PCDHB16, PCDHB2, PCDHGA1, PCDHGA11, PCDHGC4 Integrin signalling 24 67% ITGA4, ITGA9, ITGA11, LAMA1, LAMA4, LAMA5, FN1, ILK c-­‐Jun N-­‐terminal kinase signalling 9 96% MAP4K3, TNF, ATF2, NFATC3 KRAS signalling 5 100% KRAS, MAP2K4, RASGRP3 Regulation of invasion 46 92% ADAM11, ADAM12, ADAM19, ADAM5220, ADAMTS15, DPP6, MEP1A, PCSK6, APG4A, PRSS23 Small GTPase–dependent signaling (other than KRAS) 33 79% AGHGEF7, ARHGEF9, CDC42BPA, DEPDC2, PLCB3, PLCB4, RP1, PLXNB1, PRKCG TGF-­‐β signalling 37 100% TGFBR2, BMPR2, SMAD4, SMAD3 Wnt/Notch signalling 29 100% MYC, PPP2R3A, WNT9A, MAP2,TSC2, GATA6, TCF4 Table 1.5: Core signalling pathways and processes genetically altered in most pancreatic cancers. Adapted from Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008 Sep 26;321(5897):1801-­‐6 (63) 25 1.6 Pancreatic cancer micro-­environment 1.6.1 Pancreatic cancer stroma and tumour-­stromal interaction The histological hallmark of PDAC is the presence of an intense desmoplastic stromal reaction which can account for up to 90% of the tumour volume (64). Desmoplasia can be defined as exuberant proliferation of stromal cells, abundant synthesis of extra-­‐cellular matrix proteins (ECM) with excessive collagen deposition (65). This desmoplastic reaction has been attributed to the gross hypovascularity seen in PDACs and also may attribute to the resistance to chemotherapy and radiotherapy. It has been well established that cancer stroma plays an active and dynamic role in tumour growth, invasion and metastasis and are not just bystanders (66). The stromal compartment is composed of different types of cells including stromal fibroblasts, immune cells, endothelial cells and matrix proteins. The cancer cells and stromal cells interact with each other and the cellular cross-­‐talk creates an environment that promotes tumour growth and invasion which involves ECM remodelling and metastasis (65). Interactions between the cancer cells and non-­‐neoplastic cells have been proposed to contribute to the desmoplastic reaction seen in PDAC. At the molecular level, tumour stroma production is promoted by several cancer cell derived pathways such as TGFβ1, fibroblast growth factor (FGF), insulin like growth factor (IGF) and epidermal growth factor (EGF) via autocrine and paracrine mechanisms (67). Matrix metalloproteinases (MMP) such as MMP2 and MMP9 are commonly over-­‐expressed in pancreatic cancer and play an important role in tumour invasion and migration by modifying the surrounding ECM (68). In addition, tissue inhibitors of metalloproteinases (TIMPs) which are involved in regulating matrix degradation are also found to be over-­‐expressed in pancreatic cancer, especially TIMP1 and TIMP2 (69). Other proteins involved in protein remodelling include SERPINE2 a serine protease inhibitor which is over-­‐expressed in various gastrointestinal malignancies and enhances ECM production and local invasion of pancreatic tumours in vivo (70). EMMPRIN, an inducer of ECM metalloproteinase stimulates MMP1 expression in stromal fibroblasts are frequently overexpressed in breast and ovarian cancer correlating with tumour size and prognosis (71). In pancreatic cancer, 26 EMMPRIN is expressed on the cell surface and supernatant of EMMPRIN-­‐positive pancreatic cancer cell lines such as MiaPaCa-­‐2 and Panc1 induces MMP-­‐2 synthesis in cultured pancreatic stellate cells (PSCs) (72). Therefore, there is a complex interplay between various components of the stroma in engendering a microenvironment that promotes tumour progression and invasion (Figure 1.2). Figure 1.2: Schematic illustration depicting pancreatic tumour microenvironment. Adapted and modified from Kleeff J, Beckhove P, Esposito I, Herzig S, Huber PE, Lohr JM, et al. Pancreatic cancer microenvironment. Modified from Int J Cancer. 2007 Aug 15;121(4):699-­‐705 (73) 1.6.2 Pancreatic stellate cell The pancreatic stellate cell has now emerged as the primary cell responsible for the intense desmoplasia that is characteristic of PDAC (74, 75). Akin to hepatic stellate cells, the pancreatic stellate cells are vitamin A storing cells and store about 10% of total vitamin stores of the body (hepatic stellate cells store about 85%). They are found in the periacinar spaces of the gland and in the normal pancreas are present in an un-­‐activated or quiescent state wherein they exhibit a more 27 rounded cell body with cytoplasmic processes (hence the name ‘stellate’) and contain lipid droplets which store vitamin A. In response to cellular injury (for e.g alcohol) or on exposure to certain secreted mediators (cancer cells), they assume an activated state where the morphology becomes more angular, with loss of the vitamin A storing lipid droplets, and they start secreting extracellular matrix proteins such as collagen type I and fibronectin(76). Characterisation of pancreatic stellate cell In 1982, Watari et al. first described the presence of vitamin A storing cells in the pancreata of mice fed on a vitamin A rich diet. The cells expressed a characteristic rapidly fading blue-­‐green auto fluorescence due to the presence of vitamin A when they were exposed to ultraviolet light at 328nm wavelength(77). Subsequently, Ikijeri et al. in 1990 demonstrated similar auto-­‐fluorescent cells in the rat and human pancreas. With the help of light, fluorescence and electron microscopy they demonstrated the presence of vitamin A storing cells in the peri-­‐acinar spaces of the normal human pancreas. They also examined specimens of patients with chronic pancreatitis and found that these cells were found in abundance in the fibrotic areas(78). It had already been established that the fibrotic response in liver cirrhosis was primarily initiated by the vitamin A storing ‘Ito’ cells of the liver (79, 80) and therefore, in keeping with this analogy, it appeared that these cells participated in the fibrosis of chronic pancreatitis. Saotome et al. in 1997 isolated periacinar fibroblast like cells from normal human pancreas and by immunocytochemistry showed that cells stained positively for alpha smooth muscle actin (α SMA) which is a marker for myofibroblasts like phenotype(81). The cells also stained positively for collagen type I and III, fibronectin and laminin suggesting that these cells could be involve in pancreatic fibrosis. In 1998 Apte et al. and Bachem et al isolated and characterised pancreatic stellate cells (PSC) from rat and human pancreas(74, 82). Freshly isolated cells contained lipid droplets, exhibited the autofluorescence and stained positive for vimentin, desmin and glial fibrillary acidic protein (GFAP) but negative for αSMA. On subsequent culture, these cells lost their vitamin A stores, changed morphologically into a more angular shape with long cytoplasmic processes and happen to be 28 positive for αSMA. This provided evidence that the stellate cells exist in a quiescent state and upon activation, convert into a myofibroblast like phenotype. Subsequent studies has shown that various factors such as alcohol induced injury, oxidant stress, cytokines secreted by macrophages and cancer cells, are involved in converting the quiescent stellate cell into an activated myofibroblast like cell capable of inducing the fibrotic reaction(83, 84). Table 1.6 summarises the characteristics of quiescent and active stellate cells. Quiescent stellate cell Active stellate cell Rounded in morphology with Angular with cytoplasmic process processes Contain lipid droplets storing Absent / vitamin – A droplets cytoplasmic decreased lipid Stain positive for GFAP, desmin, Positive, in addition, for alpha-­‐
vimentin but negative for SMA alpha-­‐SMA Not involved in production of Produce extracellular matrix extracellular matrix proteins proteins – collagen I,III and fibronectin Table 1.6. Characteristics of quiescent and activated stellate cells Attempts to further characterise pancreatic stellate cells has lead to the recognition of the possibility that these cells exist in a phenotypically heterogenous population. Ikenaga et al have shown that at least two distinct populations of stellate cells exist according to the presence or absence of CD10 marker which is a 90-­‐110 kilodalton membrane associated metallo-­‐proteinase and is a marker for acute leukemias and used in the subclassification of malignant lymphomas(85). CD10 has also been shown to be present in the stroma of gastric cancer(86), breast cancer(87) and colorectal cancer(88). In PDAC, the expression of CD10 positive cells was found to be associated with nodal metastasis and decreased survival time and in vitro experiments showed that CD10 positive stellate cells promoted invasiveness of pancreatic cancer cells(85). It has also been observed that stromal fibroblasts over-­‐expressing FAP (fibroblast activating protein), produce an extracellular 29 matrix that enhances invasive velocity and directionality of pancreatic cancer cells (89). Lee et al observed that FAP over-­‐expressing fibroblasts remodelled the ECM by increasing the levels of fibronectin and collagen fibre organisation (89). Origin of stellate cells The origin of stellate cells is a subject of much ambiguity. They express vimentin, a member of intermediate filament family of proteins which, along with microtubules and actin microfilaments make up the cytoskeleton and is a marker for cells of mesenchymal origin. Glial fibrillary acidic protein, which the stellate cells also express, is a marker for cells arising from the neuroectoderm(82). Seaberg et al describe the identification of a multipotential precursor cell in mouse pancreas that generates cell types characteristic of all three germ cell layers including pancreatic endocrine, exocrine and stellate cells; as well as neural and glial cells, therefore demonstrating that stellate cells are derived from pancreas specific precursors(90). Buchholz et al demonstrated that on transcriptome analysis, pancreatic and hepatic stellate cells share common transcriptional phenotypes and demarcated them from other fibroblast cell lineage suggesting that pancreatic and hepatic stellate cells perhaps share a common precursor(91). It was demonstrated that 74 genes were differentially expressed between skin fibroblasts and stellate cell in general; whereas only 29 genes were differentially expressed between HSCs and PSCs. The genes diferrentially expressed were mainly associated with ECM production and turnover, cell adhesion and cell communication (Table 1.7). More recently, growing evidence suggest that bone marrow derived precursors could be responsible for generating pancreatic stellate cells. Sparman et al demonstrate that in experimentally induced pancreatitis in rats, bone marrow derived cells were recruited into the pancreas that expressed stellate cell markers suggesting bone marrow as a source of circulating stellate cell precursors(92). It was estimated that about 7% of PSCs in normal healthy rat pancreas were of bone marrow origin and increased to about 18% in regenerating pancreas following injury. 30 Gene Name
GenBank ID
HUGO
Name
Ratio
HSC/
PSC
Tissue factor pathway inhibitor 2
Interferon induced transmembrane protein 1 (9-27)
Forkhead box F1
Small inducible cytokine A2 (monocyte chemotactic protein 1)
Secreted frizzled-related protein 1
Zinc finger protein, subfamily 1A, 5 (Pegasus)
Hypothetical protein DKFZp434B044
Tumor necrosis factor receptor superfamily, member 11b
(osteopr
Nidogen 2
Sparc/osteonectin, cwcv and kazal-like domains proteoglycan
KIAA1350 protein
Destrin (actin depolymerizing factor)
Homo sapiens cDNA FLJ30550 fis, clone BRAWH2001502
Striated muscle contraction regulatory protein
Ubiquitin carboxyl-terminal esterase L1 (ubiquitin thiolesterase)
Homo sapiens cDNA: FLJ23313 fis, clone HEP11919
Actin, gamma 2, smooth muscle, enteric
Integrin, alpha 7
Lactate dehydrogenase A
S100 calcium binding protein A10 (annexin II ligand, calpactin
Hypoxia-inducible factor 1, alpha subunit (basic helix-loop-hel
Connective tissue growth factor
Fibronectin 1
Protease, serine, 11 (IGF binding)
KIAA0977 protein
Desmoplakin (DPI, DPII)
Four and a half LIM domains 1
Carboxypeptidase E
Osteoblast specific factor 2 (fasciclin I-like)
NM_006528
NM_003641
NM_001451
NM_002982
NM_003012
NM_022466
NM_031476
NM_002546
TFPI2
IFITM1
FOXF1
CCL2
SFRP1
4.78
3.95
3.76
3.64
2.66
2.66
2.55
2.51
NM_007361
NM_004598
AB037771
NM_006870
AK055112
M96843
NM_004181
AK026966
NM_001615
NM_002206
NM_005566
NM_002966
NM_001530
NM_001901
NM_002026
NM_002775
NM_014900
NM_004415
NM_001449
NM_001873
NM_006475
TNFRSF1
1B
NID2
SPOCK
DSTN
ID2B
UCHL1
ACTG2
ITGA7
LDHA
S100A10
HIF1A
CTGF
FN1
PRSS11
DSP
FHL1
CPE
POSTN
2.34
0.49
0.49
0.47
0.47
0.47
0.46
0.45
0.43
0.43
0.43
0.42
0.40
0.34
0.33
0.32
0.31
0.31
0.24
0.21
0.17
Table 1.7. Genes differentially expressed between HSCs and PSCs. Buchholz M, Kestler HA, Holzmann K, Ellenrieder V, Schneiderhan W, Siech M, et al. Transcriptome analysis of human hepatic and pancreatic stellate cells: organ-­‐specific variations of a common transcriptional phenotype. J Mol Med. 2005 Oct;83(10):795-­‐805(91). Regulation of activation The activation of pancreatic stellate cells is initiated by a number of secreted mediators acting both in an autocrine and paracrine fashion. Studies mainly concerning activation of stellate cells in the setting of chronic pancreatitis have revealed that the stellate cell activation is brought about by various pro-­‐inflammatory cytokines secreted by acinar cells and other immune cells in response to injury(83, 93). The inciting event could be direct injury such as by alcohol metabolite acetaldehyde or release of chemokines by activated macrophages (activated by secreted factors from injured ductal and acinar cells). Platelet derived growth factor (PDGF) and transforming growth 31 factor-­‐β (TGFβ) have been shown to be the most potent of mediators in converting the stellate cell from a quiescent to an activated state(94), increasing their proliferation and collagen synthesis. Basic fibroblast growth factor (bFGF)(95) has also shown to have mitogenic effects on pancreatic stellate cells. Apart from growth factors, cytokines such as IL-­‐1, IL-­‐6 and tumour necrosis factor (TNFα)(94) have also been implicated in the activation of stellate cells. In pancreatic cancer, although a different pathology, the mechanism of activation of the stellate cell appears to be similar to pancreatitis and most of the growth factors and cytokines found in pancreatic inflammation are over-­‐expressed in pancreatic cancer(96). When pancreatic stellate cells were exposed to supernatant collected from cultured pancreatic cancer cells, there was an increase in proliferation and production of collagen type I and fibronectin(97). These effects were reversed when neutralising antibodies against PDGF, TGFβ and FGF2 were used, suggesting that these secreted mediators released by cancer cells play a vital role in stimulating the stellate cell. When cancer cells were injected along with stellate cells in mouse xenograft models, when compared to stellate cells alone, it was observed that there was a marked increase in the tumour volume and histological sections showed this to be attributable to an increase in ECM deposition(97). Mediators of stellate cell function Platelet derived growth factor (PDGF) is a growth regulatory protein dimer with two polypeptide chains (A and/or B) which are linked by disulphide bonds forming three isoforms – PDGF-­‐AA, PDGF-­‐AB, PDGF-­‐BB. It is a potent mitogen for mesenchymal derivatives such as fibroblasts and smooth muscle cells. During inflammation and tissue repair PDGF is secreted in high concentrations by various cell types including platelets, mononuclear cells and macrophages(98). It has been shown to be a potent proliferative mediator for hepatic stellate cells(99, 100) and Apte et al. demonstrated that PDGF stimulates proliferation of pancreatic stellate cells in a dose dependent fashion in vitro and the isoform PDGF-­‐BB in particular was found to have significant proliferative effect in terms of cell numbers and DNA synthesis(94). It has been shown in the hepatic stellate cells that PDGF acts by stimulating the phosphoinositol pathway and Ca2+ turnover (100) and it is possible 32 that a similar mechanism exists in pancreatic stellate cells. PDGF and its isoforms (A and B) has been shown to be over-­‐expressed in pancreatic cancer(101) and pancreatic cancer lines such as PANC-­‐1, MiaPaCa 2 and HPAF (102). It is interesting to note that PDGF receptors i.e. PDGFRα and PDGFRβ are over-­‐expressed by pancreatic stellate cells(95) and therefore provide for paracrine communication between cancer cells and stellate cells. Transforming growth factor-­‐β (TGFβ) is a homodimer with two polypeptide chains linked by disulphide bonds and three homlogous isoforms (β1, β2, β3) have been identified. The Isoform β1 has been shown to play a regulatory role in the biosynthesis and turnover of ECM proteins and is the most potent fibrogenic mediator described for hepatic stellate cells(103). The role of TGFβ1 in pancreatic fibrogenesis has been well established in experimental models of pancreatitis (104, 105). TGFβ induces the transformation of quiescent stellate cell to its active phenotype and also stimulates collagen synthesis in stellate cells(94). In addition, TGFβ reduces the levels of metalloproteinases and increases the levels of tissue inhibitor of metalloptoteinases(106). It is also interesting to note that TGFβ induces the expression of PDGF receptors on the surface of pancreatic stellate cells (94, 95) and a direct stimulating effect of TGFβ on PDGF synthesis has been reported(107), suggesting an autocrine loop mechanism for PDGF turnover in stellate cells. TGFβ has been shown to be over expressed in pancreatic cancer and in cell lines of pancreatic cancer(102) suggesting that cancer cells might induce tumour desmoplasia directly via TGF signalling causing activation of stellate cells and indirectly by inhibition of matrix degradation. Activin A is another polypeptide belonging to the TGFβ superfamily which has been shown to be involved in stellate cell activation. It is a homodimer and studies have shown it to be involved in diverse actions on cellular growth and differentiation (108). In the pancreas, it is found in the islets and is involved in B cell differentiation (109). Onishi et al. demonstrated that activin A activated quiescent stellate cells to its myofibroblast like phenotype and together with TGFβ, enhanced each other’s mRNA expression and secretion in stellate cells(108). 33 Fibroblast growth factors (FGF) are a family of heparin binding growth factors that signal via transmembrane tyrosine kinase receptors. They are invoved in mitogenesis, cell differentiation and angiogenesis. Acidic fibroblast growth factor (aFGF or FGF1) and Basic fibroblast growth factor (bFGF or FGF2) are the two main isoforms. Both FGF1 and FGF2 (and other FGFs such as FGF 5 and 7) has been found to be over-­‐expressed in pancreatic cancer(110). FGF2 has been identified as a mediator involved in proliferation of stellate cells and stimulating matrix production(97) which is consistent with the finding that pancreatic cancer cells overexpress FGF2 and induce proliferation of adjacent fibroblasts in vivo(111). The addition of recombinant FGF2 to cultured rat pancreatic stellate cells resulted in an increased proliferation and production of extracellular matrix proteins(95). In addition to the above mentioned growth factors, pancreatic stellate cells have been shown to respond to certain cytokines such as TNFα, IL-­‐1 and IL-­‐6. Mews et al. studied the effect of these cytokines on pancreatic stellate cells with respect to αSMA expression, cell proliferation and production of extracellular matrix(84). TNFα was found to influence all three parameters whereas IL-­‐
1 and IL-­‐6 increased αSMA expression but did not seem to have an effect on proliferation or collagen synthesis. Recently IL13 has been identified to promote proliferation of rat pancreatic stellate cells(112) and IL-­‐33 which belongs to the IL-­‐1 family was found to be expressed in pancreatic myofibroblasts and enhanced their proliferation(113). Interestingly, there is evidence accumulating of an intrinsic renin angiotensin system that participates in the modulation of pancreatic stellate cell function (114, 115). Reinher et al. demonstrated that pancreatic stellate cells possessed AT1 receptor subtype which binds to angiotensin II (the active ligand) stimulating proliferation of stellate cells but did not have any effect on αSMA expression(116). Recently, rat pancreatic stellate cells have been shown to express CCK (cholecystokynin) receptors CCK1 and CCK2 (117) and interestingly, both its ligands, CCK and gastrin, stimulated the production of collagen I synthesis. Berna et al. demonstrated the presence of CCK1 34 and CCK2 receptors in rat pancreatic stellate cells and that activation was mainly mediated by CCK2R receptor subtype. Both CCK and gastrin were shown to activate stellate cells and stimulate the collagen 1 secretion; however, they did not seem to have any effect on proliferation (117). Table 1.8 lists the various mediators and its effect on pancreatic stellate cells. Mediator Effect on PSC Growth Factors •
•
•
PDGF TGFβ Activin A bFGF Increase proliferation(94, 118) Increase collagen synthesis(94, 95) Stellate cell activation and proliferation(108) Increase Proliferation(95, 97) Cytokines •
•
•
•
•
TNFα IL-­‐1 IL-­‐6 IL-­‐13 IL -­‐ 33 Increase in αSMA, proliferation and collagen synthesis Increase in αSMA expression(84) Increase in αSMA expression(84) Increase proliferation(112) Increase proliferation(113) Angiotensin II CCK and Gastrin Increase proliferation(116) Increase collagen I synthesis(117) Other •
•
Table 1.8: Summary of mediators influencing Panreatic stellate cell (PSC) function Upon activation, stellate cells not only undergo the above mentioned morphological changes, but also start secreting various mediators that in turn have an autocrine effect and help perpetuate and sustain the activated phenotype and also a paracrine effect on tumour cells, influencing factors like proliferation and invasion. For example, activated stellate cells secrete PDGF, TGFβ, cytokines (IL-­‐1, IL-­‐6) and pronflammatory molecules like COX-­‐2(119). They also secrete VEGF and express VEGF receptors suggesting a role in angiogenesis(120). To summarise, the activation of pancreatic stellate cells is influenced by certain key mediators that promote proliferation and extracellular matrix production. Of the growth factors, 35 PDGF and bFGF have been shown to enhance proliferation of the stellate cells while TGFβ enhances matrix production. In conjunction with this, cytokines such as TNFα (activation, proliferation and matrix production), IL-­‐1 and IL-­‐6 (mainly activation) also play a considerable role in the regulation of stellate cell activation. Following activation, stellate cells themselves secrete mediators which act in an autocrine way to perpetuate the activated phenotype and promote fibrogenesis. Signal transduction within stellate cells A number of intra-­‐cellular signalling pathways are involved in the activation and regulation of stellate cells. Understanding the various pathways involved will undoubtedly provide valuable insight into identifying targets for therapeutic intervention. Mitogen activated protein kinase (MAP kinase) pathway has been shown to play a vital role in the production of cytokines and chemokines in PSCs (121). The MAP kinase family consists of three main components – ERK, JNK and p38 MAP kinase. Each of the three components has been shown to regulate the activation and function of PSCs via extracellular stimulation by pro-­‐
inflammatory cytokines, oxidant stress, ethanol metabolites and trypsin(122). Following phosphorylation, these kinases translocate to the nucleus and activate transcription factors such as AP-­‐1 and NF-­‐κB which in turn results in the transcription of specific genes. PI3 kinase-­‐AKT pathway is an important downstream pathway of PDGF receptor. It has been reported that PDGF-­‐BB isoform activated the PI3K-­‐AKT pathway in PSCs and lead to its activation and promoted migration of PSCs(123). In addition, PDGF-­‐BB also activated the JAK/STAT pathway and JAK-­‐STAT3 has been shown to be involved in PDGF induced PSC proliferation (124). TGFβ-­‐1 signalling is mediated by the Smad group of intracellular proteins. Following binding of TGFβ1 to its receptors, Smad2 and 3 are phosphorylated and form complexes with Smad4. These complexes reach the nucleus where they activate the transcription of various target genes(125). TGFβ-­‐1 regulates a number of PSC functions including activation, αSMA expression, and reduced expression of MMP3 and MMP9 (126). 36 Upon activation, PSCs undergo changes in cytoskeleton re-­‐organisation and in this regard, the GTPase binding protein Rho has been shown to play an important role through the downstream effector Rho kinase. This Rho/Rho kinase pathway modulates PSC activation by regulating the actin cytoskeleton and formation of stress fibres (127). Recently, the hedgehog (Hh) pathway has been shown to play a significant role in the desmoplasia of PDAC. In vitro experiments have shown that sonic hedgehog (one of the ligands of the hedgehog pathway – others include indian and desert hedgehog) promotes activation, migration and invasion of PSCs which were reversed when the hedgehog pathway was inhibited (61). Binding of the Hh ligand to the receptor Patched causes the release of the transmembrane protein Smo, resulting in the activation of the Gli family of transcription factors. Olive et al also showed that in transgenic KPC mice when treated with hedgehog inhibitor IPI-­‐926 (a semisynthetic derivative of cyclopamine which inhibits Smo), a reduction in the desmoplastic reaction was observed which improved the drug delivery into the tumour tissue, providing promising evidence for therapeutic intervention (128). In addition, it was demonstrated that treatment with IPI-­‐926 also caused a reduction in αSMA positive cells in the stromal compartment and an increase in αSMA negative cells, suggesting a direct effect of this inhibitor on stellate cell activity. Therefore, various mediators are involved in this cross-­‐talk between PSCs and tumour cells in promotion of pancreatic diseases and hence would be a promising prospect to identify strategies where this cross-­‐talk could be targeted. Strategies that can be foreseen in this regard include interruption or reversion of stellate cell activation or blockade of growth factor signalling pathways. Inhibition of activation or reversion from activated to the quiescent state of stellate cells has been proposed. Activated stellate cells when exposed to ATRA (all trans retinoic acid; an active metabolite of vitamin A) revert to their quiescent state and was also shown to inhibit activation (129). Studies have implicated the nuclear hormone receptor peroxisome proliferator-­‐activated receptor γ (PPARγ) in the inhibition of stellate cell activation in liver and pancreas(130). The PPARγ 37 ligand troglitazone (an antidiabetic drug of the thiazolidinedione group) acts as antagonists of PSC activation in vitro by blocking PDGF stimulated cell proliferation and expression of α-­‐SMA(131). Similarly, inhibition of COX2 mediated TGFβ signalling can be a relevant therapeutic target(119) and angiotensin II inhibitors have been shown to decrease the fibrotic reaction in chronic pancreatitis models(132). Yang et al have recently shown that L-­‐cysteine administration attenuated fibrosis by inhibiting stellate cell activation in rats wherein pancreatitis was induced by trinitrobenzene sulfonic acid (TNBS) (133). A recent study by Lee et al combined HMG-­‐CoA reductase inhibitor along with PPARγ agonist troglitazone and observed a reduction in proliferation of activated rat PSCs associated with attenuation of αSMA expression. Simvastatin reversed the effects of PDGF in a dose dependant manner and in combination with troglitazone, caused a synergistic reversal of PDGF on stellate cell proliferation. The expression of «SMA was markedly attenuated by combining the two drugs, which blocked the cell cycle beyond the G0/G1 phase(134). Table 1.9 lists some of the potential agents capable of targeting stellate cell activation or modulating stromal composition. 1.6.3 Pancreatic cancer stem cells There is accumulating evidence now suggesting that the capability of a tumour to grow and propagate is dependent on a small subset of cells within the tumour, called stem cells. The cancer stem cell (CSC) can be described as a cell within a tumour that is able to self-­‐renew and to produce the heterogeneous lineages of cancer cells that comprise a tumour (135). Cancer stem cells were first demonstrated in the context of acute myeloid leukemia (136) and subsequently verified in breast (137) and brain (138) tumours. In the context of pancreatic cancer, Li et al demonstrated, using a xenograft model, the presence of a distinct, highly tumourigenic subpopulation of pancreatic cancer cells expressing cell surface markers CD44, CD24 and epithelial-­‐specific antigen (ESA). Cells positive for these tumour markers had a100-­‐fold increased tumourigenic potential when compared with other cancer cells (139). On similar lines, Hermann et al have demonstrated that cells expression CD133 marker in human pancreatic cancer were exclusively tumerigenic and also highly resistant to standard Gemcitabine therapy. In the invasive front, a distinct subpopulation of CD133+ 38 and CXCR4+ cancer stem cells were identified that determined the invasive phenotype of the tumour (140). Therefore, these cancer stem cells have characteristic features such as resistance against conventional chemotherapy, establishment of metastasis and reconstruction of hierarchical populations of tumour cells. Strategies aimed at cancer stem cell growth and survival will undoubtedly provide novel therapeutic approaches in treating pancreatic cancer. 39 Figure 1.3. Schematic illustration depicting the mediation and regulation of stellate cell function Agent ATRA Mechanism Deactivation of activated stellate cells by restoring vitamin A levels Troglitazone PPARγ agonist blocks PDGF stimulated cell proliferation and αSMA expression Simvastatin HMG-­‐CoA reductase inhibitor with PPARγ agonist inhibits PDGF pathway Lisinopril / candesartan Inhibition of renin-­‐angiotensin pathway Rofecoxib Inhibition of COX-­‐2 and blockade of TGFβ1 pathway Table 1.9: List of stellate cell / stromal targeting agents 40 1.7 Pancreatic cancer models 1.7.1 In vivo mouse models of pancreatic cancer There has been a recent expansion during the last decade in the development of in vivo mouse models of pancreatic cancer and it mainly stems from the need to urgently understand the biology of the tumour in a way that closely recapitulates the human form. Primarily there are three types of murine models which include xenograft, carcinogen induced and genetically engineered models. Xenograft models Xenograft models of pancreatic cancer can be generated by implanting human pancreatic cancer cell lines in the subcutaneous tissue of immune compromised nude mice and has been proven useful in tumourigenicity test, study of tumour cell/host cell interaction and evaluation of in vivo efficacy of new anticancer drugs (141). The advantages of this system include ease of measurement of tumour dimensions and also the calculation of tumour morphology at the end of the experiment after tumour resection. The inherent disadvantages of this system include potential contamination by mouse tissue and alterations of tumour microenvironment that can induce alterations in gene expression that are not representative of the denovo cancer. Also, the immunocompromised nature of the mouse environment ignores the contribution of the host immune system in tumour progression. In addition, subcutaneous models show extensive local growth but rarely metastasise in stark contrast to the behaviour in humans (142). Further, subcutaneous tumours do not show the signs and symptoms that are evident of tumours in the viscera (141). Another type of xenograft model uses resected tissue from pancreatic cancer patients that are implanted subcutaneously in immunocompromised mice. The advantage over cell line models is that it maintains the morphological phenotype of the original tumour and also does not seem to display the genetic alterations seen in cell line models (143). This model has been used to apply, test and validate established methods to assess drug efficacy and pharmacodynamic effects (144). However, this 41 model shares all the disadvantages of the xenograft cell line model and further enhancements have been made necessary. Orthotopic mouse models are slightly more complicated and are generated by injecting cancer cell into mouse pancreas and have been established as a convincing method of mimicking and studying pancreatic cancer (PDAC). The rate of tumour establishment using orthotopic injections has been shown to be between 50% and 100% and closely mimics the human counterpart in that up to 60% of the resulting tumour tends to disseminate (145). Various techniques of injecting cell lines have been developed. Cells can be injected directly into the pancreas and this has shown a relatively high success rate with regard to metastatic potential (141). Usually a small volume containing a million cells is injected into the distal portion (tail) of the pancreas (146). This technique may lead to intra-­‐abdominal bleeding, disruption of capsule or tumour cell spillage into the peritoneal cavity which can confound the results after the experiment. Another method is to inject cells directly into the common bile duct. In this method, the cells are usually tagged with a fluorescent dye and tumour colonies are imaged 5 days after injecting and by 28 days, the tumour encompassed the entire pancreas (147). An alternative to injecting cells is to anchor a piece of solid tumour directly into pancreas and has been done with 1mm3 tumour fragment and several weeks later a 2-­‐3cm mass has been seen to develop on the pancreas or replacing it (145). Orthoptopic models although help study the tumour in its native position, are subject to disadvantages that include spillage of cells; are more expensive and labour intensive; and technically difficult. Carcinogen induced models are generated by administering certain chemicals that lead to cellular changes resulting in pancreatic cancer. The models include intraperitoneal injection of carcinogens such as N-­‐nitrosobis(2-­‐oxopropyl)amine in Syrian golden hamsters (148), azaserine in rats (149) and 7,12-­‐dimethylbenzanthracene in mice (150). These models do mimic the range of carcinogenesis from epigenetic alterations to development of carcinoma; however, the histological 42 type (mainly acinar cell carcinoma) and adverse effects on other organs (hepatocellular carcinoma) have limited their value as a preclinical model. Genetically engineered mouse models Genetically engineered mouse models (GEMMs) mimic relevant genetic mutations that lead to spontaneous lesions or cancer within the pancreas gland. With the understanding of key genetic alterations in the progression of PDAC and coupled with technological advancements of genetic engineering, GEMMs have lately become an invaluable research tool for studying pancreatic cancer. The mouse pancreas was one of the first organs in which tissue specific transgene expression was achieved and transgenic tumour induction was accomplished (151, 152). This was facilitated by the identification of specific promoter/enhancer elements in the elastase I locus which predominantly targeted pancreatic acinar cells and produced acinar cell neoplasms when coupled to either H-­‐Ras or SV40 T-­‐antigen. The expression of c-­‐myc transgene driven by the same promoter produced mixed acinar/ductal tumours (153). These early mouse models however, failed to recapitulate the typical PDAC which is the predominant form seen in human cancer. Subsequently, better understanding of the genetic alterations in PDAC revealed the roles of KRAS oncogene activation and inactivation of tumour suppressor genes p53, p16INK4A and SMAD4 in the progression of PDAC from early PanIN lesions to invasive cancer (154). Coupled with this, progress in the understanding of pancreatic developmental biology lead to the identification of various transcription factors and signalling pathways responsible for normal foregut patterning, branching morphogenesis and exocrine/endocrine differentiation (155). Among these factors, the homeodomain protein Pdx1 and the basic helix-­‐loop-­‐helix protein Ptf1-­‐P48 were implicated as critical regulators of pancreatic embryogenesis. Cells expressing Pdx1 at E8.5, E12.5 or later gave rise to acinar and endocrine cells but not to ductal cells. On the other hand, expression of Ptf1/p48 becomes restricted to exocrine cells only by E13.5 (155). In addition, signalling pathways such as 43 Hedghog, EGF and Notch have also been implicated in regulation of pancreatic development. Hedgehog signals appear to play a critical role in foregut patterning, acting to restrict pancreatic development to nascent dorsal and ventral buds, and promoting gut and liver differentiation programs in adjacent non-­‐pancreatic endoderm (156). Within the pancreatic buds, EGF signaling appears to drive proliferation of undifferentiated precursor cells (157), while Notch acts to prevent both endocrine and exocrine differentiation (158), effectively preserving an undifferentiated precursor pool. All these cell lineage studies and genetically engineered mouse models have been facilitated by the Cre-­‐lox technology where a target gene or gene segment is flanked by loxP sites (“floxed”) and expression of Cre-­‐recombinase (a site specific recombinase that recognises loxP) under the control of a site specific promoter leads to excision of the floxed gene or gene segment (159). In 2003, an excellent animal model of pancreatic cancer was created using transgenic mice, which expressed KRASG12D under control of Cre-­‐recombinase from the pancreatic specific promoters Pdx-­‐1 or Ptf1/p48. Pancreata of all these mutant mice developed tumours, which mimicked human pancreatic intra-­‐epithelial neoplasia (PanIN). When these mice became older, higher-­‐grade Pan-­‐IN lesions were observed and, interestingly, in many mice the acinar parenchyma was largely replaced by an intense stromal reaction with inflammatory cells, fibroblasts and collagen deposition; resembling the desmoplastic reaction seen in human pancreatic cancer. When a small cohort of 29 mice was followed up longer, two mice developed invasive and metastatic pancreatic cancer (at the age of 6.25 and 8.25 months), with similar sites of metastasis as seen in human pancreatic cancer (62). These KRASG12D mice were subsequently crossed with either conditional INK4a/ARF null mice, which have a conditional deletion of exons 2 and 3 of the INK4a/ARF locus that eliminates both p16INK4A and p19ARF proteins (160), or with mice harbouring a G-­‐to-­‐A substitution at nucleotide 515 of the endogenous TP53, corresponding to the TP53R172H mutations in human cancers (161). The addition of these mutations/deletions resulted in invasive and highly metastatic tumours (160, 161), demonstrating a multistep progression from pre-­‐invasive to invasive disease, which is in line with the 44 tumour progression model described for human pancreatic cancer. To further investigate the genetic requirements for PDAC progression, Pdx-­‐Cre;KRASG12D mice were crossed with different combinations of null mutations (homozygous or heterozygous) in TP53, INK4a or ARF, which revealed a critical and cooperative role of all the three tumour suppressors studied in combination with an activated KRAS mutation (162). Additionally, the role of disrupted TGFβ signalling in the progression of early pancreatic cancer in the context of mutated KRASG12D has been explored, by again using mice harbouring the KRASG12D mutation combined with a conditional deletion of exon 8 of the floxed SMAD4/DPC4 allele, which resulted in a frame-­‐shift, rapid degradation and haploinsufficiency (DPC4/SMAD4flox/+) or no expression (DPC4/SMAD flox/flox) of SMAD4/DPC4. These mice developed cystic neoplasms resembling human MCNs, which progressed to invasive pancreatic cancers with a lower propensity compared to mice expressing mutated KRAS and TP53 (163). In addition to these discussed mouse models, which are all based on conditional expression under the control of the transcription factors Pdx1 or Ptf1/p48, more genetically engineered mouse models have been described using different promoters to drive mutagenic expression (e.g. the expression of mutant KRAS under control of the rat-­‐elastase promoter (164), the cytokeratin 19 promoter (165) or the endogenous Mist1 promoter (166)). Table 1.9 lists the various genetically engineered mouse models. Put together, although these mouse models provide excellent insight into the different pathogenetic changes that lead to the development of invasive pancreatic cancer, they have not shed much light into the study of tumour-­‐stroma interactions (no cellular or molecular analyses of the desmoplastic reaction were described). Additionally, the long latency periods involved make these models expensive and non-­‐amenable to rapid experimental manipulation. For many of the problems needed to be investigated in pancreatic cancer, with a special attention to its prominent stromal component, it is possible that in vitro three-­‐dimensional, physiologically relevant bio-­‐mimetic organotypic models could provide a solution. 45 Reference Wagner 2001 (167) Promoter Elastase Model Ela-­‐TGFα B6/Balbc Background B6 Grippo 2003 (164) Elastase Ela-­‐KRAS
Guerra 2003 (168) BGeo Ela-­‐tTA; tet-­‐o-­‐cre; LSL-­‐Kras
Aguirre 2003 (160) Pdx1 Pdx1-­‐cre; LSL-­‐Kras
Hingorani 2003 (62) Lewis 2003 (169) Pdx1 or P48 Pdx1-­‐cre; Kras
+/Cre
G12D Ptf1a
;Kras
G12D B6/129SvJae Elastase TVA-­‐RCAS-­‐PyMT (Ink4a/Arf, p53) FVB/129/B6 Brembeck 2003 (165) Cytokeratin-­‐
19 K19-­‐ KRAS
Thayer 2003 (59) Pdx1 Pdx1-­‐SHH Hingorani 2005 (161) Bardesey 2006 (162) Pdx1 Pdx1-­‐cre; LSL-­‐Kras
Pdx1 Pdx1 Pdx1 Pdx1-­‐Cre;LSL-­‐KRAS
;TP53 p16INK4a +/+ INK4a +/-­‐
with either or p16 INK4a -­‐/-­‐ or p16 G12D
G12V
FVB G12D
G12V-­‐ires-­‐BGeo
fl/fl
; Ink4a/Arf G12D
; LSL-­‐p53
R172H
fl/fl
G12D
fl/+
Pdx1-­‐Cre;LSL-­‐KRAS
;TP53 INK4a +/+ INK4a +/-­‐
with either p16
or p16 INK4a -­‐/-­‐ or p16 G12D
Pdx1-­‐Cre;LSL-­‐KRAS
; +/+
INK4A -­‐/-­‐ TP53 ;p16
Pdx1-­‐Cre;LSL-­‐KRAS
INK4a
ARF fl/fl p16
/p19
FVB/B6 B6/C3F1 B6/129SvJ/FVB B6/SJL G12D
G12D
; B6/129SvJae FVB/n FVB/n FVB/n FVB/n Tumours Acinar ductal metaplasia and carcinoma; cystic papillary neoplasia. No invasive ductal carcinoma Acinar ductal metaplasia and carcinoma; cystic papillary neoplasia. No invasive ductal carcinoma Focal acinar ductal metaplasia, PanIN, invasive ductal carcinoma Focal acinar ductal metaplasia, PanIN, invasive ductal carcinoma Focal acinar ductal metaplasia, PanIN, invasive ductal carcinoma Cystic papillary neoplasia; mixed ductal and acinar cell carcinoma Periductal lymphocytic infiltration, occasionally ductal hyperplasia Intestinal phenotype of pancreatic epithelium, acinar-­‐ductal metaplasia, lesions resembling PanIN Focal acinar ductal metaplasia, PanIN, invasive ductal carcinoma Most rapid progression to invasive cancer, independent of p16INK4a status (adenocarcinoma in 100%, 80% and 40%, p16INK4a status respectively) Progression to invasive adenocarcinoma but with longer latency period compared to homozygous deletion of TP53 Invasive sarcomatoid pancreatic tumours with metastasis Invasive tumours in 100% of mice (48% adenocarcinoma, 26% sarcomatoid, 26% anaplastic), with metastasis 46 Bardesey 2006 (162) Pdx1 Pdx1 Pdx1 Pdx G12D
fl/fl
Pdx1-­‐Cre;LSL-­‐KRAS
;TP53 p16INK4a +/+ INK4a +/-­‐
with either or p16 INK4a -­‐/-­‐ or p16 G12D
fl/+
Pdx1-­‐Cre;LSL-­‐KRAS
;TP53 INK4a +/+ INK4a +/-­‐
with either p16
or p16 INK4a -­‐/-­‐ or p16 G12D
Pdx1-­‐Cre;LSL-­‐KRAS
; +/+
INK4A -­‐/-­‐ TP53 ;p16
Pdx1-­‐Cre;LSL-­‐KRAS
INK4a
ARF fl/fl p16
/p19
G12D
; G12D
; Pdx1-­‐Cre;LSL-­‐KRAS
INK4a
ARF fl/+
p16
/p19
FVB/n FVB/n FVB/n FVB/n FVB/n Most rapid progression to invasive cancer, independent of p16INK4a status (adenocarcinoma in 100%, 80% and 40%, p16INK4a status respectively) Progression to invasive adenocarcinoma but with longer latency period compared to homozygous deletion of TP53 Invasive sarcomatoid pancreatic tumours with metastasis Invasive tumours in 100% of mice (48% adenocarcinoma, 26% sarcomatoid, 26% anaplastic), with metastasis in 11% Invasive tumours but with a longer latency period compared to p16INK4a/p19ARF fl/fl or TP53fl/+;p16INK4a-­‐/-­‐ Tuveson Mist-­‐1 Mist1-­‐Kras4B
Table 1.10: M ouse models of pancreatic B6/129SvJae cancer Acinar-­‐ductal metaplasia, 2006 occasionally PDAC or mixed (166) carcinomas +/Cre
G12D
lox/lox
Ijichi (
2006) Ptf1a Ptd1a
;Kras
;TGFβIIR
Mixed Aggressive undifferentiated 1.7.2 Three dimensional (3D) in-­vitro models (170) PDAC G12D
Kojima 2007 Pdx1 Pdx1-­‐Cre;Kras
;Smad4 C57BL/6 IPMN to PDAC progression Three dimensional organotypic culture has emerged as a valuable in vitro model for (171) +/Cre
G12D
Izeradjene Ptf1a Ptf1a
;Kras
;Smad4 Mixed MCNs resembling human studying from study of cellular 2007 (163) a variety of biological features and has gained applications ranging disease +/Cre
G12D
Siveke 2007 Elastase-­‐
Ptf1a
;Kras
;Ela-­‐TGFα mixed PanIN and IPMN derived (172) Tgfα / multicellular), drug screening, to the study 129SV;C57BL/6 behaviour (mono of tissue PDAC explants. Generally the active
Heiser 2008 Ptf1a or Ptf1a-­‐Cre; β-­‐cat
Mixed Tumours resembled human (173) Pdx1 pseudopapillary neoplasms. model consists of a matrix composed of extracellular matrix (ECM) proteins such as collagen and No PDAC G12D
Habbe (2008) Elastase Ela-­‐CreERT;Kras
Mixed Acinar derived PanIN lesions basement membrane proteins (laminin and fibronectin) with the cells or tissue cultured on top or (174) +/Cre
G12D
lox/lox Mazur & Ptf1a Ptf1a
;Kras
;Notch1
129SV;C57BL/6 Similar or slightly accelerated +/Cre
G12D
Hanlon PDAC to Ptf1a
;Kras
within the matrix. It has been widely accepted that providing a bio-­‐mimetic environment of ECM (2010) (175, 176) MCNs, PanIN1, sarcomatoid +/Cre differentiation G12D
lox/loxand organisation and the cell – matrix signal proteins facilitates cell growth, Ptf1a
;Kras
;Notch2
PDAC with long latency +/Cre
G12D
R27OH/+
Tr/D11 Skoulidis Ptf1a Ptf1a
;Kras
;p53
;Brca2
FVB/N Model of familial PDAC (2010) (177) mediated by integrins is maintained (178, 179). transduction G12D
One of the first 3D models to be described was by Elsdale et al in 1972 who described a model consisting of collagen I matrices polymerised in vitro and used for studying human fibroblast cell behaviour (180). Since then many types of 3D culture models have been developed for studying 47 different cell and tissue types (table 1.10). One common model is to make use of tissue explanted from organs (mouse aortic rings) or thin tissue slices (brain) and grow them in vitro (181, 182). Three-­‐dimensional models with isolated cells or cell lines have also been well-­‐established. One frequently used method is to propagate cells in tissue culture and then implant them on 3D matrices containing collagen I as single cells or as spheroid aggregates (183). Another method utilises ‘cell derived’ matrix where fibroblasts are stimulated (by ascorbic acid) to produce collagen, followed by extraction of the fibroblasts (184, 185). Since stroma plays an important part in cancer growth and progression, co-­‐culture of cancer cell and stromal cells in 3D cultures have emerged as a promising method of study. Organotypic models have been utilised in studying this tumour-­‐stromal interaction in different cancers such as skin (186), breast (187), prostate (188) and oesophagus (189). Froeling et al have established and validated organotypic 3D model to study pancreatic cancer and have shown that addition of stromal pancreatic stellate cell significantly modulated the behaviour of cancer cells (1, 190). Table 1.11 lists the different types of 3D models used for studying cancer in different organs. 48 Model Method Organ culture / tissue explant Harvest tissue fragments and culture (181, 182) in vitro 3D matrix culture Cells or spheroids cultured in 3D matrix scaffolds (179, 183) Cell derived matrix Cells are cultured on matrix derived from fibroblasts (184, 185) Organotypic culture Epithelial cells are culture with stromal cells within the matrix (190, 191) References Table 1.11. Types of organotypic 3D models Organ Type Readout method Skin Mixture of keratinocytes & melanoma cell lines overlaid on top of collagen layer containing stromal cells immunostaining – invasion Breast Breast cancer cell lines embedded in 3d matrix scaffold immunostaining -­‐ organisation of cells into acini with polarisation of cells (187) Electron microscopy Immunostaining -­‐ invasion (181) (189) Brain Brain tissue slices Oesophagus Oesophageal cancer cells cultures on collagen gels with stromal cells Reference (186) Pancreas Cancer cell lines and stellate cells Immunostaining -­‐ proliferation, overlaid on collagen / apoptosis, invasion Matrigel(TM) matrix or embedded within the matrix (1) Colon Stem cells co-­‐cultured with paneth cells in 3D matrix (192) Prostate Prostate adenocarcinoma fragments cultured in collagen ‘sponges’ Table 1.12: Types of 3D models used in the study of different organs Development of physiologically relevant organoids resembling crypt like structures identified by immune-­‐staining Tumour cell – stromal interaction analysed by microscopy. (188, 193) 49 Advantages of 3D models Organotypic 3D culture models offer many advantages over traditional 2D models and animal models. The cell morphology and signalling are more physiological than in 2D cultures (194). Pancreatic cancer cells and breast cancer cells have been shown to differentiate into ductal and acinar structures when grown in collagen based 3D matrices and express physiologically relevant cell surface markers (187, 190). The ability to culture more than one cell type provides an opportunity to study the juxtacrine / paracrine effects and a 3D model enables to capture and quantify invasion which is not possible with 2D culture. It provides an excellent platform to study the effects of stromal components on cancer cells and allows answering specific questions regarding tumour-­‐
stromal interactions. It allows the investigator to precisely control different parameters and components of the model and help in targeting specific areas of interest. In contrast to animal models which require a long latency period and are expensive; 3D organotypic models can offer a relatively quick, high-­‐throughput model at the same time maintaining considerable physiological relevance. The inherent problem of host factors, such as immune cells, vascularisation encountered in animal models, is absent in 3D models and, thus, avoids further confounding effects. In addition, 3D models can provide a high throughput system for screening therapeutic agents (195) and since animal models respond and metabolise drugs differently, using biomimetic 3D models can help reduce high failure rate. Organotypic models have been used to propagate and maintain stem cell differentiation in vitro and have shown to develop physiologically relevant organoids that have potential in clinical and therapeutic applications (192). Limitations of 3D models Although 3D models are intended to be physiological, they are experimental models and have limitations in their ability to mimic in vivo tissue conditions. Different tissues in vivo differ in their tissue architecture and composition that can be difficult to faithfully recapitulate in vitro and 3D models cannot reproduce all of the normal micro-­‐environmental multicellular inputs. The composition of the 3D matrix plays an important role in the design of these models (196, 197) and 50 optimal concentrations of the various components need to be present for the cells to respond physiologically. In addition to the composition, matrix stiffness can have significant effects on cell signalling and behaviour(194). For example, when breast carcinoma cells were grown in collagen type I alone, they did not develop into polarised glandular structures, but when basement membrane proteins (MatrigelTM) were added, making the model more elastic and pliable, glandular differentiation was seen (187). Also, 3D models lack the complex vascular structure of in vivo models and oxygenation, nutrition and waste removal occur through diffusion. Therefore, the thickness of the matrix can also adversely affect the nutritional status of the cells in the matrix (178). It is also important to consider that 3D models mimic static or short term conditions compared to the progressive nature of pathology in vivo. Advantages Cell morphology and signalling are more physiological than 2D culture Interaction between different cell types can be studied Rapid experimental manipulations and testing of hypotheses Permits easy imaging and quantification of invasion Stem cell differentiation and maintenance Limitations Vary in their ability to mimic in vivo tissue conditions Matrix composition and stiffness can alter cellular response Thickness of matrix can affect nutritional status of cells 3D models mimic static or short term conditions. Table 1.13. Key advantages and limitations of 3D models Compared to traditional 2D cultures, organotypic models offer distinct advantages and can be considered as bridging the gap between 2D and in vivo animal models (178). Results from 3D models have to be considered in the light of the various limitations and may have to be further tested in vivo. Nevertheless, it provides potentially useful tools for a variety of applications. 51 In our laboratory, the organotypic culture model has been utilised to study the cellular interactions in skin and pancreatic cancer. The composition of the gel matrix is adopted form oraganotypic skin cancer model developed and published by Nystrom et al In our laboratory (198). Extensive work was done by Fraoeling and Mirza with respect to pancreatic cancer organotypic culture models especially in optimising the model for pancreatic cancer (199). Froeling et al has demonstrated the differential expression of of E-­‐Cadherin and ß-­‐Catenin when pancreatic cancer cells were exposed to stellate cells using one such model (200). Most recently Froeling et al studied the effects of ATRA treated stellate cells on pancreatic cancer cells an in vitro organotypic model and demonstrated that rendering the stellate cells quiescent slowed tumour growth and invasion. Subsequently these findings were concurred in the KPC mice in vivo model, providing validation to the organotypic model (201) 52 1.8 Aim In the normal pancreas, the stromal compartment comprises about 4% of the total volume, whereas, in ductal adenocarcinoma, the stroma occupies up to 80% of the tumour volume (64). In keeping with this wide spectrum of tumour / stromal proportions and coupled with the observation that variations in stellate cell activity affects prognosis (75), it can be deduced that tumour behaviour is influenced by the proportions of the two compartments and leads to question the possibility of a ‘tipping point’ wherein the tumour-­‐stromal imbalance is such that it engenders an environment compatible for aggressive tumour behaviour. An excellent method to study tumour-­‐stromal interaction is to employ a well-­‐established physio-­‐mimetic organotypic culture model (1, 190) which enables precise alterations in cellular proportions and where specific outcomes can be objectively measured and analysed. The aim of this project was to study the influence of pancreatic stellate cells on pancreatic cancer cells in a physiological bio-­‐mimetic in vitro organotypic culture model and attempt to dissect out ‘tipping point’ of tumour-­‐stromal proportion that leads to an aggressive tumour phenotype. 53 2. Materials and methods 2.1 Overview Essentially, the experimental design involved the construction of raised organotypic gel culture models with a mixture of cancer cells and stellate cells seeded on top of the gel matrix and harvested following a ten day culture period. The cancer cell-­‐stellate cell mixture was altered in such a way that a series of gels consisted of specific ratios of each cell population in order to recapitulate the varying tumour-­‐stromal proportions as seen in human PDAC. The total number of cells per gel was kept constant while the ratio of cancer cells to stellate cells was varied in an incremental fashion such that the first gel consisted of cancer cells only and the stellate cell proportion was increased in subsequent gels till the last gel consisted of ten times more stellate cells than cancer cells. These ratios were seeded on the gels at the same time at the start of the ten day culture. Two pancreatic cancer cell lines (Capan1 and AsPc1) with stellate cells were used for the experimental arm of the study. Normal pancreatic ductal epithelial cells (DEC) with stellate cells and Capan1 cell line alone without stellate cells was used for the control arm. The experiments were conducted in triplicates (biological and technical). 2.2 Tissue culture 2.2.1 Cell lines, media and culture reagents
Cell lines The human pancreatic cancer cell lines used were Capan1, a well differentiated cell line sourced from a liver metastasis, carrying mutations in KRAS, TP53, p16, DPC4 and BRCA2 (202); and AsPc1, a moderate to poorly differentiated cell line, obtained from ascites fluid of a patient with PDAC which carries mutations in KRAS, TP53 and p16 (202). The hTERT immortalised normal pancreatic ductal epithelial cell line DEChTERT (203) was used as a control. 54 Pancreatic stellate cell line used was PS1 which was obtained from an unused normal human male pancreas (resected for transplantation) donated by the UK human tissue bank (Ethics approval; Trent MREC (05/MRE04/82)). The cells were isolated using the outgrowth technique(82), hTERT immortalised(204) and verified as being stellate cell origin by positive immune-­‐staining for glial fibrillary acidic protein (GFAP), desmin, vimentin and α-­‐smooth muscle actin (190). Human umbilical vein endothelial cells which were hTERT (HUVEC hTERT) immortalised were kindly provided by Professor Tahara (205). STR profiling for the above cell lines is provided in appendix figures 1 – 4. Culture conditions Pancreatic cancer cells Capan1 and AsPc 1 were cultured as adherent monolayers in sterile plastic tissue culture flasks in a humidified atmosphere at 37⁰c and 8% CO2 in RPMI medium (PAA laboratories, #E15-­‐842) supplemented with 10% foetal calf serum. Pancreatic stellate cells (PS1) cells were cultured in similar conditions in DMEM:F12 medium (Invitrogen #11320-­‐074) with 1μg/ml puromycin as a selection agent; and DEChTERT cells were cultured in DMEM supplemented with 10% foetal calf serum. The HUVEC hTERT cells were cultured on plastic precoated with 0.1% gelatine (#G2500, Sigma Aldrich) in medium 199 (M199, # 41150-­‐020 GIBCO), 1% antibiotic / antimycotic (#15240-­‐062, Invitrogen), 5mg of heparin per 100ml of medium; supplemented with 20% FBS and 1% endothelial mitogen (Endothelial Cell Growth Supplement, # 4110-­‐5004, AbD Serotec) When cells reached 80-­‐90% confluency, medium was aspirated and the monolayer was washed with Phosphate Buffered Saline (PBS, CRUK Media Services) followed by Trypsin-­‐EDTA (PAA Laboratories, #L11-­‐003) and incubated for 2-­‐5 minutes at 37⁰C to detach cells from the surface. After confirming that all the cells were detached, medium containing FBS (foetal bovine serum) was added to inactivate trypsin and cell suspension was centrifuged at 1200 revolutions per minute for 3 minutes. The supernatant was discarded and the pellet was resuspended in standard medium. For counting of cells, 20μl of the resuspended cell suspension was pipetted in to a chamber of 55 haemocytometer and manually counted under light microscope, or 20μl was added to 9.98ml of “CASYton”, an isotonic solution for automatic counting in the CASY counter (Scharfe). For cell storage, after trypsinisation, the pellet was suspended at a concentration of 1-­‐
2x106 cells/ml in solution of 90% FBS and 10% Dimethyl Sulphoxide (DMSO) which acted as a cryoprotectant. One ml of cell suspension was pipetted into a cryovial and gradually frozen to reduce the risk of ice crystal formation and was stored in liquid nitrogen. When recovering cells from liquid nitrogen, the cell suspension was thawed quickly in a water bath at 37⁰C to prevent formation of ice crystal and cell damage. Once completely thawed, the cell suspension was transferred into a tube containing pre-­‐warmed standard medium and centrifuged at 1200rpm for 3 minutes to remove DMSO. Supernatant was aspirated and the cell pellet was resuspended in standard medium and plated onto plastic tissue culture flasks. 56 2.3 Reagents 2.3.1 Antibodies Antibodies used are summarised in Table 2.1. Antibody Species raised in Supplier Vector Labs (#VP-­‐
K451) Dilution for IF / IHC Ki67 Rabbit Cleaved caspase-­‐3 Rabbit Cell Signalling (Asp175) (#9664) 1:100 Cytokeratin Mouse Dako Clone AE1/AE3 (M3515) 1:200 Ezrin Mouse BD Biosciences (Clone 18, #610603) 1:500 E-­‐Cadherin Mouse Abcam (Clone HECD-­‐1, #ab1416) 1:200 β-­‐Catenin Mouse BD Bioscience (Clone 14,#610154) 1:200 PIGR (Polymeric immunoglobulin receptor) Rabbit Sigma Aldrich (#HPA012012) 1:1000 Alexa Fluor 488 Goat Invitrogen (#A11029) 1:500 Alexa Fluor 488 Rabbit Invitrogen (#A11034) 1:500 Alexa Fluor 546 Goat Invitrogen (#A11003) 1:500 1:1000 Table 2.1: Antibodies used for the experiments. 2.4 Organotypic culture The organotypic culture model consisted of a matrix of extracellular proteins, which was composed of a mixture of 75% collagen and 25% MatrigelTM as previous work had shown this to be the optimum ratio (190). This model has used in our laboratory and successfully validated in various studies involving pancreatic (198) and skin cancer models (206). One ml of a mixture of 5.25 volumes of Collagen type I, 1.75 volumes of Matrigel, 1 volume of 10x DMEM, 1 volume of filtered FBS and 1 volume of standard DMEM were plated in 24-­‐well plate coated with diluted collagen type I (1:100 in 57 PBS). The matrix was polymerised by incubating it at 37⁰C for one hour. Cell suspension was then added on top of the polymerised matrix and incubated for 24hrs at 37⁰C. After 24hrs, the medium on top of the matrix was aspirated and the gel matrix (with cells attached to the matrix surface) was lifted off the well and raised onto a metal grid which was covered by nylon membranes and placed in a 6-­‐well plate. The nylon membranes were coated with 7 volumes of collagen type I, 1 volume of 10x DMEM, 1 volume of standard DMEM and 1 volume of filtered FBS. The sheets were coated with 250μl of the mixture and set to polymerise at 37⁰C for 15 minutes, cross-­‐linked with 1% gluteraldehyde/PBS and left at 4⁰C for 1 hour. The membranes were then washed in PBS (x3) to remove the toxic gluteraldehyde, covered in medium and left at 4⁰C overnight. The following day, the gel matrix was lifted onto metal grids covered by pre-­‐coated nylon membranes and standard RPMI medium was added from underneath the metal grid to provide a chemo-­‐attractive gradient (Fig 2.1). This model was cultured for 10 days with fresh medium changed on alternate days. After 10 days, the gel matrix was harvested and fixed in 10% formal saline solution and embedded in paraffin for cutting sections. Fig 2.1. Organotypic culture model: Pancreatic cancer and stellate cells are co-­‐cultured on 3D matrix consisting of a 75:25 mixture of collagen and Matrigel. After 24 hours, the model is raised on to a metal grid and medium is added from underneath to provide a chemotactic gradient. The model is cultured for 10 days, following which they are formalin fixed and paraffin embedded for microscopy. A series of gels were made in such a way that specific proportions of cancer cells and PS1 cells were seeded on the gels (Table 2.2), attempting to encompass the entire range of tumour-­‐
stromal composition. The total number of cells plated was kept constant at 5x105, while the proportions of cancer and PS1 cells ranged from cancer cell alone (100%) followed by increasing 58 proportions of PS1 cells and correspondingly decreasing proportions of cancer cells (cancer cell / PS1 – 91.9 / 9.1, 83.3 / 17.7, 66.6 / 33.3, 50 / 50, 33.3 / 66.6, 17.7 / 83.3, 9.1 / 99.9, 0 / 100%). Capan1 / AspC1 PS1 Cancer cell : PS1 (%) (x105) (x105) 1:0 (100% cancer cell) 5 0 10:1 (91.9 : 9.1) 4.55 0.45 5:1 (83.3 : 17.7) 4.17 0.83 2:1 (66.6 : 33.3) 3.3 1.7 1:1 (50 : 50) 2.5 2.5 1:2 (33.3 : 66.6) 1.7 3.3 1:5 (17.7 : 83.3) 0.83 4.17 1:10 (9.1 : 91.9) 0.45 4.55 0:1 (100% PS1 cells) 0 5 Table 2.2: Starting proportions of cancer cells and PS1 cells on pancreatic organotypic gels For submerged organotypics, a suspension of 5x105 PS1 cells in DMEM was added while preparing the gel such that the PS1 cells were submerged within the gel matrix. Following polymerisation of the gel after one hour, 2.5x105 cancer cells were seeded on top of the gel matrix and incubated at 37⁰C for 24 hours after which the gels were lifted on to a metal grid and media added from below. Gels were harvested on day 10, frozen on dry ice in cryo-­‐embedding medium (#6760096; Thermo Scientific) and stored in -­‐80⁰C for RNA extraction. a b Figure 2.2: (a) Submerged organotypic model used for laser capture microdissection. (b) H&E of frozen organotypic section with red line indicating cancer cell layer captured by laser micro-­‐
dissection. 59 Squamous cell cancer organotypics were constructed as described before (2). 5 × 105 HN-­‐CAFs (head and neck carcinoma-­‐associated fibroblasts) were embedded in 2: 1 Collagen I : Matrigel and set at 37 °C for 1 h, after which 5 × 105 SCC12 cells were placed on top in complete media. The gel was then mounted on a metal bridge and fed from underneath with complete media that was changed daily for 7 days after which the gels were harvested for RNA extraction. These experiments were conducted in Erik Sahai’s laboratory at The London Research Institute (CRUK, London, United Kingdom) (2) For oesophageal organotypics, primary oesophageal epithelial cells (keratinocytes) were obtained from normal human oesophagus and hTERT-­‐immortalised to create non-­‐transformed, oesophageal epithelial cells as described previously (207). Further, to mimic the steps of carcinogenesis, supernatants containing pFB-­‐neo retrovirus encoding either a full-­‐length E-­‐cadherin (wild-­‐type: leading to normal oesophageal epithelium formation and no invasion) or dominant-­‐
negative mutant of E-­‐cadherin lacking the cytoplasmic tail (EC: leading to some invasion) were used for transfection of hTERT-­‐ immortalised cells. Empty pFBneo was used as a control. Additionally, a dominant-­‐negative mutant of TGFβRII, sub-­‐cloned into pBABE puro, was used to generate ECdnT cells (Possessing both dominant negative E-­‐cadherin and TGFβRII) and empty pBABE puro as a control. Oesophageal cancer organotypic were constructed by seeding 5 x 105 human oesophageal epithelial cells (wild type or EC or ECdnT) onto a 3:1 Collagen I : Matrigel gel embedded with 7.5 x 104 human foetal oesophageal fibroblasts. Cells were fed with various ‘Epidermalization’ media as described elsewhere (207). On day 11, cultures were raised to an air-­‐liquid interface to induce differentiation of the epithelium. Cultures were harvested on day 15 and directly embedded into Tissue-­‐Tek O.C.T. compound (# 25608-­‐903, VWR) for frozen sections and processed for RNA extraction. These experiments were conducted by Claudia Andl in Anil Rustgi’s laboratory at Vanderbilt University (Nashville, Tennessee, USA) (208). 60 2.5 Immunohistochemistry and immunofluorescence staining Paraffin embedded organotypic sections of 4μm thickness were dewaxed in xylene (5min x2) and rehydrated (100%, 80%, 70%, 50% ethanol and distilled water). Antigen retrieval for all antibodies was by heat induced method, by boiling sections in 10mM citrate buffer solution at pH 6 for 10 minutes. For immunohistochemistry, endogenous peroxidase was blocked with 3% hydrogen peroxide in methanol. Invasion and apoptosis were assessed by staining the sections with primary antibodies to cytokeratin and cleaved caspase-­‐3, incubated overnight at 4⁰C, followed by 1 hour incubation with biotinylated secondary antibody. Peroxidase labelled Avidin-­‐Biotin complex (# PK4000, Vectastain ABC kitTM, Vector Laboratories) was added and visualised using 3,3-­‐
diaminobenzidine tetrahydrochloride (DAB) followed by counter staining with haematoxylin. For immunofluorescence, following dewaxing, rehydrating and heat induced epitope retrieval as described above, the sections were permiabalised with 0.2% TritonX-­‐100 and blocked with 2% bovine serum albumin (BSA) + 0.02% fish skin gelatine + 10% FBS. Primary antibodies were incubated at 4⁰C overnight. Immunofluorescence was used to assess proliferation (Ki67) and the expression of Ezrin, E-­‐cadherin , β-­‐catenin and PIGR (polymeric immunoglobulin receptor). The slides were then incubated in Sudan Black for 20 minutes to reduce auto-­‐fluorescence. Following this, fluorescent labelled secondary antibodies (Alexa fluorTM 488, 546) were incubated at room temperature for 1 hour and nuclei were counterstained with DAPI. 2.6 Laser capture micro-­dissection and RNA extraction For laser capture, organotypic gels were frozen on dry ice and 20μm thick sections were obtained on PEN (polyethylene naphthalate) membrane slides (#415190-­‐9041, Carl Zeiss). Before sectioning, the slides were treated with RNaseZap (#AM9780, Ambion) to eliminate RNAase activity and were irradiated with UV light (Stratalinker®, Stratagene) to render it hydrophilic. The sections were immediately fixed in ice-­‐cold 70% ethanol and stained with haematoxylin for 2min and followed by short incubations in 70%, 95% and 100% ethanol for 30 seconds each to dehydrate the 61 sections and were air-­‐dried. The slides were then immediately subjected for laser micro-­‐dissection. The cancer cell layer (stained with haematoxylin) on top of the organotypic gel matrix was captured using PALM laser capture microscope (P.A.L.M MicroBeam, Carl Zeiss). The captured cells were then incubated at 42⁰C with RNA lysis solution (#AM1931, Ambion RNAqueous micro kit) followed by RNA extraction using the Ambion RNAqueous Micro Kit as per manufacturer’s instructions. Briefly, the lysate was treated with 3μl LCM additive and 100% ethanol added to precipitate total RNA. The lysate ethanol mixture was added onto a filter and centrifuged to allow RNA to bind to the filter. Following serial washes to purify, the RNA was eluted using elution solution to a volume of 20μl. The quality and quantity of the extracted RNA was assessed by spectrophotometry (Nanodrop) and by running the samples in the Agilent bioanalyzer 2001TM system. After confirming the quality and quantity, the samples were hybridised onto the HumanHT-­‐12 v4 BeadChipTM (illumina) gene chip microarray consisting of 47,231 transcripts. The key step in laser capture microdissection was to keep RNA degradation to a minimum and a number of precautions were taken to ensure this. First of all, the time taken from sectioning in the cryostat to laser capture to RNA extraction were kept as short as possible. This was time consuming and labour intensive but yielded the best results. Secondly, the organotypic model was modified such that only the pure epithelial cell layer on top of the gel matrix was captured and the resolution of the laser used to capture was set at high level to avoid any contamination from the surrounding matrix containing the stellate cells. Lastly, utmost care was taken during the post-­‐
capture processing and RNA extraction. This was ensured by performing all the steps of RNA extraction in a separate hood specially designed for RNA work. Similarly, for skin organotypics, (done in Erik Sahai’s lab (2)), before microdissection, P.A.L.M slides (1 mm pen slides, P.A.L.M. Microlaser Technologies AG, Bernried, Germany) were first treated with RNAse Zap treatment for 2min and then washed three times in DEPC water. The slides were sterilised in UV light for 30 min in a UV stratalinker (Stratagene, La Jolla, CA, USA). Frozen 62 Sections were mounted and fixed in Ethanol 70% 15secs, washed in DEPC water 5secs, stained with haematoxylin for 1min and washed twice for 1 min in DEPC water. Dehydration followed, in ethanol (50% ethanol 30s, 70% ethanol 30s, 100% ethanol 1min). Slides were then ready after drying for processing by laser-­‐assisted microdissection (LAMD). Microdissection was performed with a high-­‐
resolution nitrogen UV laser (P.A.L.M. MicroLaser System, Zeiss, Oberkochen, Germany). In brief, RNA was extracted from laser microdissected tissue samples using the Qiagen RNeasy Micro Kit following manufacturer’s instructions. Frozen oesophageal organotypic cultures (done in Claudia Andl’s lab (208) were sectioned to 8 μm onto membrane-­‐mounted metal frame slides (MMI) using a Microm HM 505E cryostat (Richard Allen Scientific). Sections were immediately fixed, stained, and dehydrated before laser microdissection. Microdissection was performed with a Nikon Eclipse TE 2000-­‐5 microscope with a UV laser (MMI). 2.7 Gene expression microarray analysis For pancreatic cancer organtypics, 250 ng of the extracted RNA was labeled using MessageAmp™ III RNA Amplification Kit (Ambion, manufacturer’s instructions). Similar experiments, in duplicate, were done with RNA extraction from skin and oesophageal organotypics. 1.5μg of labeled cRNA was hybridized on to Illumina Human-­‐12 v4 Expression BeadChips (47,231 probes). Quality Control and normalization were performed using GenomeStudio v2011.1 (Illumina). Statistical analyses were performed using Bioconductor (www.bioconductor.org) packages within the open source R statistical environment (www.r-­‐project.org). A filter was applied using the standard deviation of gene expression values to select the top 10,000 probes on Human-­‐12 v4. Limma package (fit a linear model to the expression data of each probe) for differential expression analysis (209) and Empirical Bayes was used to borrow information across genes for stable analyses. We set a double threshold for significant changes in gene expression with a False Discovery Rate (FDR) <= 0.05 and an absolute fold change > 2. 63 RNA from oesophageal organotypics was isolated using laser-­‐capture microdissection of frozen tissue sections using the PicoPure RNA isolation kit from Arcturus (Invitrogen, Carslbad, CA). Purified total RNA was Biotin-­‐labeled and hybridized onto Affymetrix GeneChip U133 Plus 2.0 oligonucleotide array. RNA from skin organotypics was extracted using the Qiagen RNAeasy Micro Kit following manufacturer’s instructions. The microarray data were analysed with Microarray Suite version 5.0 (MAS 5.0) algorithm using Affymetrix default parameter settings and global scaling as normalization method. The MAS 5.0 scaled data was then log2 transformed for the following differential analysis. Limma package was used for the two-­‐group and multiple-­‐group (for oesophageal data) differential expression analysis. Since the number of replicates was low (2) for each group, the significance threshold of raw p-­‐value < 0.01 was applied (210), and an absolute fold change of >2 was applied for two-­‐group comparisons. Common genes differentially expressed among the pancreatic, skin and oesophageal datasets were identified by generating the Venn diagram. For the comparison between the pancreatic and skin datasets, all shared genes profiled from the two experiments were identified and the relationship was investigated in fold changes (log2 scaled) for those genes between the two datasets. For the hierarchical clustering analysis, dissimilarity matrices were measured and samples were clustered using Ward’s algorithm, with probesets/genes clustered using average linkage. The heat maps were generated using “heatmap.2” from the R package gplots and the Venn diagram generated using the R code available from (http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/overLapper.R). The raw data of the micro-­‐array experiments are deposited on the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are available with accession number GSE36775 (pancreatic), GSE36776 (skin) and GSE19472 (oesophageal); the data were MIAME compliant. These analyses were done with the help of Jun Wang of Claude Chelala’s team (Bioinformatis, Centre for Molecular Oncology, Barts Cancer Institute). 64 2.8 Quantitative real-­time PCR (qRT-­PCR ) qRT-­‐PCR was performed using the Applied Biosystems Step OneTM real time PCR systems. After a panel of topmost up or down regulated genes (Table) were selected following gene expression microarray analysis, primers were designed using the online primer design tool available at http://www.ncbi.nlm.nih.gov/tools/primer-­‐blast. For the PCR reaction, SensiFAST SYBR Hi-­‐ROX One-­‐Step KitTM (#73005; Bioline), formulated for first-­‐strand cDNA synthesis and subsequent real-­‐
time PCR in a single tube, was used. 25ng of RNA was added to the master mix consisting of SensiFAST SYBR Hi-­‐ROX One-­‐Step mix (2x), reverse transcriptase, RNase inhibitor, forward and reverse primers (400nM final concentration) and DEPC treated water made up to a final volume of 10μl. Fold change was calculated using the comparative Ct (∆∆Ct) method(211) after normalising for endogenous 18s expression. 65 Gene
CCNA2
CCNB2
CDC25B
CDCA3
CDK1
DKK1
KIF23
MMP1
PIGR
RARRES3
RARRES1
SAA1
SAA2
TNFAIP3
TOP2A
Wnt7B
KIF11
Primer sequence
Forward
GTCCAACAACCGCG
GACCC
TTCAGTCCGCGTCC
CTCCCT
GCGGGCACATCAA
GACTGCG
AATTGGGGCCCGG
CATTCCC
CGGAGAGCGACGC
GGTTGTT
CGCGCCGGGAATC
CTGTACC
TATTGCTCAGATTTC
CAACGGCCAG
TGTCAGGGGAGATC
ATCGGGACA
GAAGTCGGCCTGA
GTGGTGCG
TGGCTTCGCCACAC
CAAGAGC
CAACTTCCGGTCCG
GCTCGC
CAAAAGGGGACCTG
GGGGTGC
ACCTGGCTATGAGG
CCCTCGG
GCAACTGAGATCGA
GCCAGCG
GATGCAGGGGGCC
GAAACTCC
ACGTGAAGCTCGGA
GCACTGTC
TGCGTATGGCCAAA
CTGGCACT
Reverse
GTGCAACCCGTCT
CGTCTTCGG
TCACTGGACACCG
TCGGGCG
TCCAGGCTACAGG
GCGCGAT
TGTGACTGGGACG
CTCTTGGC
TCAATGGGTATGG
TAGATCCCGGC
CGCACGGGTACG
GCTGGTAG
GCAAGAGTTAGAG
CGCCTATGTAGC
TCCCCTCCAATAC
CTGGGCCTG
GGAGATGGCTGG
GAAGACCGC
AGGGCCCAGTGCT
CATAGCCA
GATTAATCCACGC
GCGGCCCT
TCCTCCGCACCAT
GGCCAAA
TGCCATATCTCAG
CTTCTCTGGACA
GGGCGCACTTGG
GCCTTGAG
TCCCAACCACACC
AAGGCCTGA
GCGGGGCTAGGC
CAGGAATCT
GCCAAGGGATCCT
CTTCCCAGGT
Product
length
Tm
Length
Fwd Rev
Fwd
Rev
58.1
60.05
19
22
59.26
61.14
20
20
58.97
60.04
20
20
59.68
59.05
20
21
60.05
57.84
20
24
59.91
59.57
20
20
57.63
57.63
25
25
58.61
59.38
23
22
60.05
59.25
21
21
59.91
59.63
21
21
60.38
59.92
20
21
59.57
58.61
21
20
59.77
57.3
21
25
58.25
60.59
21
20
59.78
59.98
21
22
59.18
60.38
22
21
59.34
59.67
22
23
100
90
90
99
77
71
70
93
92
76
74
90
96
71
97
81
93
Table 2.3: List of topmost differentially expressed genes and their primer sequences for qRT-­‐PCR 66 2.9 Pathway analysis Ingenuity Pathway analysis (IPA, http://www.ingenuity.com/) was used to dissect out common canonical pathways, cellular and biological functions and molecular networks affected in cancer cells upon exposure to stellate cells. Scores were generated for each aspect to demonstrate the likelihood that transcripts associated with the affected genes have not been selected by random chance (e.g., a score of 2 gives a 99% confidence, with higher scores signifying greater confidence). The global functional analysis feature calculated the significance with the right-­‐tailed Fisher's exact test, with a p-­‐value of <0.05 being significant, after applying Benjamini-­‐Hochberg approach for multiple testing(210). These were plotted on logarithmic scale for the range of p-­‐values for individual sub-­‐
functions of the main cellular functions such as cell cycle, cell death and movement, cellular growth and proliferation, cell signalling and inflammatory response. 2.10 Quantification 2.10.1 Gel length Organotypic length was measured by summating the length of serial low power (50x) fields (AxioplanTM microscope (Carl Zeiss)) run across the gel from end to end limiting to within the area of cellularity to avoid edge artefacts. 2.10.2 Gel thickness Thickness of organotypic gels was measured by averaging four vertical measurements along the length of the gel limiting to the cellular field avoiding the contracted edges. 2.10.3 Cancer cell fold change Cancer cell counts were obtained in six random high power fields (200x) per gel. L2h/lt was used to estimate the total cancer cell count, where L is the gel length, h is the number of cells per high power field, l is the length of one high power field and t is the thickness of the section (usually 4μm) (Fig 2.2). This formula was derived assuming that the gels were uniformly circular and that they were sectioned through their diameter and that the thickness of all the sections was 4µm. The Fold 67 change was arrived at by calculating the ratio between the cancer cell counts at the end of the experiment to the start. Total no. of cells in gel = no. of cells in one section (1) x no. of sections in one gel (2) (1) No. of cells in one section = no. of cells in one hpf x no. of hpf that make one section = (2) No. of sections in one gel = Total no. Of cells per gel = = Figure 2.3: Formula to calculate the total number of cells per gel 2.10.4 Proliferation, apoptosis and invasion The quantification of all counts of both Ki-­‐67 positive or cleaved caspase 3 postive cells and total cells was done in at least 6 representative pictures per gel / ratio and the percentage of positive cells for each marker was calculated. Analysis was limited to the non-­‐invading cancer cell layer which was distinguished by nuclear morphology and by remaining above the ‘demarcating’ stellate cell layer. Each experiment had 3 technical repeats and at least 3 biological repeats by two independent observers blinded to the ratios of cancer cell/stellate cells. 68 For invasion, Organotypics were stained for epithelial specific marker cytokeratin to differentiate from PS1 cells and the absolute number of invading single cancer cells and cohorts were counted in six random high power fields per gel / ratio. Clumps of two or more than two cells were counted as a cohort. Figure 2.4: Proliferation was quantified by counting the number of Ki67 positive cells (white arrow head) and the total number of cells (DAPI in blue) and calculating the percentage. Counting was was limited to the non invading cancer cell layer above the stellate cell layer (pink line). 69 Figure 2.5: Apoptosis was quantified by counting the number of Caspase positive cells (black arrow head) and the total number of cells (haematoxylin) and calculating the percentage. Counting was was limited to the non invading cancer cell layer above the stellate cell layer (black line). Cytokeratin Figure 2.6: Invasion was quantified by counting the total number of cohorts (black arrows) and the number of single cells (arrow head). Invading into the gel matrix 2.10.5 Ezrin, E-­cadherin, β-­catenin, PIGR expression Confocal images of 6 random high power fields per ratio were captured for analysis of expression of all the four proteins. The intensity of fluorescence in the green channel for all the four proteins was 70 calculated using Image J software. The threshold was set according to intensity of green channel in the cancer cell only organotypics, to which all subsequent readings were normalised. Cellularity was ascertained by counting cells in DAPI channel and the green fluorescent intensity per cell in the non-­‐
invaded cancer cell layer was calculated. The following steps were involved in calculating the intensity using image J software: 1. Splitting image into individual channels 71 2. Setting of pixel intensity threshold in the green channel 72 3. Measurement of intensity in the given area 4. Counting total number of cells and calculating intensity per cell 73 2.11 Statistical analysis For all experiments with organotypic cultures, at least 3 gels from 3 separate experiments for each ratio were analysed. The statistical analysis was done with the help of Hanna Birke of Peter Sasieni’s team at the Wolfson Institute of Preventive Medicine (London, UK). A form of trend test was conducted to analyse the association between the different proportions of stellate cells and the outcome variables. Linear association was investigated for the experiments, however, a constant for the controls. A linear regression model was fitted for each cell line and compared to a null model. Since the data were measured in batches, all models are adjusted for batch variation. A formal test was used comparing the difference of the deviances. The P value was computed by referring the difference to a χ2 distribution on 9 degrees of freedom (df) for the experiments and 2 df for the controls. The normality assumption of the residuals was tested by a Shapiro-­‐Wilk test. The homogeneity of variance was analyzed by a plot of the residuals versus the fitted values. A transformation of the outcome variable, either logarithm or square root, was used if the model fit was better regarding to the normality assumption, diagnostics plots and R². Additionally, a fractional polynomial (FP) regression model(212) was fitted to analyse if the association was non-­‐linear. The degree of FP to fit was set to two. Again, a formal test was used to contrast the FP with the linear model. The difference of the deviances was referred to a χ2 distribution on 3 df. For E-­‐cadherin and β-­‐catenin we included an extra dummy variable, which modulates the proportion of stellate cells equals to zero separately, resulting in a better model fit. Furthermore, a regression model was fitted, including the data of the experiment and the control to examine if the effect of stellate cells differed between the two groups. An interaction term between the proportion of stellate cells and a group dummy variable was included into the model. The model type was chosen by the most significant model for the experiment. A likelihood-­‐
ratio test was used to test the significance of the interaction term. The test statistic follows approximately a χ2 distribution, whereas df depend on the chosen model type (1 df for linear model, 74 2 df for FP). In the case of invasive cells, this part was missed out, since we have no control and a negative binomial regression model was considered, because count data were given and the variance is greater than the mean. For all outcome variables the fitted FP with two power terms was plotted giving everyone the flexibility. If a transformed outcome variable was considered the plotted predicted values are transformed back to the normal scale. In all tests a P value less than or equal 0.05 was considered statistical significant. The statistical analysis was performed using STATA 11.2 (StataCorp, 2009). 75 3. Results -­ I 3.1 The cancer cell-­stromal cell interaction leads to extra-­cellular matrix gel contraction The most striking feature, when pancreatic cancer cells (either Capan1: well-­‐differentiated or AsPc1: moderate-­‐poorly differentiated) were admixed with varying proportion of stellate cells (PS1(204)) on top of extracellular matrix (ECM: mixture of Collagen 1:Matrigel) gel(201), was the contraction of the ECM gels (Figure 3.1 and Fig 3.2 b-­‐i). The measure of contraction appeared to be in relation to the proportion of stellate cells in the gels. As the number of PS1 cells increased, the extent to which the gels contracted correspondingly increased. This characteristic was particularly prominent as the stellate cell proportion in the gels was two to three times that of cancer cells (stellate cell proportion of 0.66-­‐0.83) (Figure 3.6 and 3.7). This also appeared to correlate with the degree of stiffness of the gels. While harvesting the gels at the end of ten days, I observed that the gel with more number of stellate cells then cancer cells were stiffer than the ones with less or no stellate cells. 3.2 Increase in stellate cells causes an increase in total number of cancer cells. In addition to gel contraction as described above, it was evident that the epithelial cell layer thickness, as well as invasion of cancer cells were more prominent when stellate cells were the predominant cell type in starting cellular admixture (Figure 3.4 and 3.5). In order to ascertain that the cellular crowding produced by gel contraction was not introducing this artefact, the final (non-­‐
invaded) cancer cell count was estimated and, the fold change in cancer cell numbers was calculated over a ten day period. This clearly demonstrated a maximal fold change in cancer cell population in the presence of more stromal cells (maximal effect started at a stellate cell proportion of 0.66). In comparison, the control experiments (cancer cells alone, epithelial-­‐stellate cell admixture) did not demonstrate a similar rise in fold change of cancerous or normal epithelial cells (Figure 3.8). 76