Drug Discovery in Pancreatic Cancer (eBook)
XIX, 297 Seiten
Springer New York (Verlag)
978-1-4419-1160-5 (ISBN)
Pancreatic cancer is the fourth leading cause of cancer death in the United States. Every year, about 33,700 people in the United States will be diagnosed with pancreatic cancer and over 32,000 patients will die from the disease. The median survival of patients with advanced pancreatic cancer is about 6-months. This dismal picture of pancreatic cancer is mainly due to the lack of early diagnosis and effective treatment for patients with advanced disease. To increase the survival rate of pancreatic cancer patients, better tumor markers for diagnosis and new molecular targets for drug development are desperately needed. A lot of effort has been made in searching for pancreatic cancer-causing genes or genes associated with progression of malignant behavior in pancreatic cancer. As a result, alterations in the expression of several cancer-related genes have been identified in pancreatic tumors. The identification and characterization of these cancer-related genes have significantly increased our understanding of pancreatic cancer development, but unfortunately the treatment of pancreatic cancer has not advanced as much in the past 20 years.
Over the past decade, tremendous advances have been made in the field of cancer drug discovery, particularly, in the area of molecular and genetic models and technologies. Many of those advanced models and technologies have been applied to the drug discovery processes for pancreatic cancer. In this book, a team of experts will describe the latest development in the application of these models and technologies in pancreatic cancer. The authors include basic researchers as well as clinicians who work in the front-line of the war against pancreatic cancer and have the first-hand experience on these cutting-edge tools and techniques. The book can be divided into two general areas: 1) model systems and 2) genomics and proteomics tools. In recent years there have been a lot of advances in the model systems for pancreatic cancer, including the further characterization of normal and cancerous pancreatic cell lines, the establishment of transgenic mouse models that recapitulate the initiation and progression of human pancreatic cancer, the development of a new xenograft model system for the evaluation of novel agents, and the establishment of a zebrafish pancreatic cancer model. The first four chapters of the book will be devoted to these models. The advances in genomics and proteomics research have made a major impact in cancer drug discovery. A number of these -omics-based tools and techniques have been applied in the pancreatic cancer drug discovery. Chapters 5-9 of the book will discuss techniques for genome-wide examination of gene expression, copy number, methylation, function and regulation. Chapters 10-11 will discuss in situ techniques for studying chromosomal and gene copy number abnormalities as well protein expression changes in cancer samples. Chapters 12-14 will focus on techniques for global examination of protein expression levels in biospecimens obtained from pancreatic cancer patients. Cancer drug discovery has become more and more target-centric.
Pancreatic cancer is the fourth leading cause of cancer death in the United States. Every year, about 33,700 people in the United States will be diagnosed with pancreatic cancer and over 32,000 patients will die from the disease. The median survival of patients with advanced pancreatic cancer is about 6-months. This dismal picture of pancreatic cancer is mainly due to the lack of early diagnosis and effective treatment for patients with advanced disease. To increase the survival rate of pancreatic cancer patients, better tumor markers for diagnosis and new molecular targets for drug development are desperately needed. A lot of effort has been made in searching for pancreatic cancer-causing genes or genes associated with progression of malignant behavior in pancreatic cancer. As a result, alterations in the expression of several cancer-related genes have been identified in pancreatic tumors. The identification and characterization of these cancer-related genes have significantly increased our understanding of pancreatic cancer development, but unfortunately the treatment of pancreatic cancer has not advanced as much in the past 20 years.Over the past decade, tremendous advances have been made in the field of cancer drug discovery, particularly, in the area of molecular and genetic models and technologies. Many of those advanced models and technologies have been applied to the drug discovery processes for pancreatic cancer. In this book, a team of experts will describe the latest development in the application of these models and technologies in pancreatic cancer. The authors include basic researchers as well as clinicians who work in the front-line of the war against pancreatic cancer and have the first-hand experience on these cutting-edge tools and techniques. The book can be divided into two general areas: 1) model systems and 2) genomics and proteomics tools. In recent years there have been a lot of advances in the model systems for pancreatic cancer, including the further characterization of normal and cancerous pancreatic cell lines, the establishment of transgenic mouse models that recapitulate the initiation and progression of human pancreatic cancer, the development of a new xenograft model system for the evaluation of novel agents, and the establishment of a zebrafish pancreatic cancer model. The first four chapters of the book will be devoted to these models. The advances in genomics and proteomics research have made a major impact in cancer drug discovery. A number of these -omics-based tools and techniques have been applied in the pancreatic cancer drug discovery. Chapters 5-9 of the book will discuss techniques for genome-wide examination of gene expression, copy number, methylation, function and regulation. Chapters 10-11 will discuss in situ techniques for studying chromosomal and gene copy number abnormalities as well protein expression changes in cancer samples. Chapters 12-14 will focus on techniques for global examination of protein expression levels in biospecimens obtained from pancreatic cancer patients. Cancer drug discovery has become more and more target-centric.
Foreword 5
Preface 6
Contents 7
Contributors 15
Drug Evaluations in Pancreatic Cancer Culture Systems 18
Abstract 18
1.1 Introduction 19
1.2 Types of Cells and Cell Lines 19
1.2.1 Normal (Non-Cancerous) Cells 19
1.2.1.2 HPNE (Human Pancreatic Nestin Expressing Cells) 20
1.2.2 Pancreatic Cancer Cell Lines in Culture 20
1.2.3 Rodent Pancreatic Cancer Cell Lines 21
1.2.4 Insulin-Secreting Cell Lines 22
1.2.5 Stem Cells 22
1.2.6 Advantages 23
1.2.7 Limitations 24
1.3 Types of Culturing Systems 24
1.3.1 Dish Cultures 24
1.3.2 3D Cultures 25
1.3.3 Co-Cultures 25
1.3.4 High-Throughput Screening 26
1.4 Applications for Pharmacotherapeutic Evaluations 26
1.4.1 Signal Transduction Pathways 26
1.4.1.1 MAPK 27
1.4.1.2 JAK/STAT 27
1.4.1.3 Protein Kinase C 28
1.4.1.4 PI3K 28
1.4.1.5 AKT (Protein Kinase B) 29
1.4.2 Receptor Tyrosine Kinases 30
1.4.3 VEGF 32
1.4.3.1 Anti-VEGF Abs 32
1.4.3.2 Other Anti-VEGF Therapies 33
1.4.4 NF-.B (Nuclear Factor-kappaB) 33
1.4.5 Cell Cycle 35
1.5 In Vitro Analyses 35
1.5.1 Apoptosis 35
1.5.2 Cell Proliferation 35
1.5.3 DNA Synthesis 36
1.5.4 Cell Viability in Cell Populations 37
1.5.5 Cell Cycle Parameters 38
1.5.6 Invasion/Metastasis 39
1.6 Concluding Remarks 39
References 40
Mouse Xenograft Models for Drug Discovery in Pancreatic Cancer 45
2.1 Introduction 45
2.2 Classical Drug Development Program at the NCI 46
2.3 New Animal Models 48
2.3.1 Orthotopic Tumor Models 48
2.3.2 Genetically Engineered Mouse Models 50
2.3.3 Humanized Mice 51
2.3.4 Freshly Heterotransplanted Human Tumor Xenografts 52
2.4 Interpreting Results: Variables and Endpoints 53
2.4.1 Growth Characteristics of the Model 54
2.4.2 Strain of Mice 54
2.4.3 Stage at Which the Treatment Begins 54
2.4.4 The Test Compounds 55
2.4.5 Pharmacokinetics 55
2.4.6 Endpoints 55
2.5 Future Approaches in Drug Discovery: Biomarkers and Personalized Therapies 56
2.5.1 Predictive Biomarkers 57
2.5.2 Models and Techniques for Personalized Treatments 59
2.5.2.1 “Tailor-Made” Treatments 59
2.5.2.2 “Prêt-à-porter” Treatments 60
2.6 Concluding Remarks 60
Fluorescent Metastatic Mouse Models of Pancreatic Cancer for Drug Discovery 66
3.1 Introduction 67
3.2 Green Fluorescent Protein (GFP) Models of Pancreatic Cancer 70
3.2.1 GFP Models of Pancreatic Cancer 70
3.2.2 Tumor Selective Metastatic Organ Targeting (Bouvet et al. 2000b) 70
3.2.3 Real-Time Simultaneous Whole-Body Imaging of BxPC-3-GFP Tumor and Multiple Metastatic Growth (Bouvet et al. 2002) 72
3.2.4 Sequential Intravital Images of Omental and Liver Micrometastasis of BxPC-3-GFP (Bouvet et al. 2002) 74
3.3 Red Fluorescent Protein (RFP) Models of Pancreatic Cancer 75
3.3.1 Red Fluorescent Protein Models of Pancreatic Cancer (Katz et al. 2003a, c, 2004b Bouvet et al. 2005
3.3.2 Sensitivity of Fluorescence Imaging 76
3.3.3 Use of RFP Models for Drug Discovery and Evaluation 78
3.4 Dual-Color Models of Pancreatic Cancer 80
3.4.1 Dual-Color Imaging of Nascent Blood Vessels Vascularizing Pancreatic Cancer in an Orthotopic Model Demonstrates Anti-Angiogenesis Efficacy of Gemcitabine (Amoh et al. 2006a) 80
3.4.2 Dual-Color Imaging of Nascent Blood Vessels Vascularizing Liver Metastasis of Pancreatic Cancer also Demonstrates Anti-Angiogenesis Efficacy of Gemcitabine (Amoh et al. 2006b) 82
3.5 Concluding Remarks 83
A New Preclinical Paradigm for Pancreas Cancer 88
4.1 Introduction 88
4.1.1 Delayed Diagnosis in a Rapidly Lethal Disease 89
4.1.2 Diagnostics and Measures of Treatment Efficacy 89
4.1.3 Lost in Translation 90
4.2 Cancer as a Complex Organ 91
4.3 Human Disease in a Mammalian Surrogate: Modeling Pancreas Cancer in Mice 92
4.4 Lessons Learned from Modeling in Mice 93
4.5 Defining a Way Forward 96
4.6 Criteria for a Valid Mouse Model of Disease 97
4.7 Coordinated Targeting of Stroma and Epithelium: Getting Through the Shield to Hit the Heel 97
4.7.1 Cell Autonomous Targets 97
4.7.2 Non-cell Autonomous Targets 98
4.7.2.1 Immune Cells 99
4.7.2.2 Pancreatic Stellate Cells 100
4.7.2.3 Angiogenesis 100
4.8 Putting Principles into Practice 101
4.8.1 Risk Factors and Chemoprevention 101
4.8.2 Early Detection, Disease Recurrence, and Minimum Residual Disease 101
4.8.3 Target Inhibition 102
4.8.4 Acquired Resistance 102
4.8.5 Imaging 102
4.8.6 Sequencing of Agents and Dosing Schedules 103
4.9 Concluding Remarks 105
Zebrafish as a Biological System for Identifying and Validating Therapeutic Targets and Compounds 109
5.1 Introduction 110
5.1.1 Role of Zebrafish in Pancreatic Cancer 110
5.1.2 Zebrafish Model as a Biological System to Identify Molecular Targets and Validate Drugs 110
5.2 Zebrafish Models and Techniques for Drug Discovery in Pancreatic Cancer 111
5.3 Wild-Type Zebrafish 113
5.3.1 Exocrine Pancreas of Wild-Type Zebrafish as Models 113
5.3.2 Techniques in Wild-Type Zebrafish for Drug Discovery 114
5.4 Germ-Line Mutants 115
5.4.1 Zebrafish with Germ-Line Mutations Affecting Exocrine Pancreas as Models 115
5.4.2 Techniques in Zebrafish with Germ-Line Mutations for Drug Discovery 117
5.5 Transgenics 118
5.5.1 Zebrafish Developing Pancreatic Cancer as Models 118
5.5.2 Techniques in Genetically Engineered Zebrafish for Drug Discovery 120
5.6 Transplants 120
5.6.1 Zebrafish Xenograft of Pancreatic Cancer as Models 120
5.6.2 Techniques in Zebrafish Xenograft Models for Drug Discovery 122
5.7 Concluding Remarks 122
Gene Expression Arrays in Pancreatic Cancer Drug Discovery Research 127
6.1 Introduction 127
6.2 Gene Expression Profiling Using DNA Microarray 128
6.2.1 Gene Expression Microarray Platforms 128
6.2.2 Technical Considerations 130
6.3 Application of Microarray Data in Drug Discovery for Pancreatic Cancer 130
6.3.1 Reclassifying Cancer Types 131
6.3.2 Characterizing Known Cancer Classes 131
6.3.3 Identifying New Classes 133
6.3.4 Expression Signatures of Cancer Subclasses 133
6.3.5 From Lists of Differentially Expressed Genes to Drug Targets 135
6.3.6 Gene Expression Signatures as a Drug Discovery Tool 137
6.4 Other Potential Applications of DNA Microarray 138
6.4.1 Biomarkers 138
6.4.2 Drug Resistance 139
6.4.3 Incorporating Gene Expression Arrays with Model Organisms 140
6.4.4 Integrative High-Throughput Analyses 141
6.5 Concluding Remarks 142
Using Array Comparative Genomic Hybridization of Pancreatic Cancer Samples to Map Interesting Regions for Target Gene Identification 149
7.1 Introduction 149
7.2 Alteration of the Genome Is Central to the Causation of All Cancer 150
7.3 Development of Array Comparative Genomics Hybridization and Platform Comparison 151
7.4 Analysis of Array CGH Data 153
7.5 Pancreatic Tumor Samples Analyzed and Findings 154
7.6 Further Analysis to Validate Regions and Identify Cancer Genes 161
7.7 Concluding Remarks 163
The Application of High-Throughput RNAi in Pancreatic Cancer Target Discovery and Drug Development 166
8.1 Introduction 166
8.2 RNA Interference (RNAi) 167
8.3 RNAi High-Throughput Screening 169
8.4 High-Throughput RNAi for Target Identification and Its Application to Pancreatic Cancer 173
8.5 High-Throughput RNAi in Drug Discovery and Development and its Application to Pancreatic Cancer 177
8.6 Concluding Remarks 181
8.5 High-Throughput RNAi in Drug Discovery and Development and Its Application to Pancreatic Cancer 177
MicroRNA Profiling and Its Application in Drug Discovery in Pancreatic Cancer 184
9.1 Introduction 184
9.2 miRNA Profiling 185
9.2.1 Microarray Technology 186
9.2.2 Bead-Based Method Using Flow Cytometry 186
9.2.3 Cloning Methods—miRNA Serial Analysis of Gene Expression 187
9.2.4 RNA-Primed Array-Based Klenow Enzyme Assay 188
9.3 Study of miRNA in Pancreatic Cancer 188
9.4 Application of miRNA Profiling in Drug Discovery in Pancreatic Cancer 190
9.5 Concluding Remarks 191
Methylation Detection and Epigenomics in Pancreatic Cancer 194
10.1 Introduction 194
10.1.1 Pancreatic Cancer Is a Genetic Disease 194
10.1.2 Precursor Lesions to Pancreatic Cancer 196
10.2 Epigenetic alterations in pancreatic cancer 196
10.2.1 Histone Modifications 197
10.2.1.1 Alterations in Histone Methylation 197
10.2.1.2 Alterations in Histone Acetylation 199
10.2.2 Global DNA Methylation Studies 200
10.2.2.1 DNA Hypomethylation in Pancreatic Cancer 202
10.2.2.2 DNA Hypermethylation in Pancreatic Cancer 204
10.2.2.3 Gene Promoter Hypermethylation in Precursor Lesions of Pancreatic Cancer 205
10.2.3 Epigenetic Regulation of miRNA in Cancer 206
10.2.4 Aberrant DNA Methylation Patterns as Potential Biomarkers of Pancreatic Cancer 206
10.3 The Pancreatic Cancer Genome Project 207
10.4 Concluding Remarks 208
Tissue Microarray Applications in Drug Discovery for Pancreatic Cancer 218
11.1 Introduction 218
11.2 FFPE Tissues in Cancer Research 219
11.3 Tissue Microarrays 220
11.3.1 Types of Tissue Microarrays 221
11.3.2 Construction Equipment 222
11.3.3 Construction Process 223
11.4 Representation of Tissue 223
11.5 Applications of TMAs 224
11.5.1 Frozen Tissue Arrays 224
11.5.2 Clinical Applications Using TMAs 225
11.5.3 Cell Line Material TMAs 225
11.6 In Situ Techniques 226
11.6.1 Immunohistochemistry 227
11.6.2 Fluorescent In Situ Techniques 230
11.6.2.1 Types of Probes 230
11.6.2.2 Steps Involved in FISH 231
11.6.2.3 Applications of FISH 232
11.6.2.4 FISH on FFPE Samples 232
Proteomic Analysis of Blood and Pancreatic Juice 236
12.1 Introduction 236
12.2 Quantitative LC-MS/MS Approaches 239
12.2.1 Stable Isotope Labelling—In Vitro 239
12.2.2 Stable Isotope Labelling—In Vivo 241
12.2.3 Phosphoproteomics: Detection and Quantification of Phosphoproteins 242
12.2.4 Multiple Reaction Monitoring 243
12.3 Overview of Proteomic Analysis of Juice and Blood in Pancreatic Cancer Samples to Date 244
12.4 Novel Treatments for Pancreatic Cancer: Analysis of Plasma/Serum Biomarkers 245
12.4.1 The EGFR Pathway in Pancreatic Cancer and its Therapeutic Intervention: Associated Biomarkers 246
12.4.2 The VEGF Pathway and Its Therapeutic Intervention: Associated Biomarkers 248
12.4.1 The EGFR Pathway in Pancreatic Cancer and Its Therapeutic Intervention: Associated Biomarkers 246
12.5 Concluding Remarks 249
Applications of Antibody-Lectin Sandwich Arrays (ALSA) to Pancreatic Cancer Diagnostics and Drug Discovery 255
13.1 Introduction 255
13.2 Protein Glycosylation in Normal Biological Functions 256
13.2.1 Structural Features of Protein Glycosylation 257
13.2.2 N-Glycans 257
13.2.2.1 Biosynthesis and Structures 257
13.2.2.2 Major Functions of N-glycans 259
13.2.2.2 Major Functions of N-Glycans 259
13.2.3 O-Glycans 259
13.2.3.1 Biosynthesis and Structures of Mucin-type Glycosylation 259
13.2.3.2 Major Functions of O-glycans 260
13.2.3.2 Major Functions of O-Glycans 260
13.3 Protein Glycosylation in Cancer 261
13.3.1 Roles of Glycans in Cancer Progression 261
13.3.1.1 Cell Adhesion and Metastasis 261
13.3.1.2 Immunomodulation 262
13.3.2 Common Glycan Alterations in Cancer 263
13.3.2.1 Core-1 O-Glycan Structures on Mucins 263
13.3.2.2 .1-6GlcNAc Branching of N-Glycans 264
13.3.2.3 Blood Group Structures 265
13.3.2.4 Other Motifs: Fucose, Sialic Acid, and Mannose 266
13.4 Antibody-Lectin Sandwich Arrays (ALSA) 267
13.4.1 Reproducible and Sensitive Detection Using Affinity Reagents 267
13.4.2 Multiplexing Through the Use of Microarrays 269
13.4.3 Convenient Detection of Both Core Protein and Glycan Levels 270
13.4.4 Low-Volume, High-Throughput Sample Processing 270
13.5 ALSA in Pancreatic Cancer Research 271
13.5.1 Types of Experiments Using ALSA 271
13.5.1.1 Characterizing Glycosylation Variation in Populations 271
13.5.1.2 Identifying Glycan Changes on Specific Proteins in Model Systems 271
13.5.1.3 Characterizing Protein Carriers of Specific Glycans 272
13.5.2 Example Applications in Pancreatic Cancer Research 272
13.5.2.1 Serum Biomarkers for Pancreatic Cancer 272
13.5.2.2 Cyst Fluid Biomarkers 273
13.5.2.3 Glycosylation in Cancer Cell Subpopulations 273
13.5.2.4 Defining Targets for Immunotherapy 274
The Development of PharmacodynamicEndpoint Models for Evaluationof Therapeutics in Pancreatic Cancer 282
14.1 Introduction 282
14.2 Assay Validation 283
14.3 In Vitro Models 283
14.4 Xenograft Models 284
14.5 Hetero-Transplanted Human Xenografts 284
14.6 Genetically Engineered Mouse Models 285
14.7 Clinical Assessment of PD 285
14.8 Imaging Biomarkers 286
14.9 PD Models for Gemcitabine 286
14.9.1 Equilibrative Nucleoside Transporter 1 287
14.9.2 Cytidine Deaminase Activityand Deoxycytidylate Deaminase 287
14.9.3 Ribonucleotide Reductase 288
14.9.4 Survivin: An Anti-Apoptotic GemcitabineResistance Marker 288
14.9.5 CA 19-9 as a Prognostic Factor for Response to Gemcitabine 289
14.9.5 CA 19-9 as a Prognostic Factor for Responseto Gemcitabine 289
14.10 PD Models for Erlotinib 290
14.11 PD models for Investigational Angiogenesis-Targeting Agents in Pancreatic Cancer 290
14.11.1 Introduction to Angiogenesis in Cancer 290
14.11.2 Angiogenesis as a Therapeutic Target 291
14.11.3 Biomarkers of Angiogenesis in Cancer 291
14.11.3.1 Soluble Angiogenic Proteins 291
14.11.3.2 Cellular PD Markers: Circulating Endothelial Cells 292
14.12 PD Models Utilized in Assessing Mechanismand Activity of COX-2 Expression and Inhibitionin Pancreatic Cancer 292
14.12.1 COX-2 is Highly Expressed in Pancreatic Cancers 292
14.12.2 Selective COX-2 Inhibitors 293
14.12.3 PD Models for COX-2 Inhibition in Pancreatic Cancer 293
14.13 Targeting Stroma: Secreted Protein Acid and Richin Cysteine 294
14.14 Other Potential PD Endpoints: Circulating Tumor Cells 294
14.15 Concluding Remarks 295
14.13 Targeting Stroma: Secreted Protein Acidand Rich in Cysteine 294
Index 301
| Erscheint lt. Verlag | 11.3.2010 |
|---|---|
| Zusatzinfo | XIX, 297 p. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Medizin / Pharmazie ► Medizinische Fachgebiete ► Onkologie |
| Medizin / Pharmazie ► Medizinische Fachgebiete ► Pharmakologie / Pharmakotherapie | |
| Medizin / Pharmazie ► Studium | |
| Schlagworte | Cancer • Diagnostics • drug discovery • Han • Imaging • pancreatic • siRNA |
| ISBN-10 | 1-4419-1160-X / 144191160X |
| ISBN-13 | 978-1-4419-1160-5 / 9781441911605 |
| Haben Sie eine Frage zum Produkt? |
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