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Integration of Omics Approaches and Systems Biology for Clinical Applications

Integration of Omics Approaches and Systems Biology for Clinical Applications

Antonia Vlahou (Editor), Fulvio Magni (Editor), Harald Mischak (Editor), Jerome Zoidakis (Editor)

ISBN: 978-1-119-18395-2

Jan 2018

384 pages

Description

Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications

This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed.

Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting.

  • Describes a range of state of the art omics analytical platforms
  • Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis
  • Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer)

Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

List of Contributors xv

Preface xix

Acknowledgement xx

Part I Platforms for Molecular Data Acquisition and Analysis 1

1 Clinical Data Collection and Patient Phenotyping 3
Katerina Markoska and Goce Spasovski

1.1 Clinical Data Collection 3

1.1.1 Data Collection for Clinical Research 3

1.1.2 Clinical Data Management 3

1.1.3 Creating Data Forms 4

1.1.3.1 Different Data Forms According to the Type of Study 4

1.1.4 Case Report Form (CRF) 5

1.1.4.1 CRF Standards Characterization 5

1.1.4.2 Electronic and Paper CRFs 6

1.1.5 Methods and Forms for Clinical Data Collection and/or Extraction from Patient’s Records 6

1.1.5.1 Electronic Health Records (EHRs) 6

1.1.6 Data Collection Workflow 7

1.1.6.1 Defining Baseline and Follow]Up Data 7

1.1.6.2 Medical Coding 7

1.1.6.3 Errors in Data Collection and Missing Data 8

1.1.6.4 Data Linkage, Storage, and Validation 8

1.2 Patient Phenotyping 8

1.2.1 Approaches in Defining Patient Phenotype 9

1.2.2 Phenotyping CKD Patients 9

1.3 Concluding Remarks 10

References 10

2 Biobanking, Ethics, and Relevant Legal Issues 13
Brigitte Lohff, Thomas Illig, and Dieter Tröger

2.1 Introduction 13

2.2 Brief Historical Derivation to the Ethical Guidelines in Medical Research 13

2.2.1 1900: Directive to the Head of the Hospitals, Polyclinics, and Other Hospitals 14

2.2.2 1931: Guidelines for Novel Medical Treatments and Scientific Experimentation 14

2.2.3 1947: The Nuremberg Code 14

2.2.4 1964: The Declaration of Helsinki 14

2.2.5 The Declaration of Helsinki and Research on Human Materials and Data 15

2.2.6 2013: Current Valid Declaration of Helsinki in the 7th Revision 15

References 15

2.3 Biobanking: Definition, Role, and Guidelines of National and International Biobanks 16

2.3.1 Introduction 16

2.3.2 Definition of Biobanks 17

2.3.3 Human Biobank Types 17

2.3.4 Clinical Biobanks 17

2.3.5 Governance in HUB 18

2.3.6 Epidemiological Biobanks 18

2.3.7 Quality of Samples 19

2.3.8 Harmonization and Cooperation of Biobanks 19

2.3.9 Situation in Germany 20

2.3.10 Situation in Europe and Worldwide 20

2.3.11 Definition of Ownership, Access Rights, and Governance of Biobanks 20

2.3.12 IT in Biobanks 21

2.3.13 Financial Aspects and Sustainability 21

2.3.14 Conclusion 21

References 22

2.4 Tasks of Ethics Committees in Research with Biobank Materials 23

2.4.1 General Basic Concept 23

2.4.1.1 The Application Procedure 23

2.4.2 About the Respective Ethics Commissions 23

2.4.3 The Establishment of Biobanks 24

Further Reading 24

3 Nephrogenetics and Nephrodiagnostics: Contemporary Molecular Approaches in the Genomics Era 26
Constantinos Deltas

3.1 Introduction 26

3.2 Applications of Molecular Diagnostics 27

3.3 Aims of Present]Day Molecular Genetic Investigations 28

3.4 Material Used for Genetic Testing 28

3.5 Clinical, Genetic, and Allelic Heterogeneity 29

3.6 Oligogenic Inheritance 31

3.7 ADPKD, Phenotypic Heterogeneity, and Genetic Modifiers 32

3.8 Collagen IV Nephropathies, Genetic and Phenotypic Heterogeneity, and Genetic Modifiers 33

3.9 CFHR5 Nephropathy, Phenotypic Heterogeneity, and Genetic Modifiers 36

3.10 Unilocus Mutational and Phenotypic Diversity (UMPD) 38

3.11 Next]Generation Sequencing (NGS) 39

3.12 Conclusions 40

Acknowledgments 41

References 41

4 The Use of Transcriptomics in Clinical Applications 49
Daniel M. Borràs and Bart Janssen

4.1 Introduction 49

4.2 Clinical Applications of Transcriptomics: Cases and Potential Examples 53

4.2.1 PCR Applications 53

4.2.2 Microarrays 55

4.2.3 Sequencing 57

4.2.4 Discussion 60

References 63

Further Reading 66

5 miRNA Analysis 67
Theofilos Papadopoulos, Julie Klein, Jean]Loup Bascands, and Joost P. Schanstra

5.1 miRNA Biogenesis, Function, and Annotation 67

5.2 Annotation of miRNAs 69

5.3 miRNAs: Location, Stability, and Research Methods 69

5.3.1 miRNA Analysis and Tissue Distribution 69

5.3.2 miRNAs in Body Fluids 69

5.3.3 Stability of miRNAs 71

5.3.4 Methods to Study miRNAs 71

5.3.4.1 Sampling 71

5.3.4.2 Extraction Protocols 71

5.3.4.3 miRNA Detection Techniques 72

5.3.4.4 Data Processing and Molecular Integration 73

5.3.4.5 In Vitro Target Validation 77

5.4 Use of miRNA In Vivo 79

5.4.1 Chemically Modified miRNAs 82

5.4.2 miRNA Sponges or Decoys 82

5.4.3 Modified Viruses 82

5.4.4 Microvesicles 82

5.4.5 The Polymers 83

5.4.6 Inorganic Nanoparticles 83

5.5 miRNAs as Potential Therapeutic Agents and Biomarkers: Lessons Learned So Far 83

5.5.1 miRNAs as Potential Therapeutic Agents 83

5.5.2 miRNAs as Potential Biomarkers 84

5.5.2.1 Cancer 84

5.5.2.2 Metabolic and Cardiovascular Diseases 84

5.5.2.3 Miscellaneous Diseases 84

5.6 Conclusion 84

References 85

6 Proteomics of Body Fluids 93
Szymon Filip and Jerome Zoidakis

6.1 Introduction 93

6.2 General Workflow for Obtaining High]Quality Proteomics Results 93

6.3 Body Fluids 95

6.3.1 Blood 95

6.3.1.1 Plasma 95

6.3.1.2 Serum 96

6.3.2 Urine 96

6.3.3 Cerebrospinal Fluid (CSF) 96

6.3.4 Saliva 96

6.4 Sample Collection and Storage 97

6.5 Sample Preparation for MS/MS Analysis 97

6.5.1 Protein Separation 97

6.5.1.1 Electrophoresis]Based Methods 98

6.5.1.2 Liquid Chromatography Methods 98

6.5.2 Sample Preparation for MS/MS (Tryptic Digestion) 102

6.5.3 Separation of Peptides 102

6.6 Analytical Instruments 103

6.7 Data Processing and Bioinformatics Analysis 103

6.7.1 Peptide and Protein Identification 103

6.7.2 Protein Quantitation 103

6.7.3 Data Normalization (Example of Label]Free Proteomics Using Ion Intensities) 104

6.7.4 Statistics in Proteomics Analysis 105

6.8 Validation of Findings 105

6.9 Clinical Applications of Body Fluid Proteomics 106

6.10 Conclusions 109

References 109

7 Peptidomics of Body Fluids 113
Prathibha Reddy, Claudia Pontillo, Joachim Jankowski, and Harald Mischak

7.1 Introduction 113

7.2 Clinical Application of Peptidomics 113

7.3 Different Types of Body Fluids Used in Biomarker Research 113

7.3.1 Blood 113

7.3.2 Urine 114

7.4 Sample Preparation and Separation Methods for Mass Spectrometric Analysis 115

7.4.1 Depletion Strategies 115

7.4.1.1 Ultrafiltration 115

7.4.1.2 Precipitation 116

7.4.1.3 Liquid Chromatography 116

7.4.1.4 Capillary Electrophoresis 116

7.4.1.5 Instrumentation 117

7.5 Identification of Peptides and Their Posttranslational Modifications 117

7.6 Urinary Peptidomics for Clinical Application 118

7.6.1 Kidney Disease 118

7.6.2 Urogenital Cancers 119

7.6.3 Blood Peptides as Source of Biomarkers 120

7.6.4 Proteases and Their Role in Renal Diseases and Cancer 120

7.7 Concluding Remarks 122

References 122

8 Tissue Proteomics 129
Agnieszka Latosinska, Antonia Vlahou, and Manousos Makridakis

8.1 Introduction 129

8.2 Tissue Proteomics Workflow 130

8.3 Tissue Sample Collection and Storage 132

8.4 Sample Preparation 133

8.4.1 Homogenization of Fresh]Frozen Tissue 133

8.4.1.1 Mechanical Methods of Tissue Homogenization 135

8.4.1.2 Chemical Methods of Tissue Homogenization 136

8.4.2 LCM 136

8.4.3 Protein Digestion 137

8.5 Overcoming Tissue Complexity and Protein Dynamic Range: Separation Techniques 138

8.5.1 Subcellular Fractionation 139

8.5.2 Gel]Based Approaches 139

8.5.3 Gel]Free Approaches 140

8.6 Instrumentation 141

8.6.1 LTQ Orbitrap 141

8.6.2 LTQ Orbitrap Velos 142

8.6.3 Q Exactive 142

8.7 Quantitative Proteomics 143

8.8 Functional Annotation of Proteomics Data 144

8.9 Application of MS]Based Tissue Proteomics in Bladder Cancer Research 145

8.10 Conclusions 148

References 148

9 Tissue MALDI Imaging 156
Andrew Smith, Niccolò Mosele, Vincenzo L’Imperio, Fabio Pagni, and Fulvio Magni

9.1 Introduction 156

9.1.1 MALDI]MSI: General Principles 157

9.2 Experimental Procedures 159

9.2.1 Sample Handling: Storage, Embedding, and Sectioning 159

9.2.2 Matrix Application 160

9.2.3 Spectral Processing 162

9.2.3.1 Baseline Removal 162

9.2.3.2 Smoothing 164

9.2.3.3 Spectral Normalization 164

9.2.3.4 Spectral Realignment 166

9.2.3.5 Generating an Overview Spectrum 166

9.2.3.6 Peak Picking 166

9.2.4 Data Elaboration 168

9.2.4.1 Unsupervised Data Mining 168

9.2.4.2 Supervised Data Mining 168

9.2.5 Correlating MALDI]MS Images with Pathology 169

9.3 Applications in Clinical Research 169

References 171

10 Metabolomics of Body Fluids 173
Ryan B. Gill and Silke Heinzmann

10.1 Introduction to Metabolomics 173

10.2 Analytical Techniques 174

10.2.1 NMR 174

10.2.1.1 Sample Preparation for Urine 175

10.2.1.2 Sample Preparation for Blood 177

10.2.1.3 Sample Preparation for Tissue 177

10.2.1.4 Instrumental Setup 177

10.2.2 MS 178

10.2.2.1 Ionization 178

10.2.2.2 Mass Analyzers 179

10.2.2.3 Coupled Separation Methods 179

10.2.2.4 MS Sample Pretreatment Techniques 180

10.2.3 Protein Removal (PPT) 181

10.2.4 LLE 182

10.2.5 Solid]Phase Extraction (SPE) 182

10.3 Statistical Tools and Systems Integration 182

10.3.1 Post]Measurement Spectral Processing 183

10.3.2 Spectral Alignment 183

10.3.3 Normalization and Scaling 184

10.3.4 Peak Versus Feature Detection 184

10.3.5 Data Analysis 184

10.3.6 Unsupervised 184

10.3.7 Supervised 185

10.3.8 Spectral Databases and Metabolite Identification 185

10.3.9 Pathway Analysis 186

10.3.10 Validation and Performance Assessment 186

10.3.11 Application into Systems Biology 187

10.4 Metabolomics in CKD 187

10.4.1 Uremic Toxins and New Biomarkers of eGFR and CKD Stage 187

10.4.2 Dimethylarginine 188

10.4.3 p]Cresol Sulfate (PCS) 188

10.4.4 Indoxyl Sulfate (IS) 188

10.4.5 Gut Microbiota 189

10.4.6 Osmolytes 190

10.5 Conclusions 190

References 191

11 Statistical Inference in High]Dimensional Omics Data 196
Eleni]Ioanna Delatola and Mohammed Dakna

11.1 Introduction 196

11.2 From Raw Data to Expression Matrices 196

11.3 Brief Introduction R and Bioconductor 197

11.4 Feature Selection 197

11.5 Sample Classification 199

11.6 Real Data Example 200

11.7 Multi]Platform Data Integration 200

11.7.1 Early]Stage Integration 201

11.7.2 Late]Stage Integration 201

11.7.3 Intermediate]Stage Integration 202

11.7.4 Intermediate]Stage Integration: Matrix Factorization 202

11.7.5 Intermediate]Stage Integration: Unsupervised Methods 202

11.8 Discussion and Further Challenges 202

References 203

12 Epidemiological Applications in ]Omics Approaches 207
Elena Critselis and Hiddo Lambers Heerspink

12.1 Overview: Importance of Study Design and Methodology 207

12.2 Principles of Hypothesis Testing 207

12.2.1 Definition of Research Hypotheses and Clinical Questions 207

12.2.2 Hypothesis Testing in Relation to Types of Biomarkers Under Assessment 208

12.3 Selection of Appropriate Epidemiological Study Design for Hypothesis Testing 208

12.4 Types of Epidemiological Study Designs 209

12.4.1 Observational Studies 209

12.4.1.1 Cross]Sectional Studies 209

12.4.1.2 Case]Control Studies 210

12.4.1.3 Cohort Studies 211

12.4.1.4 Health Economics Assessment 211

12.5 Selection of Appropriate Statistical Analyses for Hypothesis Testing 211

12.6 Summary 212

References 213

Part II Progressing Towards Systems Medicine 215

13 Introduction into the Concept of Systems Medicine 217
Stella Logotheti and Walter Kolch

13.1 Medicine of the Twenty]First Century: From Empirical Medicine and Personalized Medicine to Systems Medicine 217

13.2 The Emerging Concept of Systems Medicine 218

13.2.1 The Need for Establishment of Systems Medicine and the Field of Application 218

13.2.2 Bridging the Gap: From Systems Biology to Systems Medicine 219

13.2.3 Attempting a Definition 220

13.2.4 The Network]Within]a]Network Approach in Systems Medicine 220

13.2.4.1 Great Expectations for Systems Medicine: The P4 Vision 221

13.2.4.2 How Systems Medicine Will Transform Healthcare 222

13.2.4.3 The Five Pillars of Systems Medicine 223

13.2.4.4 The Stakeholders of Systems Medicine 223

13.2.4.5 The Key Areas for Successful Implementation 223

13.2.4.6 Improvement of the Design of Clinical Trials 223

13.2.4.7 Development of Methodology and Technology, with Emphasis on Modeling 224

13.2.4.8 Generation of Data 224

13.2.4.9 Investment on Technological Infrastructure 224

13.2.4.10 Improvement of Patient Stratification 224

13.2.4.11 Cooperation with the Industry 224

13.2.4.12 Defining Ethical and Regulatory Frameworks 224

13.2.4.13 Multidisciplinary Training 225

13.3 Networking Among All Key Stakeholders 225

13.4 Coordinated European Efforts for Dissemination and Implementation 225

13.5 The Contributions of Academia in Systems Medicine 226

13.6 Data Generation: Omics Technologies 226

13.7 Data Integration: Identifying Disease Modules and Multilayer Disease Modules 227

13.8 Modeling: Computational and Animal Disease Models for Understanding the Systemic Context of a Disease 228

13.9 Examples and Success Stories of Systems Medicine]Based Approaches 228

13.10 Limitations, Considerations, and Future Challenges 229

References 230

14 Knowledge Discovery and Data Mining 233
Magdalena Krochmal and Holger Husi

14.1 Introduction 233

14.2 Knowledge Discovery Process 233

14.2.1 Defining the Concept and Goals 234

14.2.2 Data Preparation/Preprocessing 235

14.2.3 Database Systems 236

14.2.4 Data Mining Tasks and Methods 236

14.2.4.1 Statistics 238

14.2.4.2 Machine Learning 239

14.2.4.3 Text Mining 241

14.2.5 Pattern Evaluation 242

14.3 Data Mining in Scientific Applications 242

14.3.1 Genomics Data Mining 243

14.3.2 Proteomics Data Mining 243

14.4 Bioinformatics Data Mining Tools 244

14.5 Conclusions 244

References 245

15 -Omics and Clinical Data Integration 248
Gaia De Sanctis, Riccardo Colombo, Chiara Damiani, Elena Sacco, and Marco Vanoni

15.1 Introduction 248

15.2 Data Sources 249

15.3 Integration of Different Data Sources 252

15.4 Integration of Different ]Omics Data 252

15.4.1 Integrating Transcriptomics and Proteomics 252

15.4.2 Integrating Transcriptomics and Interactomics 253

15.4.3 Integrating Transcriptomics and Metabolic Pathways 254

15.5 Visualization of Integrated ]Omics Data 255

15.6 Integration of ]Omics Data into Models 260

15.6.1 Multi]Omics Data Integration into Genome]Scale Constraint]Based Models 262

15.7 Data Integration and Human Health 263

15.7.1 Applications to Metabolic Diseases 263

15.7.2 Applications to Cancer Research 264

15.8 Conclusions 265

References 265

16 Generation of Molecular Models and Pathways 274
Amel Bekkar, Julien Dorier, Isaac Crespo, Anne Niknejad, Alan Bridge, and Ioannis Xenarios

16.1 Introduction 274

16.2 PKN Construction Through Expert Biocuration 274

16.3 Modeling and Simulating the Dynamical Behavior of Networks 276

16.3.1 Logic Models 276

16.3.1.1 Boolean Networks 276

16.3.1.2 Probabilistic Boolean Networks (PBN) 278

16.3.1.3 Multiple Value Modeling 278

16.3.1.4 Fuzzy Logic]Based Modeling 278

16.3.1.5 Contextualization of PKNs Using Experimental Data 279

16.3.1.6 Ordinary Differential Equations 280

16.3.1.7 Piecewise Linear Differential Equations 280

16.3.1.8 Constraint]Based Modeling 281

16.3.1.9 Hybrid Models 282

16.4 Conclusions 283

References 283

17 Database Creation and Utility 286
Magdalena Krochmal, Katryna Cisek, and Holger Husi

17.1 Introduction 286

17.2 Database Systems 286

17.2.1 Introduction to Databases 286

17.2.2 Data Life Cycle and Objectives of Database Systems 286

17.2.3 Advantages and Limitations 288

17.2.4 Database Design Models 288

17.2.5 Development Life Cycle 291

17.2.6 Database Transactions, Structured Query Language (SQL) 292

17.2.7 Data Analysis and Visualization 292

17.3 Biological Databases 293

17.3.1 Development Life Cycle 294

17.3.1.1 Data Extraction 294

17.3.1.2 Semantic Tools for ]Omics 294

17.3.2 Existing Biological Repositories 295

17.3.2.1 Information Sources for ]Omics 295

17.3.2.2 Renal Information Sources for ]Omics 296

17.3.3 Application in Research 297

17.3.3.1 Data Mining on Large Multi]Omics Datasets 297

17.3.3.2 Multi]Omics Tools for Researchers 297

17.3.3.3 Limitations of Multi]Omics Tools 297

17.3.3.4 Future Outlook for Multi]Omics 298

17.4 Conclusions 298

References 298

Part III Test Cases CKD and Bladder Carcinoma 301

18 Kidney Function, CKD Causes, and Histological Classification 303
Franco Ferrario, Fabio Pagni, Maddalena Bolognesi, Elena Ajello, Vincenzo L’Imperio, Cristina Masella, and Giovambattista Capasso

18.1 Introduction 303

18.2 The Evaluation of Glomerular Filtration Rate 303

18.3 Causes of CKD 305

18.3.1 Histological Classification of CKD 307

18.4 Assessment of Disease Progression and Response to Therapy for the Individual: Interval Renal Biopsy 310

18.5 Recent Advances: Pathology at the Molecular Level 310

18.6 Digital Pathology 313

18.7 Conclusions 315

References 315

19 CKD: Diagnostic and Other Clinical Needs 319
Alberto Ortiz

19.1 The Evolving Concept of Chronic Kidney Disease 319

19.2 A Growing Epidemic 320

19.3 Increasing Mortality from Chronic Kidney Disease 321

19.4 The Issue of Cause and Etiologic Therapy 322

19.5 Unmet Medical Needs: Biomarkers and Therapy 323

19.6 Conclusions 324

Acknowledgments 324

References 324

20 Molecular Model for CKD 327
Marco Fernandes, Katryna Cisek, and Holger Husi

20.1 Introduction 327

20.2 Data]Driven Approaches and Multiomics Data Integration 327

20.2.1 Database Resources 328

20.2.2 Software Tools and Solutions 330

20.2.2.1 Gene Ontology (GO) and Pathway]Term Enrichment 331

20.2.2.2 Disease–Gene Associations 331

20.2.2.3 Resolving Molecular Interactions (Protein–Protein Interaction, Metabolite–Reaction–Protein–Gene) 332

20.2.2.4 Transcription Factor(TF)]Driven Modules and microRNA–Target Regulation 332

20.2.2.5 Pathway Visualization and Mapping 333

20.2.2.6 Data Harmonization: Merging and Mapping 333

20.2.3 Computational Drug Discovery 334

20.2.3.1 High]Throughput Virtual Screening (HTVS) 334

20.2.3.2 Advantages and Limitations of HTVS 334

20.3 Chronic Kidney Disease (CKD) Case Study 335

20.3.1 Dataspace Description: Demographics and Omics Platforms Information 337

20.3.2 Dataspace Description: No. of Associated Molecules Per Omics Platform 337

20.3.3 Data Reduction by Principal Component Analysis (PCA) 338

20.3.4 Gene Ontology (GO) and Pathway]Term Clustering 339

20.3.5 Interactome Analysis: PPIs and Regulatory Interactions 342

20.3.5.1 Protein–Protein Interactions (PPIs) 342

20.3.5.2 Regulatory Interactions 343

20.3.6 Interactome Analysis: Metabolic Reactions 343

20.4 Final Remarks 343

Acknowledgments 343

Conflict of Interest Statement 343

References 345

21 Application of Omics and Systems Medicine in Bladder Cancer 347
Maria Frantzi, Agnieszka Latosinska, Murat Akand, and Axel S. Merseburger

21.1 Introduction 347

21.2 Bladder Cancer Pathology and Clinical Needs 348

21.2.1 Epidemiological Facts and Histological Classification 348

21.2.2 Current Diagnostic Means 348

21.2.3 Treatment Options 349

21.2.4 Recurrence and Progression 349

21.2.5 Molecular Classification 350

21.2.6 Biomarkers for Bladder Cancer 350

21.2.7 Considerations on Patient Management 351

21.2.8 Defining the Disease]Associated Clinical Needs 351

21.3 Systems Medicine in Bladder Cancer 351

21.3.1 Omics Datasets for Biomarker Research 353

21.3.1.1 Diagnostic Biomarkers for Disease Detection/Monitoring 353

21.3.1.2 Prognostic Signatures 354

21.3.1.3 Predictive Molecular Profiles 355

21.3.1.4 Molecular Sub]Classification 356

21.4 Outlook 357

Acknowledgments 357

References 358

Index 361