Nucleic Acids as Molecular Diagnostics

By integrating technology, supporting infrastructure and efficient application, this all–in–one guide presents molecular diagnostics as an essential component of modern, personalized clinical practice. It considers all important aspects, from the hardware and software needed, to recent improvements in blood– and non–bloodbased biomarker tests. Chapters on ethical challenges and a look at current trends and the latest innovations are also included.

Bridging the gap between industry and academia, this is a highly useful resource for practitioners as well as for developers of modern, DNA– and RNA–based molecular diagnostics.

List of Contributors XIII

Preface XIX

1 Next–Generation Sequencing for Clinical Diagnostics ofCardiomyopathies 1
Jan Haas, Hugo A. Katus, and Benjamin Meder

1.1 Introduction 1

1.2 Cardiomyopathies and Why Genetic Testing is Needed 1

1.3 NGS 2

1.4 NGS for Cardiomyopathies 2

1.5 Sample Preparation 3

1.6 Bioinformatics Analysis Pipeline 4

1.7 Interpretation of Results and Translation into ClinicalPractice 4

References 6

2 MicroRNAs as Novel Biomarkers in Cardiovascular Medicine11
Britta Vogel, Hugo A. Katus, and Benjamin Meder

2.1 Introduction 11

2.2 miRNAs are Associated with Cardiovascular Risk Factors12

2.3 miRNAs in Coronary Artery Disease 13

2.4 miRNAs in Cardiac Ischemia and Necrosis 15

2.5 miRNAs as Biomarkers of Heart Failure 19

2.6 Future Challenges 20

Acknowledgments 20

References 21

3 MicroRNAs in Primary Brain Tumors: Functional Impact andPotential Use for Diagnostic Purposes 25
Patrick Roth and Michael Weller

3.1 Background 25

3.2 Gliomas 26

3.2.1 miRNA as Biomarkers in Glioma Tissue 28

3.2.2 Circulating miRNA as Biomarkers 29

3.3 Meningiomas 30

3.4 Pituitary Adenomas 31

3.5 Medulloblastomas 31

3.6 Other Brain Tumors 32

3.6.1 Schwannomas 32

3.6.2 PCNSLs 33

3.7 Summary and Outlook 33

References 34

4 Genetic and Epigenetic Alterations in Sporadic ColorectalCancer: Clinical Implications 39
Pawel Karpinski, Nikolaus Blin, and Maria M. Sasiadek

4.1 Introduction 39

4.2 Chromosomal Instability 40

4.3 Microsatellite Instability 43

4.4 Driver Somatic Mutations in CRC 46

4.4.1 APC 46

4.4.2 TP53 47

4.4.3 KRAS 47

4.4.4 BRAF 47

4.4.5 PIK3CA 48

4.4.6 Other Mutations 48

4.5 Epigenetic Instability in CRC 48

4.6 Hypomethylation 49

4.7 CpG Island Methylator Phenotype 50

4.8 Concluding Remarks 51

References 51

5 Nucleic Acid–Based Markers in Urologic Malignancies63
Bernd Wullich, Peter J. Goebell, Helge Taubert, and SvenWach

5.1 Introduction 63

5.2 Bladder Cancer 64

5.2.1 Hereditary Factors for Bladder Cancer 65

5.2.2 Single Nucleotide Polymorphisms 65

5.2.3 RNA Alterations in Bladder Cancer 66

5.2.3.1 FGFR3 Pathway 66

5.2.3.2 p53 Pathway 67

5.2.3.3 Urine–Based Markers 67

5.2.3.4 Serum–Based Markers 68

5.2.4 Sporadic Factors for Bladder Cancer 69

5.2.5 Genetic Changes in Non–Invasive Papillary UrothelialCarcinoma 69

5.2.5.1 FGFR 3 69

5.2.5.2 Changes in the Phosphatidylinositol 3–Kinase Pathway70

5.2.6 Genetic Changes in Muscle–Invasive Urothelial Carcinoma72

5.2.6.1 TP53, RB, and Cell Cycle Control Genes 73

5.2.6.2 Other Genomic Alterations 74

5.2.7 Genetic Alterations with Unrecognized Associations toTumor Stage and Grade 75

5.2.7.1 Alterations of Chromosome 9 75

5.2.7.2 RAS Gene Mutations 76

5.3 Prostate Cancer 77

5.3.1 Hereditary Factors for Prostate Cancer 77

5.3.2 Sporadic Factors for Prostate Cancer 80

5.3.2.1 PSA and Other Protein Markers 80

5.3.2.2 Nucleic Acid Biomarkers 81

5.3.3 Prostate Cancer: Summary 87

5.4 Renal Cell Carcinoma 87

5.4.1 Hereditary Factors for RCC 87

5.4.2 Sporadic Factors for RCC 90

5.4.2.1 The Old 90

5.4.2.2 The New 91

5.5 Summary 92

References 96

6 From the Genetic Make–Up to the Molecular Signature ofNon–Coding RNA in Breast Cancer 129
Michael G. Schrauder and Reiner Strick

6.1 Introduction 129

6.2 Molecular Breast Cancer Detection 130

6.2.1 Circulating Free DNA 130

6.2.2 Long Intergenic Non–Coding RNA 132

6.2.2.1 HOTAIR 132

6.2.2.2 H19 133

6.2.2.3 GAS5 134

6.2.2.4 LSINCT5 134

6.2.2.5 LOC554202 134

6.2.2.6 SRA1 134

6.2.2.7 XIST 134

6.2.3 Natural Antisense Transcripts 135

6.2.3.1 HIF–1a–AS 136

6.2.3.2 H19 and H19–AS (91H) 137

6.2.3.3 SLC22A18–AS 137

6.2.3.4 RPS6KA2–AS 137

6.2.3.5 ZFAS1 137

6.2.4 miRNAs 138

6.2.4.1 Tissue–Based miRNA Profiling in Breast Cancer 138

6.2.4.2 Circulating miRNAs 141

6.3 Molecular Breast Cancer Subtypes and Prognostic/PredictiveMolecular Biomarkers 142

References 144

7 Nucleic Acid–Based Diagnostics in GynecologicalMalignancies 155
Sebastian F.M. Häusler, Johannes Dietl, and JörgWischhusen

7.1 Introduction 155

7.2 Cervix, Vulva, and Vaginal Carcinoma 155

7.2.1 Background 155

7.2.2 Routine Diagnostics for HPV Infection 157

7.2.2.1 Digene Hybrid Capture 2 High–Risk HPV DNA Test (Qiagen)158

7.2.2.2 Cervista HPV HR (Holologics) 158

7.2.2.3 cobas 4800 System (Roche) 159

7.2.2.4 APTIMA HPV (Gen–Probe) 159

7.2.2.5 Abbot RealTime High Risk HPV Assay (Abbot) 159

7.2.2.6 PapilloCheck Genotyping Assay (Greiner BioOne) 160

7.2.2.7 INNO–LiPA HPVG enotyping Extra (Innogenetics) 160

7.2.2.8 Linear Array (Roche) 160

7.2.2.9 Recommendations for Clinical Use 160

7.2.3 Outlook DNA Methylation Patterns 161

7.3 Endometrial Carcinoma (Carcinoma Corpus Uteri) 162

7.3.1 Background 162

7.3.2 Routine Diagnostics Microsatellite Instability162

7.3.3 Emerging Diagnostics miRNA Markers 163

7.4 Ovarian Carcinoma 164

7.4.1 Background 164

7.4.2 Routine Diagnostics 165

7.4.3 Emerging Diagnostics/Perspective miRNA Profiling166

7.5 Breast Cancer 167

7.5.1 Background 167

7.5.2 Routine Diagnostics 168

7.5.2.1 HER2 Diagnostics 168

7.5.2.2 Gene Expression Profiling 169

7.5.2.3 Hereditary Breast Cancer/BRCA Diagnostics 170

7.5.3 Emerging Diagnostics/Perspectives 173

7.6 Conclusion 175

References 175

8 Nucleic Acids as Molecular Diagnostics in HematopoieticMalignancies Implications in Diagnosis, Prognosis, andTherapeutic Management 185
Janine Schwamb and Christian P. Pallasch

8.1 Introduction 185

8.2 Methodological Approaches 186

8.3 Cytogenetic Analysis to Molecular Diagnostics 186

8.4 Minimal Residual Disease 186

8.5 Chronic Myeloid Leukemia 187

8.6 Acute Myeloid Leukemia 189

8.7 Acute Lymphocytic Leukemia 191

8.8 Chronic Lymphocytic Leukemia 192

8.9 Outlook and Perspectives 196

References 196

9 Techniques of Nucleic Acid–Based Diagnosis in theManagement of Bacterial and Viral Infectious Diseases 201
Irene Latorre, Verónica Saludes, Juana Díez, andAndreas Meyerhans

9.1 Importance of Nucleic Acid–Based Molecular Assays inClinical Microbiology 201

9.2 Nucleic Acid Amplification Techniques 202

9.2.1 Target Amplification Techniques 203

9.2.1.1 PCR–Based Techniques 203

9.2.1.2 Transcription–Based Amplification Methods 204

9.2.2 Signal Amplification Techniques 204

9.3 Post–Amplification Analyses 205

9.3.1 Sequencing and Pyrosequencing 205

9.3.2 Reverse Hybridization 206

9.3.3 High–Throughput Nucleic Acid–Based Analyses 206

9.3.3.1 DNA Microarrays 206

9.3.3.2 Mass Spectrometry 207

9.3.3.3 NGS 208

9.4 General Overview and Concluding Remarks 209

Acknowledgments 209

References 209

10 MicroRNAs in Human Microbial Infections and DiseaseOutcomes 217
Verónica Saludes, Irene Latorre, Andreas Meyerhans, andJuana Díez

10.1 Introduction 217

10.2 General Aspects of miRNAs in Infectious Diseases 218

10.2.1 miRNAs in Bacterial Infections 218

10.2.2 miRNAs in Viral Infections 219

10.2.2.1 Cellular miRNAs Control Viral Infections 220

10.2.2.2 Viruses Use miRNAs for Their Own Benefit 221

10.3 miRNAs as Biomarkers and Therapeutic Agents in Tuberculosisand Hepatitis C Infections 222

10.3.1 Tuberculosis: A Major Bacterial Pathogen 222

10.3.1.1 Tuberculosis Diagnosis and the Need for ImmunologicalBiomarkers 222

10.3.1.2 miRNAs Regulation in Response to M. tuberculosis223

10.3.1.3 Future Perspectives 225

10.3.2 Chronic Hepatitis C: A Major Viral Disease 225

10.3.2.1 Liver Fibrosis Progression and Treatment Outcome225

10.3.2.2 miRNAs Involved in Liver Fibrogenesis 226

10.3.2.3 Prediction of Treatment Outcome in Chronic HCV–1Infected Patients 228

10.3.2.4 Future Perspectives 229

10.4 miRNA–Targeting Therapeutics 230

10.5 Concluding Remarks 230

Acknowledgments 231

References 231

11 Towards the Identification of Condition–Specific MicrobialPopulations from Human Metagenomic Data 241
Cédric C. Laczny and Paul Wilmes

11.1 Introduction 241

11.2 Nucleic Acid–Based Methods in Diagnostic Microbiology242

11.2.1 Limitations of Culture–Dependent Approaches 242

11.2.2 Culture–Independent Characterization of MicrobialCommunities 243

11.2.3 Metagenomics 243

11.2.4 Fecal Samples as Proxies to Evaluate HumanMicrobiome–Related Health Status 244

11.3 Need for Comprehensive Microbiome Characterization inMedical Diagnostics 244

11.4 Challenges for Metagenomics–Based Diagnostics: ReadLengths, Sequencing Library Sizes, and Microbial CommunityComposition 248

11.5 Deconvolution of Population–Level Genomic Complements fromMetagenomic Data 250

11.5.1 Reference–Dependent Metagenomic Data Analysis 251

11.5.1.1 Alignment–Based Approaches 251

11.5.1.2 Sequence Composition–Based Approaches 253

11.5.2 Reference–Independent Metagenomic Data Analysis 254

11.6 Need for Comparative Metagenomic Data Analysis Tools256

11.6.1 Reference–Based Comparative Tools 257

11.6.2 Reference–Independent Identification ofCondition–Specific Microbial Populations from Human MetagenomicData 257

11.7 Future Perspectives in Microbiome–Enabled Diagnostics258

Acknowledgments 262

References 262

12 Genome, Exome, and Gene Panel Sequencing in a ClinicalSetting 271
Claudia Durand and Saskia Biskup

12.1 Introduction 271

12.1.1 Genetic Inheritance and Sequencing 271

12.1.2 Genetic Testing by DNA Sequencing 272

12.2 Genetic Diagnostics from a Laboratory Perspective From Sanger to NGS 273

12.2.1 Sanger Sequencing 273

12.2.2 NGS 274

12.2.3 Practical Workflow: From a Patient s DNA to NGSSequencing Analysis 276

12.2.3.1 Preparation of gDNA 277

12.2.3.2 Quality Control 277

12.2.3.3 Library Preparation and Evaluation 277

12.2.3.4 Enrichment 277

12.2.3.5 Quality Control 278

12.2.3.6 Sequencing 278

12.3 NGS Diagnostics in a Clinical Setting ComparisonBetween Genome, Exome, and Panel Diagnostics 279

12.3.1 Overview 279

12.3.2 Clinical Application of WGS 279

12.3.3 Clinical Application of WES 282

12.3.4 Clinical Application of Diagnostic Panels 284

12.4 Conclusion and Outlook 287

References 289

13 Analysis of Nucleic Acids in Single Cells 291
Stefan Kirsch, Bernhard Polzer, and Christoph A. Klein

13.1 Introduction 291

13.2 Isolating Single Cells 291

13.3 Looking at the DNA of a Single Cancer Cell 292

13.4 Molecular DNA Analysis in Single Cells 294

13.5 Approaches to Analyze RNA of a Single Cell 296

13.6 Expression Analysis in Single Cells and its BiologicalRelevance in Cancer 299

13.7 Thoughts on Bioinformatics Approaches 300

13.8 Future Impact of Single–Cell Analysis in Clinical Diagnosis301

References 303

14 Detecting Dysregulated Processes and Pathways309
Daniel Stöckel and Hans–Peter Lenhof

14.1 Introduction 309

14.2 Measuring and Normalizing Expression Profiles 311

14.2.1 Microarray Experiments 311

14.2.2 Normalization 312

14.2.3 Batch Effects 314

14.3 Biological Networks 314

14.4 Measuring the Degree of Deregulation of Individual Genes315

14.4.1 Microarray Data 316

14.4.2 RNA–Seq Data 317

14.5 Over–Representation Analysis and Gene Set EnrichmentAnalysis 318

14.5.1 Multiple Hypothesis Testing 320

14.5.2 Network–based GSEA Approaches 320

14.6 Detecting Deregulated Networks and Pathways 321

14.7 miRNA Expression Data 326

14.8 Differential Network Analysis 327

14.9 Conclusion 328

References 328

15 Companion Diagnostics and Beyond An EssentialElement in the Puzzle of Transforming Healthcare 335
Jan Kirsten

15.1 Introduction 335

15.2 The Healthcare Environment 335

15.3 What is Companion Diagnostics? 336

15.4 What are the Drivers for Companion Diagnostics? 337

15.5 Companion Diagnostics Market 338

15.6 Partnerships and Business Models for Companion Diagnostics341

15.7 Regulatory Environment for Companion Diagnostics Tests342

15.8 Outlook Beyond Companion Diagnostics TowardsHolistic Solutions 344

References 348

16 Ethical, Legal, and Psychosocial Aspects of MolecularGenetic Diagnosis 349
Wolfram Henn

16.1 General Peculiarities of Genetic Diagnoses 349

16.2 Informed Consent and Genetic Counseling 350

16.2.1 Testing of Persons with Reduced Ability to Consent352

16.3 Medical Secrecy and Data Protection 354

16.4 Predictive Diagnosis 355

16.5 Prenatal Diagnosis 356

16.6 Multiparameter Testing 358

References 359

Index 361

Andreas Keller studied Computational Biology at Saarland University in Saarbrücken (Germany), completing his PhD in 2009. He joined Febit Biomed GmbH in 2008 where he directed the biomarker discovery effort. In 2011, he joined the healthcare division of Siemens AG in Erlangen as director diagnostic innovation. In 2013 he became professor for Clinical Computational Biology at Saarland University. Dr. Keller has published more than 80 peer–reviewed manuscripts and field more than 30 patents in the area of biomarker discovery and molecular diagnostics.

Eckart Meese is Professor of Human Genetics and Molecular Biology at Saarland University Medical School in Homburg (Germany), where he directs the Institute of Human Genetics. He obtained his PhD in biology in 1987 and did postdoc work at the Arizona Cancer Center in Tucson (USA). He then joined the University of Michigan Medical Center as Assistant Professor, before joining Saarland University in 1992. His scientific focus is on miRNA and autoantibody profiling for the diagnosis of cancer and other diseases.