Biomarker Validation E-Book

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 540

Veröffentlichungsjahr: 2015

Beschreibung

Built on a decade of experience with novel molecular diagnostics, this practice-oriented guide shows how to cope with validation issues during all stages of biomarker development, from the first clinical studies to the eventual commercialization of a new diagnostic test.

Leseprobe

Table of Contents

Cover

Related Titles

Title Page

Copyright

List of Contributors

Preface

Chapter 1: Biomarkers – Past and Future

1.1 Introduction

1.2 Definitions of Biomarkers

1.3 Biomarkers in the Past

1.4 Novel Molecules and Structural Classes of Biomarkers by New Technologies

1.5 Biomarkers in Drug Research

1.7 Summary and Outlook

References

Chapter 2: Quantitative Proteomics Techniques in Biomarker Discovery

2.1 Introduction

2.2 2D-Difference Gel Electrophoresis

2.3 Mass Spectrometry-Based Proteomics

2.4 MALDI Mass Spectrometry Imaging

2.5 Conclusion

References

Chapter 3: Biomarker Qualification: A Company Point of View

3.1 Introduction

3.2 Biomarker Uses

3.3 Biomarker Types

3.4 Validation vs. Qualification

3.5 Strategic Choices in Business Models

3.6 Validation of Analytical Methods

3.7 Clinical Qualification of Candidate Biomarkers

3.8 Biomarker Qualification in the ‘omics Era

3.9 An Example of a Biomarker Provider

3.10 Conclusion

References

Chapter 4: Biomarker Discovery and Medical Diagnostic Imaging

4.1 Introduction

4.2 Factors to Consider in Biomarker Selection for Imaging

4.3 Defining the Insertion Point of the Assay and Its Business Case

4.4 Practical

In Vitro

Methods Used to Identify Biomarkers

4.5 Preclinical Models

4.6 Preclinical Analysis Techniques

4.7 Translational Considerations and Restrictions

4.8 Other Uses of Preclinical Models

4.9 Nuclear Imaging Infrastructure

4.10 Image Processing

4.11 Concluding Remarks

References

Chapter 5: Breath: An Often Overlooked Medium in Biomarker Discovery

5.1 Introduction

5.2 Breath Analysis Studies: Targets, Techniques, and Approaches

5.3 Biomarker Confounders

5.4 Biomarkers in Breath

5.5 Outlook for Breath Analysis

Acknowledgments

References

Chapter 6: HTA in Personalized Medicine Technologies

6.1 Introduction

6.2 Health Technology Assessment (HTA)

6.3 Validation and Evaluation of Biomarker Tests

6.4 Health Technology Assessment of Personalized Medicine Technologies

6.5 Concluding Remarks

References

Chapter 7: Bone Remodeling Biomarkers: New Actors on the Old Cardiovascular Stage

7.1 Introduction

7.2 Cardiovascular Disease and Osteoporosis: Common Risk Factors and Common Pathophysiological Mechanisms

7.3 Biomarkers of Bone Health in CVD

7.4 Conclusion

References

Chapter 8: Identification and Validation of Breast Cancer Biomarkers

8.1 Introduction

8.2 Current Detection and Treatment Modalities

8.3 Current Biomarker Limitations

8.4 Future Biomarker Discovery Targets

8.5 Summary

References

Chapter 9: Evaluation of Proteomic Data: From Profiling to Network Analysis by Way of Biomarker Discovery

9.1 Introduction

9.2 Proteomic Methodologies

9.3 Shotgun Proteomics

9.4 Biomarker Discovery

9.5 Protein–Protein Interaction Network Analysis

9.6 Conclusion

References

Chapter 10: Biomarkers: From Discovery to Commercialization

10.1 Comparison of Different Platforms

10.2 Mass Spectrometry

10.3 Enzyme-Linked Immunosorbent Assay

10.4 SPR Imaging

10.5 Reverse Phase Protein Microarrays

10.6 Next-Generation Sequencing (NGS)

10.7 Still a Struggle: Achieving Clinical Trial Status

10.8 Commercial Biomarker Assays

10.9 Quo Vadis, Biomarker Assays?

References

Chapter 11: Clinical Validation

11.1 Introduction

11.2 Classification of Biomarkers

11.3 Translational Use of Biomarkers

11.4 Biomarkers in Clinical Studies

11.5 Safety Markers in Clinical Development

11.6 Statistical Considerations

11.7 Validation

11.8 Regulatory Considerations for Implementation of Biomarkers in Clinical Studies

11.9 Biorepositories and Ethics

11.10 Conclusion

References

Chapter 12: Genomics and Proteomics for Biomarker Validation

12.1 Introduction

12.2 Challenges in Biomarker Discovery/Verification Phases

12.3 Verification of Biomarkers

12.4 Role of Biobanking in Biomarkers Validation

12.5 Conclusions

References

Index

End User License Agreement

Pages

xi

xii

xiii

xiv

xv

xvi

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

Guide

Cover

Table of Contents

Preface

Begin Reading

List of Illustrations

Figure 1.1

Figure 1.2

Figure 1.3

Figure 2.1

Figure 2.2

Figure 2.3

Figure 3.1

Figure 5.1

Figure 5.2

Figure 6.1

Figure 7.1

Figure 9.1

Figure 9.2

Figure 9.3

Figure 9.4

Figure 9.5

Figure 9.6

Figure 9.7

Figure 10.1

Figure 10.2

Figure 10.3

Figure 10.4

Figure 10.5

Figure 10.6

Figure 11.1

Figure 11.2

Figure 11.3

Figure 12.1

Figure 12.2

Figure 12.3

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 3.1

Table 7.1

Table 7.2

Table 8.1

Table 8.2

Table 9.1

Related Titles

Veenstra, T.D.

Proteomic Applications in Cancer Detection and Discovery

2013

Print ISBN: 978-0-471-72406-3; also available in electronic formats

Williams, J., Lalonde, R., Koup, J.R., Christ, D.D. (eds.)

Predictive Approaches in Drug Discovery and Development

Biomarkers and In Vitro/In Vivo Correlations

2012

Print ISBN: 978-0-470-17083-0; also available in electronic formats

Bleavins, M.R., Carini, C., Jurima-Romet, M., Rahbari, R. (eds.)

Biomarkers in Drug Development

Handbook of Practice, Application, and Strategy

2010

Print ISBN: 978-0-470-16927-8; also available in electronic formats

Vaidya, V.S., Bonventre, J.V. (eds.)

Biomarkers

In Medicine, Drug Discovery, and Environmental Health

2010

Print ISBN: 978-0-470-45224-0; also available in electronic formats

Bioinformatics and Biomarker Discovery - “Omic” Data Analysis for Personalized Medicine

2010

Print ISBN: 978-0-470-74460-4; also available in electronic formats

ISBN: 978-0-470-68642-3

Edited by Harald Seitz and Sarah Schumacher

Biomarker Validation

Technological, Clinical and Commercial Aspects

The Editors

Dr. Harald Seitz

Fraunhofer IZI-BB

Potsdam

Germany

Sarah Schumacher

Fraunhofer IBMT

Potsdam

Germany

Cover

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Library of Congress Card No.: applied for

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Bibliographic information published by the Deutsche Nationalbibliothek

The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de.

© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

Print ISBN: 978-3-527-33719-4

ePDF ISBN: 978-3-527-68066-5

ePub ISBN: 978-3-527-68067-2

Mobi ISBN: 978-3-527-68068-9

oBook ISBN: 978-3-527-68065-8

Printing and Binding Markono Print Media Pte Ltd., Singapore

List of Contributors

Jonathan D Beauchamp

Fraunhofer Institute for Process Engineering and Packaging IVV

Department of Sensory Analytics

Freising

Germany

Kaïdre Bendjama

Firalis SAS, 35 Rue du Fort

Huningue

France

Thilo Bracht

Medizinisches Proteom-Center

Zentrum für klin. Forschung

Raum 1.062, Ruhr-Universität Bochum

Universitätsstraße 150

Bochum

Germany

Francesca Brambilla

Proteomics and Metabolomics Department

Institute for Biomedical Technologies – National Research Council (CNR)

Fratelli Cervi 93

20090, Segrate (Milan)

Italy

Maximilian Breitner

Firalis SAS, 35 Rue du Fort

Huningue

France

Juan Casado-Vela

Spanish National Research Council (CSIC)

Spanish National Biotechnology Centre (CNB)

Darwin 3, Cantoblanco

Madrid

Spain

Rosa M

a

Dégano

Proteomics Unit, Cancer Research Institute

IBSAL, University of Salamanca-CSIC

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

Paula Díez

Cancer Research Institute, University of Salamanca-CSIC

Avda University of Coimbra IBSAL,

Department of Medicine General Cytometry Service

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

and

Proteomics Unit, Cancer Research Institute

IBSAL, University of Salamanca-CSIC

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

Hüseyin Firat

Firalis SAS, 35 Rue du Fort

Huningue

France

Manuel Fuentes

Cancer Research Institute, University of Salamanca-CSIC

Avda University of Coimbra

IBSAL, Department of Medicine

General Cytometry Service

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

and

Proteomics Unit, Cancer Research Institute

IBSAL, University of Salamanca-CSIC

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

Corinna Henkel

Medizinisches Proteom-Center, Zentrum für klin. Forschung

Raum 1.062, Ruhr-Universität Bochum

Universitätsstraße 150

Bochum

Germany

Franz Hessel

SRH Hochschule Berlin Healthcare Management

Ernst-Reuter-Platz 10

Berlin

Germany

Sebastian Hoppe

Fraunhofer Institute for Cell Therapy and Immunology Branch Bioanalytics and Bioprocesses (IZI-BB)

Department of Bioanalytics and Biosensorics

Am Muehlenberg 13

Potsdam

Germany

Nieves Ibarrola

Proteomics Unit, Cancer Research Institute

IBSAL, University of Salamanca-CSIC

Campus Miguel de Unamuno S/N, 37007 Salamanca

Spain

Giorgio Iervasi

Fondazione G. Monasterio CNR-Regione Toscana and Institute of Clinical Physiology-CNR

Via Moruzzi 1

Pisa

Italy

Kori Jackson

University of Texas Health Science Center

Department of Surgery

US Hwy 271 Tyler TX 75708

USA

Silvia Maffei

Fondazione G. Monasterio CNR-Regione Toscana and Institute of Clinical Physiology-CNR

Via Moruzzi 1

Pisa

Italy

Pierluigi Mauri

Proteomics and Metabolomics Department

Institute for Biomedical Technologies – National Research Council (CNR)

Fratelli Cervi 93

20090, Segrate (Milan)

Italy

Dominik Andre Megger

Medizinisches Proteom-Center, Zentrum für klin. Forschung

Raum 1.062, Ruhr-Universität Bochum

Universitätsstraße 150

Bochum

Germany

Henry Memczak

University of Potsdam, Institute of Biochemistry and Biology

Department of Molecular Bioanalytics and Bioelectronics

Karl-Liebknecht-Straße 24/25

Potsdam

Germany

Sara Motta

Proteomics and Metabolomics Department

Institute for Biomedical Technologies – National Research Council (CNR)

Fratelli Cervi 93

20090, Segrate (Milan)

Italy

Wael Naboulsi

Medizinisches Proteom-Center, Zentrum für klin. Forschung

Raum 1.062, Ruhr-Universität Bochum

Universitätsstraße 150

Bochum

Germany

Siegfried Neumann

Clemens-Schoepf-Institut für Organische Chemie und Biochemie

Technische Universität Darmstadt

Alarich-Weiss-Strasse 4

Darmstadt

Germany

Joachim D Pleil

Human Exposure and Atmospheric Sciences Division

National Exposure Research Laboratory

US Environmental Protection Agency

Research Triangle Park

NC 27709

USA

Mads Almose Røpke

Clinical Pharmacology Medical Department

LEO Pharma A/S

Industriparken 55

Ballerup

Denmark

Andreas P. Sakka

GE Healthcare, The Grove Centre

White Lion Road, Amersham

Buckinghamshire, HP7 9LL

UK

Edward Sauter

University of Texas Health Science Center

Department of Surgery

US Hwy 271 Tyler TX 75708

USA

Dario Di Silvestre

Proteomics and Metabolomics Department

Institute for Biomedical Technologies – National Research Council (CNR)

Fratelli Cervi 93

20090, Segrate (Milan)

Italy

Barbara Sitek

Medizinisches Proteom-Center, Zentrum für klin. Forschung

Raum 1.062, Ruhr-Universität Bochum

Universitätsstraße 150

Bochum

Germany

Cristina Vassalle

Fondazione G. Monaserio CNR-Regione Toscana and Institute of Clinical Physiology-CNR

Via Moruzzi 1

Pisa

Italy

James R. Whiteside

GE Healthcare, The Grove Centre

White Lion Road, Amersham

Buckinghamshire, HP7 9LL

UK

Preface

The term biomarker is a very general one and covers a wide range that includes cells, genes, proteins, hormones, or lipids. The official definition of a biomarker by the NIH (National Institutes of Health, USA) is “A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”

Complex organ function or characteristic changes in biological structures can be used as medical indicators/biomarkers, for example, for patient stratification and prediction of the response to a treatment. In recent years, the interest in the pharmaceutical industry in the exploration of biomarkers was increased, and many small and medium enterprises were founded. This is reflected by an over 10-fold increased number of clinical trials with biomarkers in the recent years. Despite intensive research efforts and huge amounts of money that have been invested by research institutes and pharmaceutical industry as well as by governments, many complex diseases, such as AIDS or cancer, are not or (too) late diagnosable or even treatable.

The process of biomarker validation and development includes the discovery phase, clinical study design, qualification, verification, assay optimization, clinical validation, and commercialization of the biomarker(s). The present book deals with all these different aspects and examples of current methods and concepts are given. A complete overview of all methods used today to identify biomarkers and the different kinds of biomarkers that might contribute to better diagnosis and treatment is not the focus of the book. The book rather sheds some light on how biomarkers are validated today, what the common hurdles are, and what can be learned from many years/decades of biomarker research and validation. The book is divided into different sections, addressing among others, technologies for biomarker discovery, clinical trials, and bioinformatics. Finally, future opportunities and trends of biomarkers and the challenges in the future are discussed.

We think these different sections deal with the essential aspects of biomarker validation and provide trustworthy data.

This book contains contributions from experts in different fields and from universities as well as research institutes to make sure that varying viewpoints are presented.

We want to thank Wiley-VCH-Verlag for giving us the opportunity to write this book. Furthermore, the editors thank the project coordinators Dr. Frank Weinreich and Dr. Gregor Cicchetti.

We also want to acknowledge the authors for distributing their chapters and spending their time in preparing interesting articles.

Finally, we want to thank all colleagues who made this book possible.

In conclusion, we hope that you will find this book inspiring literature and gain useful knowledge about biomarkers.

Sarah SchumacherHarald Seitz

Chapter 1Biomarkers – Past and Future

Siegfried Neumann

1.1 Introduction

Biomarker is a term used for a characteristic property that can be precisely measured and objectively validated. Biomarkers serve as indicators of biological processes in health, disease or disease stages, or in the body's response to a therapeutic intervention. “Biomarker” is relatively new as a buzz word as it was recently coined in conjunction with the advent of molecular analysis in research programs and in exploratory medical diagnosis. However, quantitative data on the characteristics of physiological reactions in relation to functional changes and on molecular analytes in blood, serum, or other body fluids have been used in research and laboratory medicine for long, for example, some since the beginning of the preceding century.

Key advances in analytical instrumentation and its development for analytical precision and sensitivity revolutionized our knowledge of molecular and cellular biology of body functions. On the other hand, discoveries in basic biology science continuously pushed the demands on increasing the resolution power of technologies. This is true for science and technology in the high-speed sequencing of whole genomes and their transcription profiles, for the differentiation of protein patterns in a biological sample toward unprecedented borders of resolution, and for high-resolution submicroscopic cellular imaging. The convergence of many disruptive developments in instrument-based analytical precision with scientific discoveries in the molecular universe of genomic DNA, various types of RNA molecules as their transcripts, and proteins as the translation products led to the elucidation of crucial molecular interactions and allowed modeling of regulatory networks. In the field of malignant diseases, new paradigms for the molecular biology of pathogenetic processes lead to a deeper understanding of pathophysiological mechanisms working in the onset and spread of cancer.

As a consequence, one can expect that in medical practice the classification of diseases and the rules and decisions on their treatment will see dramatic changes in the near future. In a variety of diseases, the canonical definitions based on clinical symptoms, for example, a descriptional classification, and treatment based on an afflicted organ will both change toward molecular diagnosis, altered molecular markers, and a targeted intervention directed toward causative mechanisms that drive a pathological process. In the best case, identification of those molecular drivers can lead to early intervention and also provide options for prophylaxis.

In biomedicine, there are an ever-increasing number of new molecular markers for metabolic disorders, cardiovascular diseases, neurological disorders, chronic inflammatory diseases, and cancer. Accumulating data from molecular diagnostics offer physicians various complex marker panels to stratify patients for subtypes of a disease, evaluate the disease stage, and adjust their therapy regimen accordingly. This direction is now overarchingly termed as “precision medicine.” Such a research-driven direction in medicine is nourished by activities in laboratory medicine, in vivo diagnostics for body/tissue imaging and cytology, and histology research by pathologists. This short overview illuminates the technologic status of biomarkers through selected examples, for example, with a main focus on oncology, and tries to anticipate where the developments on biomarkers might go in the coming years.

1.2 Definitions of Biomarkers

In 2001 the Biomarkers Definitions Working group termed “biomarker” as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [1].

In a similar understanding the term “cancer biomarker” was described as a “biomarker that is present in tumor tissue or serum and includes many different molecules, such as DNA, mRNA, or proteins: Tumor biomarkers are measured in tumor tissue and, tumor DNA biomarkers are measured from tumor tissue” [2]. In addition, there is now evidence that a tumor biomarker may also become delineated from the titer and genotype of circulating tumor cells [3] and from the circulating tumor cell DNA.

Biomarkers signal differences in biological states. The many potential applications of using biomarkers also include detection of pharmacologic profiles in a given group of probands, for instance, for detecting kidney toxicity after drug exposure in clinical trials with novel drugs or after the approval of drugs [4]. The term biomarker is not restricted by how the marker is measured. Thus, a valuable quantitative data profile may be obtained by means of a physical, optical, or enzyme assay, immunochemistry, mass spectrometry, cytological and histochemical analyses or by in vivo imaging techniques such as magnetic resonance tomography or positron emission tomography. In addition, a combination of data from various analytical levels is frequently in use.

In preclinical drug research and development biomarkers can address different key questions. They can contribute selectively to either one of the following research aims:

Pharmacodynamic biomarkers

: are related to the mechanisms of the action of a drug and give answers on whether the drug reaches and interacts with its target and whether this creates downstream effects.

Disease biomarkers

: are related to a disease type or subtype and answer questions such as whether the drug affects a disease-relevant or a disease-relevant intermittent phenotype, which can be measured with a biomarker or biomarker panel.

Predictive biomarkers

: are associated with the response or lack of response to a particular therapy.

Surrogate biomarkers

: substitute a clinical endpoint. They are expected to predict a clinical benefit or harm or – on the contrary – a lack of benefit or harm. They inform on whether the treatment can reduce the disease burden as measured by clinical endpoints.

For the use of biomarkers in clinical practice a solid understanding and knowledge of their role in molecular and cellular biology of disease processes is a prerequisite in order to demonstrate a benefit in reduction of disease burden. Response monitoring by using imaging biomarkers in clinical trials will support identification of effective versus ineffective drugs and dose finding, and might correlate with the overall clinical efficacy. In conclusion, biomarkers can serve for a variety of crucial medical objectives, both in preclinical research and clinical development of drugs as well as in diagnosis, treatment decisions, and prognosis in clinical practice [5].

1.3 Biomarkers in the Past

Biomarkers of various molecular classes have been in medical use since the beginning of the twentieth century, for instance, since the impact of inborn errors of metabolism on neurological development was recognized by Garrod (1908) [6]. In the middle of the twentieth century the development accelerated when filter-based and spectral photometers became available to the laboratories and when the analytical portfolio for enzyme assays was continuously expanding. This step allowed measuring the presence and levels of many enzymes in samples from healthy and diseased individuals by clinical chemistry. The introduction of protein separation by electrophoresis and the quantitation of proteins and peptide hormones by radioimmunoassays and later by enzyme-linked immunoassays helped discover the presence of various tissue-derived oligo- and polypeptides as well as glycoproteins as disease markers in circulating blood, cerebrospinal fluid, and other body fluids as well as in tissue extracts. This then lead to the introduction of many enzymes, proteins, peptide markers, steroids, and metabolite-like lipids and cholesterol as measurable entities in panels of diagnostic parameters.

Table 1.1 as a selected listing makes a case on how the detection of tissue-derived markers, in the form of enzyme activities, protein concentrations, and metabolite levels, helped in the detection and staging of organ damage in a variety of classes of human disease.

Table 1.1 Established diagnostic measurements by clinical chemistry on serum, selected by organ, or systemic disease

Disease

Serum analyte

Liver

Enzymes

Glutamate-oxalacetate-transaminase (EC 2.6.1.1)

Glutamate-pyruvate-transaminase (EC 2.6.1.2)

Glutamate-dehydrogenase (EC 1.4.1.3)

Alkaline phosphatase (EC 3.1.3.1)

Gamma-glutamyl-transferase (EC 2.3.2.2)

Protein

Carbohydrate deficient transferrin

ECM compounds

Collagen propeptide

Metabolites

Galactose tolerance

Ammonia

Pancreas

Enzymes

α-Amylase (EC 3.2.1.1)

Lipase (EC 3.1.1.3)

Kidney

Metabolites

Creatinin

Urea

Function tests

Creatinin clearance

Heart, cardiovascular diseases

Enzymes

Creatinkinase (EC 2.7.3.2)

Creatinkinase MB isoenzyme (EC 2.7.3.2)

Lactatdehydronase (EC 1.1.1.27)

Structural proteins

Cardiac troponins cTnT, cTnI

Proteins

Myoglobin

C-reactive protein

Pregnancy-associated plasma protein-A (PAPP-A)

Peptides

B-type natriuretic peptide (BNP)

N-terminal pro BNP (NT-pro BNP)

Bone disease

Enzyme

Alkaline phosphatase (EC 3.1.3.1)

Hormone

Parathormone

Metabolites

Phosphate

Calcium

Clinical Chemistry/Laboratory Medicine departments were the main drivers to exploit the wealth of information from the occurrence and levels of these analytes for early detection, differential diagnosis, and therapy control in clinical practice, and for outdoor patients. For more detailed information, the reader is referred to textbooks on clinical chemistry [7–10].

Many advances have been achieved in the discovery of biomarkers as sensors for different stages in long-lasting disease processes. Immunochemical assays in a practical performance format and characterized by high analytical accuracy were a major breakthrough here. For instance, such progress has been achieved for a routine utilization of biomarkers in cardiovascular diseases. This also includes emergency situations. Immunoassays for cardiac markers allow a longitudinal follow-up from the early onset of atherosclerosis to the final damage in myocardial tissue due to acute myocardial infarction (Figure 1.1).

Figure 1.1 Biomarkers in the diagnosis of cardiovascular disease processes. (Adapted from 11, modified.)

In an acute disease attack solid phase immunoassay techniques for point of care (PoC) testing allow the determination of a selection of these biomarkers (cTnI, creatine kinase isoenzyme MB, myoglobin) and enable a rapid detection of acute myocardial infarction in the emergency room. Thereby, the use of PoC tests on biomarkers is crucial for differential diagnosis and for decision making on early interventions through an appropriate anti-clotting therapy [12].

In oncology, technology in laboratory medicine reached a boom by the introduction of immunoassays for tumor markers. In advanced stages of a malignant disease tumor marker molecules are produced or secreted by malignant cells. They appear in the blood or other body fluids. Their absolute specificity for tumor has been under discussion for long and this restriction definitely has prevented their broad utilization in screening for early tumor detection in patients with no clinical symptoms [13, 14]. Tumor marker tests have to guarantee high accuracy in terms of specificity and sensitivity. When they reach these requirements they are frequently useful for differential diagnosis and staging of tumor-bearing patients.Table 1.2 lists tumor marker assays, which until now have been frequently used in medical practice. In most indications their main clinical application is for therapy monitoring and recognition of tumor progression, for example, by longitudinal observation of blood levels of tumor marker following the initial therapeutic intervention.

Table 1.2 Frequently used established tumor markers (excerpted from Ref. [7])

Analyte

Assay technology

Major applications

Carcinoembryonic antigen (CEA)

Immunoassays (EIA, FIA, CIA)

Carcinoma of pancreas, lung, colorectum, stomach, breast

Cancer antigen 19-9 (CA 19-9)

Immunoassays (RIA, EIA)

Carcinoma of pancreas, bile ductus, liver, stomach, colon

Mucins of mamma carcinoma (CA 15-3, CA 549, MCA, BCM)

Immunoassays (RIA, FIA, EIA)

Carcinoma of breast, ovarium, bile ductus, and others

Alpha fetoprotein (AFP)

Immunoassays (RIA, FIA, EIA)

Hepatocellular carcinoma, germ cell carcinoma

Human chorion-gonado trophin (HCG)

Immunoassays (RIA, FIA, EIA)

Chorioncarcinoma, mole, germ cell carcinoma

Prostate-specific antigen (PSA)

Immunoassays (RIA, EIA)

Prostate carcinoma

Minimal residual disease (MRD) in leukemia or lymphoma

PCR

Evidence for residual leukemia cells, chronic myeloid leukemia, and others

Erb-B2

IHC, FISH

Carcinoma of breast, gastric cancer

Erb-B1

IHC, FISH

Colorectal carcinoma. head- and neck-carcinoma, lung carcinoma, and others

EIA – enzyme linked immunoassay; FIA – fluorescence immunoassay; RIA – radio immunoassay; PCR – polymerase chain reaction technique; IHC – immunohistochemistry; FISH – fluorescence in-situ hybridization; MCA – mucin-like carcinoma associated antigen; and BCM – breast cancer mucin.

The next phase in the state-of-the-art of tumor markers started with the introduction of the therapeutic antibody trastuzumab (Herceptin®) against the tumor-associated antigen erbB-2. This molecule is amplified both in copy numbers of its gene and by the expression of the protein on the surface of tumor cells in a large fraction of patients with mammary tumor. These modalities of amplification can be detected by immunohistology (for increased erbB-2 expression in the tissue from a tumor biopsy) and/or by proof of erbB-2 gene amplification by fluorescence in situ hybridization in the tissue sample; see Table 1.2. A combination of chemotherapy with the trastuzumab antibody has led to a significant increase in overall survival time in a fraction of those treated patients who showed overexpression of this tumor marker. By the end of the 1990s, this instant success initiated the broader awareness that appropriate biomarkers can provide a valuable stratification of patients into a class of potential responders for a special immunological treatment versus non-responders. This is nowadays cited as the “personalized medicine” paradigm.

A similar situation was matched some years later with another therapeutic antibody, for example, cetuximab (Erbitux®), which is directed to epidermal growth factor (EGF) receptor erbB-1, another physiologically important member of the erb-B family of surface receptors for the growth factors EGF, TGF alpha, and some others (Table 1.2).

Both of these fields of therapeutic application subsequently met significant limitations insofar as a durable curative efficacy of this personalized approach could not be achieved. Obviously, the ubiquitous heterogeneity of tumor cells within a tumor and in its metastases as well as the robustness of tumors by means of clonal selection dynamics in the course of a treatment continuously demand additional therapeutic options.

1.4 Novel Molecules and Structural Classes of Biomarkers by New Technologies

Molecular biology revolutionizes biomedicine in ongoing activities [15]. It helps understand biologic defects, elaborate new drug targets and candidates for new biological entities as candidate drugs, and elucidates molecular diversity as a source for novel biomarkers. On the cellular level, all stages in the regulatory hierarchy of molecules can serve for discovery of biomarkers.

DNA

on the genomic level by copy number variations, gene amplification and mutations, genetic polymorphisms, and disease-related methylation patterns

RNA transcripts

by their expression levels and alternative splicing products, also by the expression of noncoding transcripts as RNAi or microRNA molecules

Proteins

by expression levels, altered activities, and localizations, and by a variety of post-translational modifications

Lipids and other metabolites

by their molecular identity and concentration.

Functional genomics is the most advanced technical approach for the detection of biomarkers in research and clinical diagnostics, using a highly developed set of technologies for analytics of nucleic acid molecules as frequently used in oncology research (Figure 1.2). This development started in the 1990s (see for instance [13, 14]).

Figure 1.2 Impact of genomics on identification of novel biomarker candidates.

Genomic sequences can now be retrieved from comprehensive genome data bases such as ENSEMBL [16], which collects whole genome sequences, single nucleotide polymorphisms, and data on gene expression in different tissues. Other genome data bases such as the NCBI Genome [17] or CODIS [18] organize information on sequences, maps, chromosomes, assemblies, and annotations. The broad range of established applications for molecular diagnostics is given in appropriate details in modern textbooks (such as in [8, 9, 19]). Gene expression profiles are now also developing in clinical research in cardiovascular diseases [20]. Network-based stratifications of tumor mutations were recently demonstrated in ovarian and uterine cancer cohorts [21] from the Cancer Genome Atlas [22].

Following the worldwide active collection of genomic data, similar activities came up in protein data collection and their asservation for data mining and biomarker search in data bases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [23], Gene Ontology (GO) [24], and STRING [25], among others, all of which help predict protein functions and functional interaction networks of proteins. In May 2014 two seminal publications [26, 27] presented catalogs on large human proteomes. This opens a deep search for biomarkers of proteins in health and disease in a similar manner to the earlier search of data bases for human genome(s) and transcriptome(s). The mass-spectroscopy-based Proteomics DB [27] contains proteomic data from 60 human tissues, 13 body fluids, and 147 cancer cell lines. Altogether both data bases cover some 18 000 proteins of the estimated 20 000 proteins in the human proteome. Clinical proteomics will now closely follow these presentations by associating distinct protein patterns with the onset or progression of a disease. Clinical proteomics will seek biomarkers and biomarker signatures, assign those biomarkers by appearance and levels to clinical subtypes in order to support differential diagnosis, and correlate biomarker levels with clinical outcome, such as those reported in [28] on proteomic risk markers for coronary heart disease and stroke.

Last but not least, metabolites also enter the spotlight. In the past, metabolites such as glucose or circulating lipids have long been used in diagnostics as risk biomarkers for atherosclerosis, coronary heart disease, or stroke. Recently, large epidemiologic studies using mass spectroscopic differential analyses discovered associations between patterns of small molecular weight molecules and metabolic diseases. Some novel fingerprints of small molecules in serum become apparent and these may step in as potential biomarkers for predictive and prognostic diagnostics [28, 29]. In addition, a new epigenome-wide study on this population revealed some correlations between DNA methylation and metabolic traits (called metabotypes by these authors) in human blood [31].

The direct correlations between aberrant DNA sequences, transcriptome profiles, protein patterns, and metabolic profiles within a cell or a tissue have yet to be more tightly elucidated – a demanding task for future work by bioinformatics.

1.5 Biomarkers in Drug Research

Biomarkers are now established as important tools both in preclinical research and clinical development of a candidate drug and this extends to the marketing phase of a drug (Table 1.3). In preclinical research biomarkers can serve as target molecules for drug activity screening and for elucidation of their mode of action in a molecular or cellular test setting. On the organismal level, biomarkers can signal both efficacy and toxicological effects in an animal model. In drug discovery work, biomarkers can play a major role in studies on pharmacodynamics:

In

proximal pharmacodynamics,

reactions of the biomarker can correlate with target modulation independent of the overall response of the model; thus it helps optimize a drug lead candidate and is important information for dose finding for first-in-man studies.

In a more

distal phase in pharmacodynamics,

biomarkers help

demonstrate the modulatory effects along a signaling pathway,

provide information on dose finding,

show efficacy of a drug and

thus contribute to demonstrate the proof of principle of a drug candidate.

Table 1.3 Relevance of biomarkers in the drug research process

Targets

Preclinical stage

Clinical trial: early phases

Clinical trial: late phases

Post-marketing

Identification

HTS for lead discovery

Bioavailability

Stratification of patient populations for disease subtypes

Patient stratification

Validation

SAR of targets for lead compounds

Bioequivalence

Monitoring of therapy response

Analyses of disease causes and for key factors for disease progression

Pharmacokinetics

Dose–response ratio

Dosage finding

Recognition of adverse drug responses

Pharmacodynamics

Data submission for approval

Competitive effectiveness studies

Toxicity testing

Toxicity in man

Mode of action

Proof of concept in man

HTS – high-throughput screening and SARs – structure–activity relationships.

In the clinical development of new drugs biomarkers serve different objectives. Table 1.3 defines some objectives for biomarker monitoring. In the early clinical phases, for example, phase I and II, the focus is on pharmacological criteria; in late phase II, dosage optimization and patient stratification are in focus, which then is extended in multicentric studies by phase III. The Boston Consulting Group (BCG) provided data on the use of biomarkers in clinical trials through a study that was based on data from the data base clinicaltrials.gov over the period from 2005 to 2011 [32]. In all these trials the share of studies that used biomarkers was

21% in phase I,

27% in phase II,

17% in phase III, and

18% in phase IV.

By now, it is a commonplace statement by major drug companies that they involve accompanying biomarker studies in more than half of their trials, especially for the clinical development of a novel drug. Another survey from 2010 found that at least 50% of clinical trials are collecting DNA from study participants to aid in the discovery of drug-related safety and efficacy biomarkers, and 30% of the companies in the survey require all drug compounds in development to have a biomarker [33].

How should biomarkers be clinically evaluated? For validation of biomarkers in clinical oncology the European Expert Group proposed a four-phase model for biomarker monitoring [34]. Following this proposal will imply that in clinical phase I biomarker kinetics and correlation with tumor burden in the patient population are assessed. Phase II evaluates whether the biomarker is able to identify, exclude, and/or predict changes in the patients' disease status. Phase III evaluates the effectiveness of the intervention, which is guided by the tumor biomarker by measuring the patient outcome in randomized trials. Phase IV will monitor the long-term effects when monitoring of the tumor biomarker has become the standard in patient care.

Which molecular entity should a biomarker or a marker use in order to become translated to use in routine laboratory medicine? Table 1.4 presents a practical view regarding this issue.

Table 1.4 Sources and types of biomarkers

Type of biomarkers

Difficulty to discover

Difficulty to implement in clinic routine

Pharmacodynamic biomarker (PDγ)

Plasma marker imaging protein expression (IHC) expression profiles

Disease biomarker

Imaging, plasma marker, protein expression, expression profiles

Predictive biomarkers

Plasma marker, imaging, protein expression, (IHC), genetic/genomic alterations, expression profiles

Surrogate biomarker

Plasma marker protein expression (IHC), expression profiles

The size within an arrow indicates the level of problem.

IHC – immunohistochemistry.

These analytical approaches differ distinctly in terms of assessment of samples, needs for appropriate instrumentation and demands on laboratory and technical expertise: Plasma markers in the blood sample as easily accessible biological specimen are technically feasible, imaging markers need expensive instrumental investments and require that for in vivo use technological facilities are available on call, protein pattern analysis by immunohistochemistry needs provision and storage of suitable cell or tissue samples, and transcriptome analysis on a cellular extract and by polynucleotide enrichment demands availability of an experienced molecular biology facility. Research laboratories and routine clinical practice, such as that under the conditions of a hospital outside of a university medicine environment, will differ in their views on how to establish proper implementation of testing for molecular biomarkers.

1.6 Current Development and Future Trends for Biomarkers in Laboratory Diagnostics

1.6.1 Biomarker Test Validation

Where does clinical validity of biomarkers stand now? The early phase in biomedicine in the years after the human genome has been sequenced started from a gray zone: “Even now, disappointment might be expected, in part because rules of evidence to assess the validity of studies about diagnosis and prognosis are both underdeveloped and not routinely applied” [35]. What then is needed to overcome this situation? In an OECD workshop in 2008 on biomarkers the discussions went around a concept that separated the development of biomarkers or a multivariate panel of them into two phases [36]:

Assay development

, which covers the development of the assay technology, with work to prove its analytical validity and collection of data to prove the association of the biomarker(s) with a disease or with distinct disease stages to reach scientific validity.

Criteria for the analytical accuracy of an assay are well defined for developmental projects in laboratory medicine. Such criteria are sensitivity, specificity, positive and negative predictive values, receiver operator characteristics, likelihood ratios, and odds ratios [7–10, 19, 37, 38]; see also Chapter XX in this book.

Next, the developed assay will be translated to a

test version

for work on clinical validation. This involves measurement of the test performance under clinical real-life conditions. These differ from those in a research laboratory in many aspects. Clinical scientists or pathologists will then validate the technical maturity of the test under clinical conditions and clinicians will review and report on its clinical utility.

The accuracy of a biomarker assay or a combination of biomarkers in a diagnostic model is proved by their ability to

identify a target disease in a patient,

differentiate between diseases with similar clinical symptoms, or

predict a patient's response to treatment or monitor effects of therapy on the patient's disease burden.

For validation of a pattern recognition model in discovery-based research the biostatistics of overfitting can become a problem [35]. It can be reduced by splitting the tested population into two groups, that is, a training set and an independent validation set, both endowed with biostatistically sufficient group sizes. This separation step can be organized by randomly splitting the original test population, which should be present in sufficient size, into two cohorts (split-sample validation) [35].

In a well-planned design for development of a diagnostic molecular biomarker, the overall sequence chain for studies should extend from a discovery phase to clinical validation and proof for clinical utility through steps with various objectives (Table 1.5) [2].

Table 1.5 Phases in the development of diagnostic biomarkers

Phase

Description

Objectives

I

Discovery

Identification of promising biomarker candidates

Assay development

Define and optimize the analytical process into robust, reproducible, and valid device

Retrospective validation

Clinical assay detects disease, develop a first algorithm for combination test

II

Retrospective refinement

Validate early detection properties of biomarker (set), development/refinement of algorithm for combination tests

III

Prospective investigation

Determine diagnostic accuracy (sensitivity, specificity) in situation of clinical practice

IV

Randomized controlled trial

Quantify effect of making the biomarker information available to the physician to optimize treatment

V

Health economics study

Quantify cost-effectiveness, evaluate clinical utility from a societal perspective

Adapted from Ref. [2], modified.

These studies cover both work on analytical development, analytical and scientific validity, clinical validity, and finally utility in clinical decision making. However, by now only in a few cases biomarker studies have made it up to presenting results for the final phase, that is, data provision for a benefit–cost ratio. Clinical utility has to be proved to reach acceptance by the medical community, regulatory authorities, and willingness of healthcare payers for reimbursement. This is written even though by common sense the benefits of biomarker testing for patients and physician may be obvious to the developers.

When industrial providers are going to introduce a biomarker product to the market a variety of information will be needed in order to verify the clinical relevance of testing and to provide its evidence in order to reach a broad clinical exploitation [36, 39].

A conclusive analysis of the available data from studies before and after its market introduction

The clinical claims for handling, appropriate labeling, and product information

Definition of patient groups with special restrictions for use of the measurement

Information on comparable measurements that support safety and/or mode of application

Indications, clinical confounding factors, stage and severity of a disease, criteria for a suitable sample, patient populations.

1.6.2 Companion Diagnostics in Clinical Pharmacology

Patients in a population almost differ by an intrinsic variability in pharmacokinetics and pharmacodynamics, for example, variation is seen in delivery and input rate, drug metabolism and transport, drug access to the site(s) of action, type of transduction, drug–target interaction, disease stage, and homeostasis. In addition, there are issues with extrinsic variability, such as drug–drug interactions, drug–food interactions, and interactions with endogenous substances. One drug with one dosage does not fit all patients within a given disease class. It was reported that the percentage of a patient population for which a particular drug in a disease entity is ineffective ranges between 38 and 75% on the average [40].

In many indications clinical prediction on how a patient will react to a treatment can be supported by biomarker measurements for efficacy and safety. Pharmacogenomics (genomics to study how a drug acts) and pharmacogenetics (scientific information on how genes affect a person's drug response) create a large potential to optimize drug selection and drug dosing for therapy. Novel low molecular weight drugs in the kinase inhibitor class as new medical entities have recently been clinically developed and successfully submitted to the regulatory authorities in the United States and Europe. In these cases the application of a measurement for efficacy by an appropriate biomarker has become indispensable to check the patient's ability to respond to that drug. Regulatory authorities combined the approval of these drugs with the need to determine the putative responsiveness by a suitable biomarker [33, 41].

In each of the enlisted drugs from Table 1.6 the biomarker technology applies a molecular biology test to affirm that the patient carries the crucial genetically determined aberration that is addressed by the singular drug for a protein target-selective mode of action. In this way, the patient population will be stratified.

Table 1.6 Recent advances in companion diagnostics for cancer treatment by kinase inhibitors

Drug

Indication

Company

Companion diagnostic device

Year of approval of CDx

Clinical relevance

Vemurafenib (Zelboraf®)

Metastatic melanoma

Roche, Daiichi-Sankyo

Cobas®4800 BRAF V600E mutation test (Roche)

PMA (FDA) in August 2011

Patient stratification for treatment inclusion

PMA (FDA)

THxID®-BRAF test (bio Merieux)

In May 2013

Crizotinib (Xalkori®)

Advanced non-small-cell lung cancer, subpopulation

Pfizer

Vysis ALK Break Apart FISH Probe Kit (Abbott Molecular, Inc.)

PMA (FDA) in September 2011

Selection of NSCLC subgroup with translocation of ALK for Crizotinib therapy

Erlotinib (Tarceva®)

Metastatic non-small-cell lung cancer, subpopulation, for first-line monotherapy

Roche

Cobas EGFR-mutation test (Roche)

Cobas EGFR-mutation test CE marked by second half of 2011

Selection of NSCLC subgroup with EGFR-activating mutation subtype

CDx – companion diagnostic.

There are ongoing activities in academic research and by research and development departments in drug companies to extend this companion diagnostics approach, be it in a combination of new drugs with evidence for a disease-driving function of the target or by addressing such targets by existing drugs that have not been used for that disease, for example, in a new medical use case. In a report from 2013 genetic classification has been successfully used to classify patients in a population of more than 6000 patients for lung tumor subtypes [42]. The results of this consortial study have led to a reclassification of one cancer subtype, that is, large cell lung cancers. The results also initiated a wider clinical use of stratified patient management and prognostic information. Various patients experienced a distinct survival benefit when their therapy has been adapted according to the information from genetic testing on a selected set of candidate genetic markers (ERB-B2amp, ALKfusion, B-RAFmut, EGFR mut, K-RASmut, PIK3CAmut, FGFr1amp, DDR2mut).

When therapeutic antibodies were introduced to tumor treatment (Section 1.3), it became apparent that a therapeutic response was observed in just a share of the patient groups. As a consequence, patients will be analyzed for a strong expression of the antibody target before treatment with these biologicals is taken into clinical consideration. The established immunochemical or molecular biology tests help stratify the patients. Patients who are considered not to develop a response do not get this costly treatment, which besides its therapeutic benefits in a share of the responder patients may also introduce adverse effects to many of the recipients of the biopharmaceutical.

In the case of the therapeutic antibodies cetuximab and panitumumab, which both bind to the tumor-associated target EGF receptor, a response testing for responsiveness does not only look at whether the tumor tissue overexpresses the target on the tumor cells. In addition, a molecular biology test looks for the mutational status of the gene for K-RAS, which is a main mediator of signal transduction downstream of the EGF receptor (Figure 1.3).

Figure 1.3Relevance of tumor biomarkers for antibody therapeutics. Cetuximab or panitumumab are antibody drugs that block the EGF-receptor-mediated cell activation. Mutants in pathways for signal transduction, which have to be considered for therapy resistance, are marked by bold letters.

In the course of clinical trials with these antibodies it became clear that tumor cells with mutants of the putative oncogene K-RAS show a constitutive activation for cell cycle progression, survival, and proliferation irrespective of the antibody binding to its EGF receptor target. This evidence led the FDA and European Medicines Agency (EMA) to the approval of cetuximab, which is given in combination with chemotherapy combined with obligatory testing for the presence of wild type K-RAS. Approval is valid for first-line treatment of metastatic colorectal tumor [43]. There is upcoming preliminary clinical evidence that some mutations in other genes for mediators of the signal transduction network, which all are downstream of the EGF receptor, may also be related to resistance to the antibody treatment (indicated by bold circles in Figure 1.3).

1.6.3 Biomarker Multivariate Index Assays

Variation of both the occurrence of biomarkers and their levels reflects variability, which is a common observation in biology, for example, on the level of a human population, between tissues and cells of an individual, and/or between different persons with an apparently similar disease. For instance, cells in a tissue sample differ by type, their stage in the cell growth cycle, and by their being embedded in a cellular environment. In tumor samples there are many reasons that the tumor cells in a given biopsy may individually differ in their genetic constitution such as in the presence and activity of tumor driver and circumstantial passenger genes. Biomarker signatures were developed, which in parallel sense the presence and expression levels of multiple molecular players, for example, those which by the current clinical science are considered as tumor drivers or as accessible drug targets, respectively. Clinical research in oncology had to run a long phase of validation to establish such diagnostic or prognostic biomarker panels by in vitro diagnostic multivariate index assays (IVDMIAs). Some of these multivariate index assays reached approval for clinical use (Table 1.7). Their main indication is for the prediction of risk for tumor progression or for response to the drugs in the therapeutic portfolio, respectively.

Table 1.7 Clinical practice for use of companion diagnostics together with biopharmaceuticals

Drug

Main indications

CDx

Approval status

Clinical relevance

Trastuzumab (Herceptin®)

Breast cancer, gastric cancer

IHC

PMA (FDA)

Patient stratification for inclusion into treatment

FISH

PMA (FDA)

CISH two-color CISH

Pertuzumab (Perjeta®)

Breast cancer

IHC

PMA (FDA)

Patient stratification for inclusion into treatment

Adotrastuzumab emtansine (Kadcyla®)

Gastric cancer

FISH

PMA (FDA)

Cetuximab (Erbitux®);

Colorectal cancer

IHC

PMA (FDA)

Patient stratification for cancer-associated antigen expression

Panitumumab (Vectibix®)

Head and neck cancer

qRT-PCR

PMA (FDA)

Patient selection for K-RAS wild-type expression

Excerpted from Ref. [40].

CDx – companion diagnostic; IHC – immunohistochemistry; FISH – fluorescence in-situ hybridization; CISH – chromogenic in-situ hybridization; qRT-PCR – quantative real time polymerase chain reaction.

Many more biomarker multivariate index assay sets are to follow. Research on such novel marker panels is ongoing worldwide. At present, a series of them are offered as laboratory-developed tests (LDTs). Establishing reliable algorithms for a final marker combination from a broader set of candidate markers needs dedicated heavy expert work by bioinformatics. The finally fixed combination shall be validated extensively in order to safeguard its discriminatory power and its reliability for risk prediction or therapy responsiveness, respectively. There are ongoing discussions in working groups of diagnostics providers with regulatory authorities on the necessary validation work on data for biomarker panels and suitable algorithmic calculations, which all shall be submitted for approval for clinical use [33, 44–46].

1.6.4 Regulatory Policies on Biomarker Tests

In the United States novel in vitro diagnostic tests (IVDs) fall into main categories as medical devices [33, 47]:

Diagnostic tests by

Class I or II,

which need a

510(k) premarketing clearance

by FDA. These are tests where an analogous test with FDA approval is already on the market. The submission contains data on the intended use and classification, data sets in comparison to the established test peer, and the analytical profile for validation: precision, linearity, specificity, and sensitivity in patient groups, compared to the established peer test.

Diagnostics test by

Class III,

which need a

Premarket Approval

(PMA) by FDA. These are tests that result in information with a high risk profile, such as in diagnostics or therapy for cancer or when the clinical use of the marker/technology is novel and no analogous test is available. Data such as in the 510(k) are needed; additionally, information must be submitted on clinical results, and on correlation of the test results with the disease stage and with the clinical information.

It is quite obvious that novel biomarker and multivariate index assays fall under the second category. Thus, their path to reaching approval needs extensive analytical and clinical contributions and adequate documentation. FDA-cleared tests exist for a limited number of markers, such as those listed in Tables 1.6–1.8.

Table 1.8 Multianalyte profiling for diagnostics in cancer patients

Product

Company

Profile

Clinical use

Status

MammaPrint®

Agendia

Microarray on 70 genes in a tumor sample

Prognosis in stage 1 and 2 LN

−

breast cancer

Approved in 2/2007 by FDA

Oncotype DX® Breast Cancer Assay

Genomic Health

21 gene expression test by qRT-PCR in tumor samples

Prediction of chemotherapy response in early stage LN-ER

+

breast cancer

Oncotype DX® assays for breast cancer resp. colon cancer are for laboratory-developed assay service conducted in the CLIA-licensed Genomic Health clinical laboratory

Oncotype DX® Colon Cancer Assay

Genomic Health

12 gene assay

Prediction of colon cancer recurrence in stage 2 patients by a recurrence score

OVA1®

Vermillion and Quest Diagnostics

5 protein assay on serum samples

Prediction of malignancy of ovarian cancer

Approved in 9/2009 by FDA

qRT-PCR – quantitative real time polymerase chain reaction.

However, most of the recently developed innovative molecular diagnostics on biomarkers or signatures have reached a status as LDTs, which presently are not overseen by the FDA. Laboratories performing LDTs fall under the rules of the Clinical Laboratory Improvement Amendment (CLIA) [47, 48]. Clinical laboratories obtain CLIA certifications from Centers for Medicare and Medicaid Services (CMS). FDA is currently considering its responsibility for clearing LDTs [49]. To date, the majority of molecular tests have not been submitted for FDA approval [33]. In 2013 a review reported that CLIA-certified laboratories developed LDTs that are used for over 2000 genetic tests [48]. As that review discusses, LDTs are developed quickly and fill a void where no FDA-approved test is available, but LDTs may lack adequate validation. Therefore, risk-aware validation of LDTs and proof for a reliable discriminatory power remain a constant challenge [2].

In Europe, diagnostics are regulated by rules for medical products under the EC Directive IVD 98/79/EC on in vitro diagnostic medical devices and its various updates [50]. This directive is continuously under revision for updating reasons and for risk-appropriateness of its regulations on medical devices. The EC directive regulates the approval of diagnostics for high-risk applications in a special appendix. Those diagnostics outside of that clearance provision for high-risk applications are subject to analytical quality control within the responsibility of the test-kit providing organization and are CE-certified under the auspices of a special external authorized body. In addition, the EMEA guideline on clinical evaluation of diagnostic agents is to be considered [51]. Regarding companion diagnostics, FDA, Health Canada, and the EMA reportedly intend to jointly clarify the regulatory pathway on which companion diagnostics shall enter the market [33].

1.7 Summary and Outlook

New high-throughput technologies for genome sequencing/whole genome analysis or proteomics, advances in cellular and tissue imaging, and new technologies for in vivo imaging will continuously drive the acquisition of pathophysiologically relevant data. This will result in a variety of unprecedented biomarkers as novel candidates for use in pharmacological research and clinical diagnosis. Those accumulating novel data sets are highly complex and interrelated. Their interpretation needs biomedical professionality and advanced analysis through the expertise of bioinformaticians [51]. Many exploratory biomarker data have still to reach a statistically validated significance. Moreover, novel candidate classes are worked on in clinical research, such as circulating DNA, various microRNAs, and circulating tumor cells. All of them will have to be submitted to a similar scrutiny for analytical and clinical validation.

Some significant advances have been achieved in the combination of selected biomarkers and biomarker signature with therapy selection for stratification in a patient population, especially in clinical areas of high medical need. Evidence for clinical utility in using novel biomarkers and companion diagnostics has been achieved in various indications [33, 52]. However, the highly dynamic progress in research on pathogenic molecules and regulatory mechanisms as disease drivers has outpaced the ability of the medical community and of regulatory authorities to understand and to implement appropriate reactions. The regulations for evaluation and approval by FDA or by the EU directive for in vitro diagnostic devices primarily target analytical accuracy and safety aspects of medical devices for biomarker use. Approval regulations for multivariate index tests on biomarker panels and understanding of their inherent algorithms for score/index calculation are still in an early stage. Translation of novel biomarkers to clinical practice needs more dedicated work to provide evidence of their clinical utility. Reimbursements for biomarker assays will follow sociomedical priorities and need thorough proof of an attractive benefit/cost ratio in healthcare.

References

1. Biomarkers Definitions Working Group (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework.

Clin. Pharmacol. Ther.

,

69

, 85–95.

2. Ziegler, A., Koch, A., Krockenberger, K., and Groshennig, A. (2012) Personalized medicine using DNA biomarkers: a review.

Hum. Genet.

,

131

, 1627–1638.

3. Bidard, F.-C.

et al.

(2014) Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data.

Lancet Oncol.

,

15

, 406–414.

4. Bonventre, J.V.

et al.

(2010) Next-generation biomarkers for detecting kidney toxicity.

Nat. Biotechnol.

,

28

, 436–440.

5. Sanoudou, D.

et al.

(2012) Array-based pharmacogenomics of molecular-targeted therapies in oncology.

Pharmacogenomics J.

,

12

, 185–196.

6. a Garrod, A.E. (1908) Croonian lecture II, alkaptonuria.

Lancet

,

2

, 73–79; b cited by Knox, W.E. (1958) Sir Archibald Garrod's inborn errors of metabolism. II. Alkaptonuria.

Am. J. Hum. Genet.

,

10

, 95–124.

7. Greiling, H. and Gressner, A.M. (eds) (Hrsg) (1995)

Lehrbuch der Klinischen Chemie und Pathobiochemie

, 3rd Aufl., Schattauer Publishers, Stuttgart, New York.

8. Bishop, M.L., Fody, E.P., and Schneff, L.E. (2010)

Clinical Chemistry. Techniques, Principles, Correlations

, 6th edn, Walter Kluver Lippicott Willens & Wilkens.

9. Burtis, C.A., Ashwood, E.R., and Bruns, D.E. (eds) (2011)

Tietz Textbook of Clinical Chemistry, and Molecular Diagnostics

, 6th edn, Saunders/Elsevier, St. Louis, MO.

10. Gressner, A.M. and Arndt, T. (Hrsg) (2013)

Lexikon der Medizinischen Laboratoriumsdiagnostik

, 2nd Aufl., Springer, Berlin, Heidelberg, New York.

11. (a) Kehl, D.W.

et al.

(2012) Biomarkers in acute myocardial injury.

Transl. Res.

,

159

, 252–264; (b) cited by Braun, S.L. (2013) Verbesserte Sensitivität und Spezifität.

Trillium

,

11

, 212–213.

12. McCord, J.

et al.

(2001) Ninety-minute exclusion of acute myocardial infarction by use of quantitative point-of-care testing of myoglobin and troponin I.

Circulation

,

104

, 1483–1488.

13. Wagener, C. and Neumaier, M. (1995) in

Lehrbuch der Klinischen Chemie und Pathobiochemie

, 3rd Aufl., (Hrsg H. Greiling and A.M. Gresssner), Schattauer, Stuttgart, New York, pp. 1192–1256.

14. Wagener, C. and Neumann, S. (eds) (1992)

Molecular Diagnostics of Cancer

, Springer, Berlin, Heidelberg, New York.

15. Collins, F.S.

et al.

(2003) A vision for the future of genomics research.

Nature

,

422

, 835–847.

16. EMBL-EBI

http://www.ensembl.org/index.html

(accessed 07 November 2014).

17. Genome

http://www.ncbi.nlm.nih.gov/genome

(accessed 07 November 2014).

18. CODIS

http:// www.fbi.gov./hq/codis/national.htm

(accessed 07 November 2014).

19. Thiemann, F., Cullen, P.M., and Klein, H.-C. (2006)

Leitfaden Molekulare Diagnostik

, Wiley-VCH Verlag GmbH, Weinheim.

20. Kim, J.

et al.

(2014) Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death.

Genome Med.

,

6

, 40, doi: 10.1186/gm560.

21. Hofree, M.

et al.

(2013) Network-based stratification of tumor mutations.

Nat. Methods

,

11

, 1106–1115.

22. The Cancer Genome Atlas Research Network Software (2014)

http://idekerlab.ucsd.edu/software/NBS/

.

23. KEGG

http://www.genome.jp/kegg

(accessed 07 November 2014).

24. Gene Ontology Consortium

http://www.geneontology.org

(accessed 07 November 2014).

25. STRING

http://www.string-db.org

(accessed 07 November 2014).

26. Kim, M.-S.

et al.

(2014) A draft map of the human proteome.

Nature

,

509

, 575–581.

27. Wilhelm, M.

et al.

(2014) Mass-spectrometry-based draft of the human proteome.

Nature

,

509

, 582–587.

28. Prentice, R.L.

et al.

(2013) Proteomic risk markers for coronary heart disease and stroke: validation and mediation of randomized trial hormone therapy effects in these diseases.

Genome Med.

,

5

, 112, doi: 10.1186/gm517.

29. Illig, T.

et al.

(2010) A genome-wide perspective of genetic variation in human metabolism.

Nat. Genet.

,

42

, 137–141.

30. Suhre, K.

et al.

(2011) Human metabolic individuality in biomedical and pharmaceutical research.

Nature

,

477

, 54–60.

31. Peterson, A.-K.

et al.