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E-grāmata: Comprehensive Biomarker Discovery and Validation for Clinical Application

Edited by (University of Groningen, Netherlands), Edited by (University of Groningen, Netherlands)
  • Formāts: 320 pages
  • Sērija : Drug Discovery Series Volume 33
  • Izdošanas datums: 17-Jun-2013
  • Izdevniecība: Royal Society of Chemistry
  • Valoda: eng
  • ISBN-13: 9781849734363
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Comprehensive Biomarker Discovery and Validation for Clinical ApplicationPalielināt
  • Formāts: 320 pages
  • Sērija : Drug Discovery Series Volume 33
  • Izdošanas datums: 17-Jun-2013
  • Izdevniecība: Royal Society of Chemistry
  • Valoda: eng
  • ISBN-13: 9781849734363
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Comprehensive Biomarker Discovery and Validation for Clinical Application provides the reader with an extensive introduction into all aspects of proteomics biomarker discovery, validation and development. It discusses the current status of science and technology, its limitations, bottlenecks as well as future development trends to improve the success rate of translating biomarker discovery into useful clinical tests. The most important feature of the book is to provide an overview of current technologies and the challenges encountered during biomarker discovery and validation, such as patient selection, sample handling, data processing, statistical analysis and registration and approval of validated biomarkers through European and US regulatory authorities. The authors introduce the reader to each of these topics in significant detail and provide examples or guidelines for best practice. There are prominent chapters included on biomarkers in translational and personalised medicine; an introduction to regulatory affairs and bring biomarkers to the market; biomarker discovery and the use of mass spectrometry based profiling platforms; MALDI imaging techniques in tissue-based biomarkers discovery and a clinical application study on the use of diagnostic assays for early diagnosis of heart failure using various proteomic methods. The book concludes with a final chapter on future trends in biomarker discovery and validation. The book targets a readership of industrial and academic researchers that are involved in biomarker discovery and validation or that manage biobanks, develop sample preparation methods, analytical profiling systems and bioinformatics tools. Common pitfalls and success stories in biomarker discovery are highlighted and guidelines for best practice are provided for the different parts of the procedure. The book will be an essential information resource for scientists working in the field.


This book covers proteomics biomarker discovery and validation procedures from the clinical perspective.
Introduction
Chapter 1 Introduction: Biomarkers in Translational and Personalized Medicine
3(37)
Chanchal Kumar
Alain J. van Gool
Summary
3(3)
1.1 Introduction 3
1.2 Biomarkers
6(1)
1.3 Biomarkers in Pharmaceutical Drug Development
7(7)
1.3.1 The Pharmaceutical Research and Development Process
7(3)
1.3.2 Biomarker-Based Decisions during Early-Phase Pharmaceutical Drug Development
10(3)
1.3.3 The Use of Biomarkers in Late-Phase Pharmaceutical Drug Development
13(1)
1.3.4 Fit-for-Purpose Biomarker Applications
14(1)
1.4 Biomarkers in Translational Medicine
14(3)
1.4.1 Translational Medicine
14(1)
1.4.2 Translational Medicine in Drug Discovery and Development
15(1)
1.4.3 Animal Models and Biomarkers in Translational Medicine
16(1)
1.5 Biomarkers in Personalized Medicine
17(8)
1.5.1 Personalized Medicine
17(2)
1.5.2 Impact of Personalized Medicine
19(2)
1.5.3 Molecular Profiling Toolbox and Personalized Medicine
21(3)
1.5.4 Biomarkers in Personalized Medicine
24(1)
1.6 Trends
25(3)
1.6.1 Importance of Biomarkers
25(1)
1.6.2 Globalization
26(1)
1.6.3 High-Content Biomarker Discovery
27(1)
1.6.4 Biomarker Combinations
27(1)
1.7 Challenges -
28(3)
1.7.1 Interpretation of High-Content Biomarker Discovery Technologies
28(1)
1.7.2 Biomarker Validation
28(1)
1.7.3 Robust Biomarker Tests
29(1)
1.7.4 Biospecimen
29(1)
1.7.5 Disconnected Biomarker Development Pipeline
30(1)
1.7.6 Translate Interindividual Findings in Personalized Biomarker Tests
30(1)
1.7.7 Cost Reimbursement
30(1)
1.7.8 Handling of Personalized Data
31(1)
1.8 Opportunities
31(9)
References
33(7)
Chapter 2 Introduction: Regulatory Development Hurdles for Biomarker Commercialization: The Steps Required to Get a Product to Market
40(33)
Melissa A. Thompson
2.1 Introduction
40(1)
2.2 Regulatory Commercialization Path Options
41(2)
2.3 Product Classification
43(12)
2.3.1 Research Use Only (RUO)
44(1)
2.3.2 Investigational Use Products (IUO)
44(1)
2.3.3 Laboratory Developed Tests (LDT)
44(3)
2.3.4 In Vitro Diagnostics (IVD)
47(1)
2.3.5 IVD Kit
48(1)
2.3.6 510(k) Clearance
48(2)
2.3.7 PMA
50(1)
2.3.8 CE Marking
51(3)
2.3.9 Companion Diagnostics
54(1)
2.4 Supporting Clinical Utility
55(1)
2.5 Infrastructure and Other Considerations for Commercialization
56(3)
2.5.1 Instrument Platform/Reagent Manufacturer Selection Challenges
56(1)
2.5.2 Software
57(2)
2.6 Quality Systems
59(4)
2.6.1 What is Involved?
60(1)
2.6.2 Elements of a Quality-Management System
61(1)
2.6.3 Quality System General Requirements
61(2)
2.7 Design Control
63(5)
2.7.1 Product Development Life-Cycle Process
64(2)
2.7.2 Who is Involved?
66(2)
2.7.3 Reimbursement
68(1)
2.8 Summary
68(5)
Resources
69(1)
Applicable Domestic and International Regulations
69(1)
List of Important Internet Resources
70(1)
Documents for EMEA Submission
70(3)
Chapter 3 Introduction: The Cardinal Role of Biobanks and Human Biospecimen Collections in Biomarker Validation: Issues Impeding Impact of Biomarker Research Outcomes
73(40)
Pascal Puchois
Lisa B. Miranda
Alain van Gool
3.1 Introduction
73(2)
3.2 Navigating the Biobanking-Biomarker Collaborative Landscape
75(13)
3.2.1 Crucial Considerations in Planning Biobank-Biomarker Research Collaborations
75(4)
3.2.2 Biobanking Challenges that Impede Impact of Biomarker Research Outcomes
79(6)
3.2.3 Effect of Process-Chain Impediments on Impact of Biospecimen Collection Quality
85(3)
3.3 Recommendations: Reducing Disparity of Impact and Lag in Outcomes of Biomarker Research
88(12)
3.3.1 Technical and Scientific Recommendations for Biomarker Scientists
88(5)
3.3.2 Technical and Scientific Recommendations for Biobankers
93(5)
3.3.3 Joint Recommendations for Biomarker Scientists and Biobankers
98(2)
3.4 Utilization of Biobank Samples for Biomarker Discovery and Development
100(13)
3.4.1 Biomarker Discovery
100(2)
3.4.2 Biomarker Development
102(1)
3.4.3 Biobanking-Biomarker Collaborations
103(2)
References
105(8)
Sample Preparation and Profiling
Chapter 4 Sample Preparation and Profiling: Biomarker Discovery in Body Fluids by Proteomics
113(23)
N. Govorukhina
R. Bischoff
4.1 Introduction
113(1)
4.2 Samples
114(14)
4.2.1 Serum
114(8)
4.2.2 Urine
122(1)
4.2.3 Epithelial Lining Fluid (ELF)
122(3)
4.2.4 Cerebrospinal Fluid (CSF)
125(3)
4.3 Conclusion
128(8)
Acknowledgements
129(1)
References
129(7)
Chapter 5 Sample Preparation and Profiling: Mass-Spectrometry-Based Profiling Strategies
136(26)
Yeoun Jin Kim
Bruno Domon
5.1 Introduction
136(2)
5.2 LC-MS-Based Proteomics Applied to Biomarker Discovery
138(7)
5.2.1 Sample Preparation: Prefractionation and Enrichment
138(5)
5.2.2 Peptide Separation by Liquid Chromatography
143(2)
5.3 MS-Based Discovery Platforms: Unsupervised Profiling
145(1)
5.3.1 Data-Dependent Acquisition (DDA)
145(1)
5.3.2 Data-Independent Acquisition (DIA)
146(1)
5.4 MS-Based Discovery Platforms: Supervised Profiling
146(9)
5.4.1 Surrogate Identification by Accurate Mass and Elution Time
147(1)
5.4.2 Selective Identification Using Inclusion List
148(3)
5.4.3 Hypothesis-Driven Discovery Proteomics
151(4)
5.5 Example: Directed Proteomics and Enrichment Strategies Applied to a Colon Cancer Stem-Cell Biomarker Study
155(1)
5.6 Perspectives
156(6)
Acknowledgement
157(1)
References
157(5)
Chapter 6 Sample Preparation and Profiling: Probing the Kinome for Biomarkers and Therapeutic Targets: Peptide Arrays for Global Phosphorylation-Mediated Signal Transduction
162(37)
Jason Kindrachuk
Scott Napper
6.1 Introduction
162(1)
6.2 Understanding Complex Biology through Phosphorylation-Mediated Signal Transduction
163(1)
6.3 Kinome vs. Phosphoproteome
164(4)
6.4 Peptide Arrays for Kinome Analysis
168(2)
6.4.1 Generation and Application of Peptide Arrays
169(1)
6.4.2 Peptide Arrays: Phosphoproteome or Kinome Analysis?
169(1)
6.5 Recent Advances
170(6)
6.5.1 Customized Arrays
170(6)
6.5.2 Technical Advances
176(1)
6.6 Understanding Biology
176(10)
6.6.1 Inflammation
177(1)
6.6.2 Infectious Diseases
177(3)
6.6.3 Stress
180(1)
6.6.4 Impact of Glucocorticoids on Insulin Signaling
181(1)
6.6.5 Lupus
181(1)
6.6.6 Angiotensin II-dependent Hypertensive Renal Damage
182(1)
6.6.7 Cancer
183(3)
6.7 Validation of Results
186(1)
6.8 Remaining Challenges
187(1)
6.8.1 Data Statistics and Mining
187(1)
6.9 Conclusions
188(11)
References
189(10)
Bioinformatics and Statistics
Chapter 7 Bioinformatics and Statistics: LC-MS(/MS) Data Preprocessing for Biomarker Discovery
199(27)
Peter Horvatovich
Frank Suits
Berend Hoekman
Rainer Bischoff
7.1 Introduction
199(4)
7.2 Quantitative Preprocessing Workflow for Single-Stage LC-MS(/MS) Data
203(6)
7.3 Example Workflow: The Threshold Avoiding Proteomics Pipeline
209(5)
7.4 Performance Assessment of Quantitative LC-MS Data Preprocessing Workflows
214(7)
7.5 Summary and Future Trends
221(5)
References
222(4)
Chapter 8 Bioinformatics and Statistics: Statistical Analysis and Validation
226(17)
Huub C. J. Hoefsloot
8.1 Introduction
226(2)
8.2 Terminology
228(1)
8.3 Validation Strategies
229(2)
8.3.1 Crossvalidation
229(1)
8.3.2 Model Assessment Using Random Data
230(1)
8.3.3 Multiple Testing Corrections
230(1)
8.3.4 Data Augmentation
231(1)
8.4 Types of Biomarkers and Biomarker Panels
231(1)
8.5 Discovery and Statistical Validation of The Biomarkers
232(4)
8.5.1 How to Find a Type I Biomarker
232(1)
8.5.2 Validation for Type I Biomarker
232(1)
8.5.3 How to Find a Type II Biomarker
233(1)
8.5.4 How to Find a Biomarker Panel
233(3)
8.6 Selection of the Right Classification Method
236(1)
8.7 How to Put the Pieces Together?
237(1)
8.8 Assessing the Quality of the Biomarker Panel
238(2)
8.9 What to Do if no Biomarker Panel is Found?
240(1)
8.10 Conclusions and Recommendations
240(3)
References
241(2)
Chapter 9 Bioinformatics and Statistics: Computational Discovery, Verification, and Validation of Functional Biomarkers
243(28)
Fan Zhang
Renee Drabier
9.1 What is a Biomarker?
243(1)
9.2 What is Biomarker Research?
244(1)
9.3 Biomarker Workflow Overview
244(2)
9.4 Types of Biomarkers in Clinical Application
246(1)
9.5 Computational Methods for Biomarker Discovery, Verification, and Validation
247(7)
9.5.1 Performance Measurements
247(1)
9.5.2 Receiver Operating Curve
247(1)
9.5.3 T-test
248(1)
9.5.4 Fisher's Exact Test
248(1)
9.5.5 Neural Network
249(3)
9.5.6 Bayesian Network
252(1)
9.5.7 Support Vector Machine
252(1)
9.5.8 Crossvalidation
253(1)
9.6 System Biology Approaches for Biomarker Discovery, Verification, and Validation
254(6)
9.6.1 Literature Search
254(1)
9.6.2 Crossvalidation of Multiple Studies
255(1)
9.6.3 Pathway Analysis
255(2)
9.6.4 Interassociation Analysis
257(3)
9.7 Case Study: Breast Cancer Plasma Protein Biomarker Discovery and Verification by Coupling LC-MS/MS Proteomics and Systems Biology
260(4)
9.7.1 Study Design
261(1)
9.7.2 Biomarker's Statistical Discovery
261(1)
9.7.3 Literature Search
262(1)
9.7.4 Pathway Analysis and Gene Ontology Categorization of Significant Proteins
262(2)
9.7.5 Crossvalidation of Multiple Studies
264(1)
9.8 Conclusion
264(7)
Acknowledgements
265(1)
References
265(6)
Discovery and Validation Case Studies, Recommendations
Chapter 10 Discovery and Validation Case Studies, Recommendations: A Pipeline that Integrates the Discovery and Verification Studies of Urinary Protein Biomarkers Reveals Candidate Markers for Bladder Cancer
271(44)
Yi-Ting Chen
Carol E. Parker
Hsiao-Wei Chen
Chien-Lun Chen
Dominik Domanski
Derek S. Smith
Chih-Ching Wu
Ting Chung
Kung-Hao Liang
Min-Chi Chen
Yu-Sun Chang
Christoph H. Borchers
Jau-Song Yu
10.1 Introduction
272(6)
10.1.1 Importance of Bladder Cancer
272(1)
10.1.2 Other Methods of Diagnosis
272(1)
10.1.3 Other Urinary Biomarker Studies for Bladder Cancer
273(1)
10.1.4 Protein-Based Biomarker Discovery
274(1)
10.1.5 Introduction tp iTRAQ for Biomarker Discovery
275(1)
10.1.6 Introduction to MRM for Biomarker Discovery and Verification
276(1)
10.1.7 Biomarker Discovery Pipeline
277(1)
10.2 Experimental
278(8)
10.2.1 Materials
278(2)
10.2.2 Sample Preparation for iTRAQ and MRM-MS
280(1)
10.2.3 LC-ESI MS/MS Analysis of iTRAQ-Labeled Peptides by LTQ-Orbitrap Pulsed-Q Dissociation
281(1)
10.2.4 AB 4000 Qtrap Mass Spectrometry for MRM-MS Samples
282(2)
10.2.5 Verification of MRM-MS Results
284(1)
10.2.6 Statistical Analysis
285(1)
10.2.7 Functional Annotation and Network Analysis of Differential Proteins
286(1)
10.3 Results and Discussion
286(21)
10.3.1 iTRAQ for Candidate Selection
286(1)
10.3.2 MetaCore™ Analysis of Biological Networks Associated with Differentially Expressed Proteins in Urine
287(3)
10.3.3 Verification of iTRAQ-Discovered Biomarkers in a Larger Number of Individual Samples - Using Western Blot Analyses
290(2)
10.3.4 Verification of Additional Biomarkers in a Larger Number of Individual Urine Samples Using MRM-MS
292(15)
10.3.5 Comparison of iTRAQ and MRM Results and the Biomarker Discovery Pipeline
307(1)
10.4 Conclusions
307(8)
Acknowledgements
308(1)
References
308(7)
Chapter 11 Discovery and Validation Case Studies, Recommendations: Discovery and Development of Multimarker Panels for Improved Prediction of Near-Term Myocardial Infarction
315(19)
Peter Juhasz
Moira Lynch
Manuel Paniagua
Jennifer Campbell
Aram Adourian
Yu Guo
Xiaohong Li
Borge G. Nordestgaard
Neal F. Gordon
11.1 Introduction
315(2)
11.2 Methods
317(5)
11.2.1 Study Design
317(1)
11.2.2 Discovery Proteomics - 8-plex iTRAQ 2D-LC MS/MS
318(1)
11.2.3 Multiplex Immunoassays (Luminex 200)
319(1)
11.2.4 Multiple-Reaction Monitoring (MRM) Analysis
319(2)
11.2.5 Data Analysis
321(1)
11.3 Results
322(3)
11.4 Discussion
325(3)
11.5 Performance of Multimarker Panels
328(2)
11.6 Prepare FDA Filing for Multimarker Panel
330(4)
References
331(3)
Chapter 12 Discovery and Validation Case Studies, Recommendations: Bottlenecks in Biomarker Discovery and Validation by Using Proteomic Technologies
334(15)
Maria P. Pavlou
Ivan M. Blasutig
Eleftherios P. Diamandis
12.1 Introduction
334(1)
12.2 Considerations during Biomarker Development
335(13)
12.2.1 Preanalytical Considerations
336(3)
12.2.2 Study Design
339(2)
12.2.3 Analytical Considerations
341(4)
12.2.4 Statistical Analysis
345(2)
12.2.5 Clinical Validation
347(1)
12.2.6 Financial
348(1)
12.3 Concluding Remarks
348(1)
References 349(4)
Subject Index 353
Peter Horvatovich and Rainer Bischoff have worked at the University of Groningen Faculty of Mathematics and Natural Science for more than five years. Rainer Bischoff is professor in analytical biochemistry and has been studying protein analysis and proteomics for over 20 years. He obtained his PhD at the University of G÷ttingen before undertaking postdoctoral research at Purdue University in the USA. He also worked as a group and project leader at Transgene S. A. in Strasburg and a section manager at AstraZeneca R&D in Lund. Peter Horvatovich is an Assistant Professor. He has studied proteomics related bioinformatics for more than eight years and has an analytical chemistry background. Dr Horvatovich received his PhD at the University of Strasbourg for work related to the detection of irradiated food. He then worked at Sanofi-Synthelabo in Budapest and as a postdoctoral researcher at the Bundesinstitute für Risikobewertung in Berlin before moving to the University of Groningen.
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