Proteomic and Metabolomic Approaches to Biomarker Discovery

Haleem J. Issaq

• Publisher: Academic Press
• Year: 2013
• Print ISBN: 9780123944467
• eBook ISBN: 9780123947956
• Topic: Biological Sciences
• Subtopic: Biochemistry
• Index: Biological Sciences

Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes.Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution. The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis and modeling. This new standard effectively eliminates the differing methodologies used in studies and creates a unified approach. Readers will learn the advantages and disadvantages of the various techniques discussed, as well as potential difficulties inherent to all steps in the biomarker discovery process.A vital resource for biochemists, biologists, analytical chemists, bioanalytical chemists, clinical and medical technicians, researchers in pharmaceuticals, and graduate students, Proteomic and Metabolomic Approaches to Biomarker Discovery provides the information needed to reduce clinical error in the execution of research.- Describes the use of biomarkers to reduce clinical errors in research- Includes techniques from a range of biomarker discoveries- Covers all steps involved in biomarker discovery, from study design to study execution

Chapter 1

Biomarker Discovery

Study Design and Execution

Haleem J. Issaq and Timothy D. Veenstra, Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

Outline

Abbreviations

Introduction

Definitions

Biomarker

Sensitivity

Specificity

Positive Predictive Value (PPV)

Negative Predictive Value (NPV)

Proteomics

Profiling

Metabolomics

The Current State of Biomarker Discovery

Study Design and Execution

Study Design

Study Execution

Personnel and Instrumentation

Errors in Study Design

The Sample

Cancer Type and Stage

Sample Type

Selection of Patients and Controls

Number of Samples

Ethnicity, Sex, and Age

Sample Collection, Handling, and Storage

Method of Sample Analysis

Errors in Study Execution

Sample Preparation

Methods of Analysis

Number of Replicates

Effect of Mass Spectrometer Type on the Results

Effect of Separation Instrumentation on the Results

Errors in Measurements

Personnel and Experimental Validation

Specificity of Proteins as Biomarkers

Published Results Comparison

Statistical Data Analysis

Recommendations

Concluding Remarks and Recommendations

Acknowledgments

References

Abbreviations

LC liquid chromatography

HPLC high performance LC

UPLC ultra-high-pressure LC

CE capillary electrophoresis

GC gas chromatography

MS mass spectrometry

SDS-PAGE sodium dodecyl sulfate-polyacryl gel electrophoresis

Acknowledgments

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the United States Government.

Introduction

Diseases result in specific changes in the molecular profiles of biological fluids and tissue. These changes can be detected by analyzing the genetic, proteomic, or metabolomic composition of samples. Proteomic and metabolomic analysis provides the opportunity to detect diseases as they occur; genetic analyses identify individuals with predispositions to certain diseases and aid in the determination of long-term risk. Therefore, direct measurement of genes, proteins, and metabolites is essential for the understanding of biological processes in disease and normal states.¹ Molecules produced by the body’s metabolic processes may distinguish between two different sample sets obtained from, for example, cancer and non-cancer-bearing individuals. These distinguishing compounds are known as biomarkers. Because the majority of published studies deal with the discovery of cancer biomarkers, the discussions in this chapter are limited to cancer biomarker discovery. Many of the methods described, however, are also applicable to other diseases.

A biomarker is a substance that is overexpressed in biological fluids or tissues in patients with a certain disease. A biomarker can include patterns of single-nucleotide polymorphisms (SNPs), DNA methylation, or changes in mRNA, protein, or metabolite abundances. The important point is that these patterns correlate with the characteristics of the disease.² Biomarkers are used to examine the biological behavior of a disease and predict the clinical outcome. The biomarker should be disease specific and not due to environmental conditions or biological perturbations. To be clinically acceptable, a diagnostic biomarker should possess a sensitivity and specificity as close to 100% as possible and be measured within a noninvasive (urine) or semi-invasive (blood) collected specimen. In addition, the test should be accurate, economical, easy to perform, and reproducible by different technicians across different laboratories. A description of an ideal description of diagnostic methods is provided in Figure 1. Although some biomarkers have been approved by the Food and Drug Administration (FDA) as qualitative tests for monitoring specific cancers (e.g., nuclear matrix protein-22 for bladder cancer), the majority of discovered potential biomarkers (proteins or metabolites) are not sensitive and/or specific enough to be used for population screening.

FIGURE 1 Description of ideal methods for disease diagnosis.

Definitions

Biomarker

A biomarker is an objectively measured molecule substance that indicates the presence of an abnormal condition within a patient. A biomarker can be a gene (e.g., SNP), protein (e.g., prostate-specific antigen), or metabolite (e.g., glucose, cholesterol, etc.) that has been shown to correlate with the characteristics of a specific disease.² A biomarker in clinical and medical settings can be used for many purposes, including early disease detection, monitoring response to therapy, and predicting clinical outcome. Biomarkers can be categorized according to their clinical applications. In cancer, diagnostic markers are used to initially define the histopathological classification and stage of the disease, and prognostic markers can predict the development of disease and the prospect of recovery. Based upon the individual cases, the predictive markers can be used for the selection of the correct therapeutic procedure. The potential biomarker should be confirmed that it is indeed specific to the disease state and is not a function of the variability within the biological sample of patients due to differences in diet, genetic background, lifestyle, age, sex, ethnicity, and so on.

Sensitivity

Sensitivity of a test or marker is defined as the percentage of positive samples identified by a model as true positive. The false negative rate is the percent of patients with the disease for whom the test is negative.

Specificity

Specificity is defined as the percentage of negative samples (individuals without the disease) identified by a model as true negative. False positive is the number of individuals without the disease in whom the test is positive.

Positive Predictive Value (PPV)

Positive predictive value (PPV) is defined as the percent of individuals in whom the test is positive and the disease is present.

Negative Predictive Value (NPV)

Negative predictive value (NPV) is defined as the percent of individuals in whom the test is negative and the disease is not present.

Proteomics

Proteomics is the study of all proteins in a biological sample. The complexity and dynamic concentration range of the proteins, along with the dynamic nature of the proteins that constitute the proteome, makes the detection and quantitation of each protein virtually impossible. In general, most biomarker discovery studies aim to characterize as many proteins as possible.

Profiling

Profiling is the detection of panels of biomarkers (proteins or metabolites) that may provide higher sensitivities and specificities for disease diagnosis than is afforded with a single marker. Proteomic and metabolomics pattern analysis relies on comparison of differences in relative abundance of a number of polypeptides/proteins and metabolites (mass-to-charge ratio [m/z] and intensity) within the mass spectrum or the nuclear magnetic resonance (NMR) spectrum of two sample sets.

Metabolomics

Metabolomics, also known as metabonomics, is the study of a complete set of small molecules (less than 1,500 Daltons [Da]) found within a biological system for the understanding of biological processes in normal and disease states. Direct quantitative measurements of metabolite expressions in urine, serum, plasma, and tissue are essential but extremely difficult due to the complexity and concentration dynamic range of the metabolites in a biological sample. The difference between metabolomics and metabonomics is that metabolomics is the qualitative and quantitative measurement of all metabolites in a system, and metabonomics is the comparison of metabolite levels (profiles) found in two different samples: healthy and diseased.

The Current State of Biomarker Discovery

Examination of the scientific and medical literature clearly indicates that most protein and metabolite biomarkers presently in use are inadequate to replace an existing clinical test, or their only utility is for detecting advanced stage cancers, for which the survival rate is low. Many molecular biomarkers have been suggested for the detection of cancer and other diseases; however, none possess the required sensitivity and specificity. The state of biomarker research may be illustrated by using bladder cancer biomarkers as an example. Bladder cancer is selected because of its recurring nature and the three- to six-month monitoring requirements, making it a very expensive disease to treat. It is disheartening that a lot of effort and funds have been spent on finding a biomarker for bladder cancer without resulting in an acceptable test to replace cystoscopy, voided urine cytology, and imaging studies—the current standards of care for the detection and monitoring of bladder tumors. A literature search indicates the presence of many molecular biomarkers for bladder cancer³; however, none of the molecular markers have proven to be sensitive and specific enough to replace cystoscopy.⁴ Another reason why most published proteomic and metabolomic studies have not provided results that have progressed from the laboratory to the clinic is that the majority of studies stopped at the discovery phase and never progressed onto the necessary verification or validation phases.

The following biomarkers have been approved by the U.S. FDA as qualitative tests for bladder cancer: nuclear matrix protein (NMP22) with 56% sensitivity; bladder tumor antigen (BTAstat) with 58% sensitivity; and UroVysion with 36% to 65% sensitivity,⁵ and hyaluronic acid and hyaluronidase measurements have a sensitiv...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• List of Contributors
• Chapter 1. Biomarker Discovery: Study Design and Execution
• Chapter 2. Proteomic and Mass Spectrometry Technologies for Biomarker Discovery
• Chapter 3. Tissue Sample Preparation for Proteomic Analysis
• Chapter 4. Sample Preparation in Global Metabolomics of Biological Fluids and Tissues
• Chapter 5. Serum and Plasma Collection: Preanalytical Variables and Standard Operating Procedures in Biomarker Research
• Chapter 6. Current NMR Strategies for Biomarker Discovery
• Chapter 7. Using Data-Independent Mass Spectrometry to Extend Detectable Dynamic Range without Prior Fractionation
• Chapter 8. Gas Chromatography/Mass Spectrometry-Based Metabonomics
• Chapter 9. Liquid Chromatographic Methods Combined with Mass Spectrometry in Metabolomics
• Chapter 10. Capillary Electrophoresis–Mass Spectrometry for Proteomic and Metabolic Analysis
• Chapter 11. Current Gel Electrophoresis Approaches to Low-Abundance Protein Marker Discovery
• Chapter 12. Two-Dimensional Difference in Gel Electrophoresis for Biomarker Discovery
• Chapter 13. Affinity Targeting Schemes for Biomarker Research
• Chapter 14. Asp-Selective Microwave-Supported Acid Proteolysis
• Chapter 15. Sample Depletion, Fractionation, and Enrichment for Biomarker Discovery
• Chapter 16. Protein and Metabolite Identification
• Chapter 17. Quantitative Proteomics in Development of Disease Protein Biomarkers
• Chapter 18. Mass Spectrometry and NMR Spectroscopy–Based Quantitative Metabolomics
• Chapter 19. Multivariate Analysis for Metabolomics and Proteomics Data
• Chapter 20. Top-Down Mass Spectrometry for Protein Molecular Diagnostics and Biomarker Discovery
• Chapter 21. A Role for Protein–Protein Interaction Networks in the Identification and Characterization of Potential Biomarkers
• Chapter 22. Reverse Phase Protein Microarray Technology: Advances into the Clinical Research Arena
• Chapter 23. Autoantibodies and Biomarker Discovery
• Chapter 24. MicroRNAs and Biomarker Discovery
• Chapter 25. Imaging Mass Spectrometry of Intact Biomolecules in Tissue Sections
• Chapter 26. Mass Spectrometry–Based Approach for Protein Biomarker Verification
• Chapter 27. Mass Spectrometry Metabolomic Data Handling for Biomarker Discovery
• Chapter 28. Analytical Methods and Biomarker Validation
• Index