Proteomic and Metabolomic Approaches to Biomarker Discovery

Haleem J. Issaq, Timothy D. Veenstra

• Publisher: Academic Press
• Year: 2019
• eBook ISBN: 9780128197882
• Edition: 2
• Topic: Biological Sciences
• Subtopic: Biochemistry
• Index: Biological Sciences

Proteomic and Metabolomic Approaches to Biomarker Discovery, Second Edition 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.This second edition has been fully updated and revised to address recent advances in MS and NMR instrumentation, high-field NMR, proteomics and metabolomics for biomarker validation, clinical assays of biomarkers and clinical MS and NMR, identifying microRNAs and autoantibodies as biomarkers, MRM-MS assay development, top-down MS, glycosylation-based serum biomarkers, cell surface proteins in biomarker discovery, lipodomics for cancer biomarker discovery, and strategies to design studies to identify predictive biomarkers in cancer research.- Addresses the full range of proteomic and metabolomic methods and technologies used for biomarker discovery and validation- Covers all steps involved in biomarker discovery, from study design to study execution- Serves as a vital resource for biochemists, biologists, analytical chemists, bioanalytical chemists, clinical and medical technicians, researchers in pharmaceuticals and graduate students

Chapter 1

Biomarker discovery: Study design and execution

Haleem J. Issaq^a; Timothy D. Veenstra^{b a} Cancer Research Technology Program, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Frederick, MD, United States
^b Department of Applied Sciences, Maranatha Baptist University, Watertown, WI, United States

Abstract

The results of a disease are specific changes in the profiles (chemical and biochemical) of biological fluids and tissues before the development of clinical symptoms. Proteomic and metabolomics analysis of biological samples can reveal changes in abundance levels of proteins and metabolites that can function as useful diagnostic and prognostic clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated. Confirmation and validation applies to the analytical methodology and the candidate biomarker. A search of the scientific and medical literature indicates that many studies reported the discovery of potential biomarkers without proper validation. In this chapter, the discussion will center on biomarker study design and execution and will point out the needed steps for a successful biomarker discovery.

Keywords

Metabolomics; Proteomics, biomarkers; Mass spectrometry; Nuclear magnetic resonance spectroscopy; Chromatography; Electrophoresis

Outline

• Introduction
• Definitions
  • Biomarker
  • Sensitivity
  • Specificity
  • Positive predictive value (PPV)
  • Negative predictive value (NPV)
  • Proteomics
  • Metabolomics
  • Profiling
• The current state of biomarker discovery
• Study design and execution
  • Study design
  • Study execution
  • Personnel and instrumentation
• Errors in study design
  • The sample
• 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

Acknowledgments

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contracts 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 profiles (chemical and biochemical) of biological fluids and tissue. These changes can be detected by analyzing the samples for genes, proteins, and small molecules (metabolites). Proteomic and metabolomic analysis provides the opportunity to detect diseases as they occur while genetic analyses will identify individuals with predispositions to certain diseases and will determine long-term risk. Therefore, direct measurement of genes, metabolites, and protein expressions is essential for the understanding of biological processes in disease and normal states.¹ Molecules produced by the body's metabolic processes may be able to help distinguish between two different sample sets obtained from, for example, cancer and noncancer-bearing individuals. The distinguishing compounds are known as biomarkers. Since the majority of published studies deal with cancer biomarkers discovery, in this chapter the discussion will be limited to cancer biomarker discovery, which are 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, provided that these patterns can be shown to 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 the result of the disease and not due to environmental conditions or biological perturbations. To be clinically acceptable, a diagnostic biomarker should have a sensitivity and specificity of 100%, and be measured within a noninvasive (urine) or semiinvasive (blood) collected specimen. In addition the test should be accurate, economical, easy to perform, and reproducible by different technicians across different laboratories. Fig. 1 is a description of an ideal description of diagnostic methods. Although some biomarkers have been approved by the Food and Drug Administration as qualitative tests for monitoring specific cancers (e.g., nuclear matrix protein-22 for bladder cancer), unfortunately the majority of discovered potential biomarkers (proteins or metabolites) are not sensitive and/or specific enough to be used for population screening. However, the search for disease biomarkers remains an active area of research, e.g., a search for cancer biomarkers using PubMed search engine resulted in 21,833 hits for 2017 and 14,451 hits for 2018. Also, the search for biomarkers is not limited to cancer but almost to every medical condition known to man.

Fig. 1 Description of ideal methods for disease diagnosis.

Definitions

Biomarker

A biomarker is a substance that is objectively measured that indicates the presence of an abnormal condition within a patient. A biomarker can be gene (e.g., SNP), protein (e.g., prostate-specific antigen (PSA)), or metabolite-based (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 is used for: early disease detection, monitoring response to therapy, and predicting the clinical outcome. They can be categorized according to their clinical applications. Diagnostic markers are used to initially define the histopathological classification and stage of the disease, while 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 simply a function of the variability within the biological sample of patients due to differences in diet, genetic background, lifestyle, age, sex, ethnicity, etc. In summary, a biomarker is an agent that can predict the response to therapy, predicting prognosis, monitoring for disease recurrence and assessing response to therapy, in addition to screening for cancer.

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 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)

PPV is defined as the percent of individuals in whom the test is positive and the disease is present.

Negative predictive value (NPV)

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 that comprise the proteome makes the detection and quantitation of each protein extremely challenging if not impossible.

Metabolomics

Metabolomics also known as metabonomics is the study of complete set of small molecules (less than 1500 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 metabolit...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface to the second edition
• 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: Sample depletion, fractionation, and enrichment for biomarker discovery
• Chapter 7: Current NMR strategies for biomarker discovery
• 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: Associating 2-DE and CPLLs for low-abundance protein discovery: A winning strategy
• Chapter 12: Two-dimensional difference in gel electrophoresis for biomarker discovery
• Chapter 13: Affinity-targeting schemes for protein biomarkers
• Chapter 14: Protein and metabolite identification
• Chapter 15: Quantitative proteomics in development of disease protein biomarkers
• Chapter 16: Mass spectrometry and NMR spectroscopy based quantitative metabolomics
• Chapter 17: Top-down mass spectrometry for protein molecular diagnostics, structure analysis, and biomarker discovery
• Chapter 18: Using data-independent mass spectrometry to extend detectable dynamic range without prior fractionation
• Chapter 19: Imaging mass spectrometry of intact biomolecules in tissue sections
• Chapter 20: Mass spectrometry-based approach for protein biomarker verification
• Chapter 21: Mass spectrometry metabolomic data handling for biomarker discovery
• Chapter 22: Analytical methods and biomarker validation
• Chapter 23: Multivariate analysis for metabolomics and proteomics data
• Chapter 24: Cell surface protein enrichment for biomarker and drug target discovery using mass spectrometry-based proteomics
• Chapter 25: Advances in lipidomics for cancer biomarker discovery
• Chapter 26: Mass spectrometry for the identification of protein biomarkers in urinary extracellular vesicles
• Chapter 27: Designing clinical studies for biomarker discovery: The Design criteria
• Index