An Omics Perspective on Cancer Research

1. Auflage 2010
ISBN: 978-90-481-2675-0
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 269 Seiten

Omics is an emerging and exciting area in the field of science and medicine. Numerous promising developments have been elucidated using omics (including genomics, transcriptomics, epigenomics, proteomics, metabolomics, interactomics, cytomics and bioinformatics) in cancer research. The development of high-throughput technologies that permit the solution of deciphering cancer from higher dimensionality will provide a knowledge base which changes the face of cancer understanding and therapeutics.

This is the first book to provide such a comprehensive coverage of a rapidly evolving area written by leading experts in the field of omics. It complies and details cutting-edge cancer research that covers the broad advances in the field and its application from cancer-associated gene discovery to drug target validation. It also highlights the potential of using integration approach for cancer research.

This unique and timely book provides a thorough overview of developing omics, which will appeal to anyone involved in cancer research. It will be a useful reference book for graduate students of different subjects (medicine, biology, engineering, etc) and senior scientists interested in the fascinating area of advanced technologies in cancer research.

Readership: This is a precious book for all types of readers – cancer researchers, oncologists, pathologists, biologists, clinical chemists, pharmacologists, pharmaceutical specialists, biostatisticians, and bioinformaticists who want to expand their knowledge in cancer research.

Autoren/Hrsg.

Inhaltsverzeichnis

1;Cho_FM1_O.pdf;2
1.1;An Omics Perspective on Cancer Research;2
1.1.1;Preface;5
2;Cho_Ch01_O.pdf;8
2.1;Chapter 1;8
2.1.1;Omics Approaches in Cancer Research;8
2.1.1.1;1.1 .Introduction;9
2.1.1.2;1.2 .Genomics;9
2.1.1.3;1.3 .Epigenomics;10
2.1.1.4;1.4 .Transcriptomics;10
2.1.1.5;1.5 .Proteomics;11
2.1.1.6;1.6 .Metabolomics;11
2.1.1.7;1.7 .Interactomics;12
2.1.1.8;1.8 .Cytomics;13
2.1.1.9;1.9 .Phenomics;13
2.1.1.10;1.10 .Bioinformatics;13
2.1.1.11;1.11 .From Omics to Personalized Medicine;14
2.1.1.12;1.12 .Challenges and Prospective;14
2.1.2;References;15
3;Cho_Ch02_O.pdf;17
3.1;Chapter 2;17
3.1.1;Recent Advances in Cancer Genomics and Cancer-Associated Genes Discovery;17
3.1.1.1;2.1 .Introduction;17
3.1.1.2;2.2 .Array-Based Technologies;18
3.1.1.2.1;2.2.1 .Array Comparative Genomic Hybridization (aCGH);18
3.1.1.2.2;2.2.2 .Representational Oligonucleotide Microarray Analysis;21
3.1.1.2.3;2.2.3 .SNP Arrays;21
3.1.1.3;2.3 .Sequencing-Based Technologies;22
3.1.1.3.1;2.3.1 .Mutational Analyses of Cancer Genome;22
3.1.1.3.2;2.3.2 .Digital Karyotyping;24
3.1.1.3.3;2.3.3 .Genomic DNA End-Sequencing: BAC, Fosmid and Paired-End;25
3.1.1.3.4;2.3.4 .Paired-End diTags (PETs) cDNA Sequencing;26
3.1.1.4;2.4 .Identification of Cancer-Associated Genes;27
3.1.1.5;2.5 .Strategy to Prioritize Candidate Genes for Validation and Functional Characterization;28
3.1.1.6;2.6 .Cancer Genomics from Bench to Bedside;30
3.1.1.7;2.7 .Concluding Remarks;32
3.1.2;References;32
4;Cho_Ch03_O.pdf;36
4.1;Chapter 3;36
4.1.1;An Integrated Oncogenomic Approach: From Genes to Pathway Analyses;36
4.1.1.1;3.1 .Introduction to Gene Expression Profiling;36
4.1.1.2;3.2 .Identification of Discriminative Genes;38
4.1.1.3;3.3 .Dissecting the Components of a Gene Expression Profile;41
4.1.1.3.1;3.3.1 .Effects of Genomic Structure;42
4.1.1.3.2;3.3.2 .Effects of Cellular Pathways;44
4.1.1.3.3;3.3.3 .Pathway Analysis Methodology;46
4.1.1.4;3.4 .Integration of Gene Expression Components for Discovery;48
4.1.1.5;3.5 .The Evolution of Pathway Analysis;49
4.1.2;References;51
5;Cho_Ch04_O.pdf;56
5.1;Chapter 4;56
5.1.1;The Epigenomics of Cancer;56
5.1.1.1;4.1 .Introduction;56
5.1.1.2;4.2 .Emerging Technologies for Genome-Wide Profiling of DNA Methylation;57
5.1.1.3;4.3 .Methylation-Sensitive Restriction Enzyme-Based Method 1 – RLGS;58
5.1.1.4;4.4 .Methylation-Sensitive Restriction Enzyme-Based Method 2 – MS-RDA and MCA-RDA;60
5.1.1.5;4.5 .Methylation-Sensitive Restriction Enzyme-Based Method 3 – MIAMI;60
5.1.1.6;4.6 .DNA Immunoprecipitation (MeDIP)-Based Method – MeDIP-Chip;63
5.1.1.7;4.7 .Bisulfite-Based Methods;63
5.1.1.8;4.8 .Comparison of the Methods;66
5.1.1.9;4.9 .Histone Modifications of Cancer;67
5.1.1.10;4.10 .Technologies for Genome-Wide Profiling of Histone Modifications;69
5.1.1.11;4.11 .Epigenetic Therapy of Cancer;69
5.1.1.12;4.12 .Perspectives and Conclusion;70
5.1.2;References;70
6;Cho_Ch05_O.pdf;73
6.1;Chapter 5;73
6.1.1;Involvement of MicroRNAs in Human Cancer: Discovery and Expression Profiling;73
6.1.1.1;5.1 .Introduction to MicroRNAs;73
6.1.1.2;5.2 .The Discovery of the Involvement of MicroRNAs in Human Cancer;75
6.1.1.3;5.3 .Numerous MicroRNAs Are Involved in Human Cancer;76
6.1.1.4;5.4 .MicroRNAs Are Central Players in Malignant Transformation Processes;85
6.1.1.5;5.5 .miRNA Expression Signatures for Cancer Classification, Prognostic Stratification and Therapy Response;96
6.1.1.6;5.6 .miRNAs in Anti-cancer Therapy;97
6.1.1.7;5.7 .Concluding Remarks;100
6.1.2;References;100
7;Cho_Ch06_O.pdf;109
7.1;Chapter 6;109
7.1.1;Functional Proteomics in Oncology: A Focus on Antibody Array-Based Technologies;109
7.1.1.1;6.1 .Functional Proteomics in Oncology: Concepts;109
7.1.1.2;6.2 .Antibody Array-Based Techniques in the Context of Other Functional Proteomic Approaches;111
7.1.1.3;6.3 .Antibody Array Formats;112
7.1.1.3.1;6.3.1 .Current Formats;112
7.1.1.3.2;6.3.2 .Emerging Formats for Functional Proteomics;115
7.1.1.4;6.4 .Strategies and Applications of Antibody Arrays for Functional Proteomics;116
7.1.1.4.1;6.4.1 .Cell Culture;116
7.1.1.4.2;6.4.2 .Tissue Specimens;119
7.1.1.4.3;6.4.3 .Body Fluids;121
7.1.1.5;6.5 .Conclusions;123
7.1.2;References;125
8;Cho_Ch07_O.pdf;128
8.1;Chapter 7;128
8.1.1;Protein Graphs in Cancer Prediction;128
8.1.1.1;7.1 .Introduction;128
8.1.1.2;7.2 .Materials and Methods;132
8.1.1.2.1;7.2.1 .Protein Database;132
8.1.1.2.2;7.2.2 .MS Database;133
8.1.1.2.3;7.2.3 .MS Data Coding;133
8.1.1.2.4;7.2.4 .MARCH-INSIDE Software;134
8.1.1.2.5;7.2.5 .Lattice Network Representations;134
8.1.1.2.6;7.2.6 .MS Star Graph Representation;135
8.1.1.2.7;7.2.7 .Protein Star Graph Representation;136
8.1.1.2.8;7.2.8 .Entropy Measurements;136
8.1.1.2.9;7.2.9 .Linear Discriminant Analysis (LDA);137
8.1.1.3;7.3 .Results and Discussion;137
8.1.1.3.1;7.3.1 .Classification Function;137
8.1.1.4;7.4 .Conclusions;140
8.1.2;References;140
9;Cho_Ch08_O.pdf;144
9.1;Chapter 8;144
9.1.1;The Use of Metabolomics in Cancer Research;144
9.1.1.1;8.1 .Introduction;145
9.1.1.1.1;8.1.1 .General;145
9.1.1.1.2;8.1.2 .Metabolite Profiling of Liver Tumor Promoters;145
9.1.1.2;8.2 .Methods;146
9.1.1.2.1;8.2.1 .Metabolite Profiling – General;146
9.1.1.2.2;8.2.2 .NMR Metabonomics;147
9.1.1.2.3;8.2.3 .GC-MS/LC-MS Metabolomics;147
9.1.1.3;8.3 .Metabolite Profiling and Cancer;148
9.1.1.3.1;8.3.1 .Challenges;150
9.1.1.4;8.4 .Metabolite Profiles;152
9.1.1.4.1;8.4.1 .Control Animals;152
9.1.1.4.1.1;8.4.1.1 .NMR Metabonomics;152
9.1.1.4.1.2;8.4.1.2 .GC-MS/LC-MS Metabolomics;152
9.1.1.4.2;8.4.2 .Studies with Liver Enzyme Inducers;154
9.1.1.4.2.1;8.4.2.1 .NMR Metabonomics;154
9.1.1.4.2.2;8.4.2.2 .GC-MS/LC-MS Metabolomics;154
9.1.1.4.3;8.4.3 .Studies with Hepatotoxic Compounds;155
9.1.1.4.3.1;8.4.3.1 .NMR Metabonomics;155
9.1.1.4.3.2;8.4.3.2 .MS/LC-MS Metabolomics;158
9.1.1.4.4;8.4.4 .Studies with Peroxisome Proliferators;159
9.1.1.4.4.1;8.4.4.1 .NMR Metabonomics;159
9.1.1.4.4.2;8.4.4.2 .GC-MS/LC-MS Metabolomics;159
9.1.1.5;8.5 .Perspectives;160
9.1.1.6;8.6 .Integrated Approaches;163
9.1.2;References;166
10;Cho_Ch09_O.pdf;170
10.1;Chapter 9;170
10.1.1;Interactomics and Cancer;170
10.1.1.1;9.1 .Introduction;170
10.1.1.2;Box 1 Introduction to Graph Theory and Its Application to Network Biology;173
10.1.1.2.1;.Graph-Theoretical Description of Molecular Networks;173
10.1.1.3;9.2 .The Human Protein–Protein Interactome: Generation and Analysis;173
10.1.1.3.1;9.2.1 .Yeast-Two Hybrid System;174
10.1.1.3.2;9.2.2 .Literature Curation and Text-Mining;174
10.1.1.3.3;9.2.3 .Computational Prediction of Human Protein Interactions;175
10.1.1.3.4;9.2.4 .Databases for Human Protein Interactions;175
10.1.1.4;9.3 .Application of Interactomics to Cancer Research;176
10.1.1.4.1;9.3.1 .Network-Based Characterization of Cancer Genes;176
10.1.1.4.2;9.3.2 .Identification of New Cancer-Associated Genes and Processes Using Protein Interaction Networks;179
10.1.1.4.3;9.3.3 .Analysis of Transcriptional Regulatory Networks in Cancer Research;181
10.1.1.5;9.4 .Summary and Outlook;182
10.1.2;References;183
11;Cho_Ch10_O.pdf;186
11.1;Chapter 10;186
11.1.1;Cytomics and Predictive Medicine for Oncology;186
11.1.1.1;10.1 .Background;187
11.1.1.2;10.2 .Flow Cytometry;190
11.1.1.2.1;10.2.1 .Clinically Oriented Studies;190
11.1.1.2.2;10.2.2 .Multiparameter Data Mining;192
11.1.1.3;10.3 .Slide-Based Cytometry;193
11.1.1.3.1;10.3.1 .Predictive Medicine in Solid Tumors;195
11.1.1.3.1.1;10.3.1.1 .Diagnostic Cytomic Assays;195
11.1.1.3.1.2;10.3.1.2 .Predictive Cytomic Assays;197
11.1.1.3.2;10.3.2 .Future Aspects;197
11.1.1.4;10.4 .Conclusions;199
11.1.2;References;200
12;Cho_Ch11_O.pdf;203
12.1;Chapter 11;203
12.1.1;The Frontiers of Computational Phenomics in Cancer Research;203
12.1.1.1;11.1 .Introduction;203
12.1.1.2;11.2 .High-Throughput Collection of Phenotypes: Challenges;204
12.1.1.3;11.3 .Representation and Organization of Phenotypes for High-Throughput Analysis;205
12.1.1.3.1;11.3.1 .Ontologies Related to Cancers;205
12.1.1.3.1.1;11.3.1.1 .Gene Ontology (GO);206
12.1.1.3.1.2;11.3.1.2 .The Medical Subject Headings (MeSH);206
12.1.1.3.1.3;11.3.1.3 .The Systematized Nomenclature of Medicine (SNOMED);206
12.1.1.3.1.4;11.3.1.4 .The Unified Medical Language System (UMLS);206
12.1.1.3.1.5;11.3.1.5 .The Open Biomedical Ontologies (OBO);207
12.1.1.3.1.6;11.3.1.6 .International Classification of Diseases for Oncology (ICD-O);207
12.1.1.3.1.7;11.3.1.7 .The Medical Dictionary for Regulatory Activities (MedDRA);207
12.1.1.3.2;11.3.2 .Phenotypic Databases Related to Cancers;207
12.1.1.3.2.1;11.3.2.1 .The Online Mendelian Inheritance in Man (OMIM);208
12.1.1.3.2.2;11.3.2.2 .The Online Mendelian Inheritance in Animals (OMIA);208
12.1.1.3.2.3;11.3.2.3 .The Mouse Genome Informatics (MGI);208
12.1.1.3.2.4;11.3.2.4 .GeneCards;208
12.1.1.3.2.5;11.3.2.5 .Gene2Disease (G2D);208
12.1.1.3.2.6;11.3.2.6 .PhenomicDB;208
12.1.1.3.2.7;11.3.2.7 .PhenoGO;209
12.1.1.4;11.4 .Phenomic Analyses;209
12.1.1.5;11.5 .Future Challenges;209
12.1.2;References;211
13;Cho_Ch12_O.pdf;213
13.1;Chapter 12;213
13.1.1;Application of Bioinformatics in Cancer Research;213
13.1.1.1;12.1 .The Multidisciplinary Nature of Bioinformatics;214
13.1.1.2;12.2 .Cancer Bioinformatics;216
13.1.1.3;12.3 .Large-Scale Approach to the Study of Cancer;219
13.1.1.3.1;12.3.1 .Genomics;219
13.1.1.3.2;12.3.2 .Transcriptomics;221
13.1.1.3.3;12.3.3 .Proteomics;222
13.1.1.4;12.4 .Techniques of Large-Scale Analysis and Their Application in Cancer Research;222
13.1.1.4.1;12.4.1 .Expressed Sequences Tags (ESTs);222
13.1.1.4.2;12.4.2 .SAGE and MPSS;223
13.1.1.4.3;12.4.3 .Microarray;224
13.1.1.4.4;12.4.4 .Next-Generation Sequencing Technologies;225
13.1.1.4.5;12.4.5 .Mass Spectrometry;225
13.1.1.5;12.5 .The Integration of Omics Data;226
13.1.1.6;12.6 .Clinical Bioinformatics;227
13.1.1.6.1;12.6.1 .Identification of Gene and Protein Targets to Drugs and Vaccine Development;228
13.1.1.6.2;12.6.2 .Individualized Treatment Based on Molecular and Genetic Variation;229
13.1.1.7;12.7 .Final Remarks;231
13.1.2;References;232
14;Cho_Ch13_O.pdf;236
14.1;Chapter 13;236
14.1.1;Translational Medicine: Application of Omics for Drug Target Discovery and Validation;236
14.1.1.1;13.1 .Introduction;236
14.1.1.2;13.2 .Genomics in Drug Target Discovery and Validation;237
14.1.1.3;13.3 .Transcriptomics in Drug Target Discovery and Validation;238
14.1.1.4;13.4 .Proteomics in Drug Target Discovery and Validation;239
14.1.1.5;13.5 .Metabonomics in Drug Target Discovery and Validation;241
14.1.1.6;13.6 .Systems Biology in Drug Target Discovery and Validation;242
14.1.1.7;13.7 .Emerging Applications in Clinical Practice and Perspectives;244
14.1.1.8;13.8 .Conclusions;245
14.1.2;References;246
15;Cho_Ch14_O.pdf;249
15.1;Chapter 14;249
15.1.1;Integration of Omics Data for Cancer Research;249
15.1.1.1;14.1 .The Role of Data Integration in Cancer Research;250
15.1.1.1.1;14.1.1 .Types of Omics Data;250
15.1.1.1.2;14.1.2 .Need for Integration of Omics Data;252
15.1.1.2;14.2 .The Problem of Data Integration in Cancer Research;254
15.1.1.2.1;14.2.1 .Database Integration Approaches;255
15.1.1.2.1.1;14.2.1.1 .Centralized Approaches;255
15.1.1.2.1.2;14.2.1.2 .Query Translation Approaches;256
15.1.1.2.1.3;14.2.1.3 .Levels of Heterogeneity: Instance .versus. Schema;256
15.1.1.2.1.4;14.2.1.4 .Public Database Integration;257
15.1.1.2.2;14.2.2 .Techniques for Integrating Omics Data;258
15.1.1.2.3;14.2.3 .Omics Integration Algorithms;258
15.1.1.3;14.3 .Examples of Omics Integration: International Efforts;259
15.1.1.3.1;14.3.1 .ACGT;260
15.1.1.3.2;14.3.2 .caBIG;260
15.1.1.3.3;14.3.3 .HeC;260
15.1.1.3.4;14.3.4 .ONTOFUSION;261
15.1.1.3.5;14.3.5 .BIRN;261
15.1.1.3.6;14.3.6 .SIG;262
15.1.1.4;14.4 .Future of Data Integration in Genomics Medicine;262
15.1.1.4.1;14.4.1 .Personalized Genomic Medicine;262
15.1.2;References;263
16;Cho_Index_O.pdf;267

Leseproben

" (p. 105-106)


Abstract Protein–protein interactions, post-translational modifications, and interaction between protein and DNA or RNA can all shift the activity of a protein from what would have been predicted by its level of transcription. Functional proteomics studies the interaction of proteins within their cellular environment to determine how a given protein accomplishes its specific cellular task. Accordingly, the promise of functional proteomics is that by chronicling the function of aberrant or over-expressed proteins, it will be possible to characterize the mechanism of the disease-sustaining proteins. The further understanding of the disease networks will lead to targeted cancer therapy and specific biomarkers for diagnosis, prognosis or therapeutic response prediction based on disease specific proteins. In the context of other proteomic technologies, targeted antibody arrays are strongly contributing for functional proteomics analyses. This chapter describes how such strategies reported to date that may assist in the diagnosis, surveillance, prognosis, and potentially for predictive and therapeutic purposes for patients affected with solid and haematological neoplasias.

6.1 Functional Proteomics in Oncology: Concepts

Cancer can be described as a genetic disease, driven by the multistep accumulation of genetic and epigenetic factors. These molecular alterations result in uncontrolled cellular proliferation, cell cycle deregulation, decrease in cell death or apoptosis, blockage of differentiation, invasion, and metastatic spread.

The particular genetic and protein expression alterations that occur as part of the crosstalk between these pathways, will in great part determine the biological behavior of the tumor including its ability to grow, recur, progress and metastasize. The advent of high-throughput methods of molecular analysis can comprehensively survey the genetic and protein profiles characteristic of distinct tumor types and identify targets and pathways that may underlie a particular clinical behavior.

The driving force behind oncoproteomics is the belief that certain protein signatures or patterns are associated with a particular malignancy and clinical behavior. If so, the correlation of clinical parameters with defined protein expression patterns that reflect the mutated genetic program that caused or was involved in cancer progression, would allow tumor stratification, predict disease progression and even define improved tailored therapeutic modalities.

The technological challenges to achieve these goals are significant since the human proteome is not defined. One potential solution to finding cancer-associated protein signatures is functional proteomic antibody array-based techniques. While the amino acid sequence of a protein is uniquely determined by a nucleotide sequence, the genetic code of a protein is not a complete predictor of the function of a protein. Many in vivo factors can alter the activity level or function of a protein as cells are influenced by a complex system of communication with other cells and factors in their microenvironment.

Protein–protein interactions, posttranslational modifications, and interaction between protein and DNA or RNA can all shift the activity of a protein from what would have been predicted by its level of transcription. Functional proteomics studies the interaction of proteins within their cellular environment to determine how a given protein accomplishes its specific cellular task. Accordingly the promise of functional proteomics is that by chronicling the function of aberrant or over-expressed proteins, it will be possible to characterize the mechanism of the disease-sustaining proteins.

The further understanding of the disease networks will lead to targeted cancer therapy and specific biomarkers for diagnosis, prognosis or therapeutic response prediction based on disease specific proteins. In addition, the response of proteins to molecular targeted therapy could be monitored to determine the efficacy of the targeted therapy and potential viable future therapies involving the same protein pathway (Azad et al. 2006)."