An Omics Perspective on Cancer Research (eBook)

Cho_FM1_O.pdf 2
An Omics Perspective on Cancer Research 2
Preface 5
Cho_Ch01_O.pdf 8
Chapter 1 8
Omics Approaches in Cancer Research 8
1.1 .Introduction 9
1.2 .Genomics 9
1.3 .Epigenomics 10
1.4 .Transcriptomics 10
1.5 .Proteomics 11
1.6 .Metabolomics 11
1.7 .Interactomics 12
1.8 .Cytomics 13
1.9 .Phenomics 13
1.10 .Bioinformatics 13
1.11 .From Omics to Personalized Medicine 14
1.12 .Challenges and Prospective 14
References 15
Cho_Ch02_O.pdf 17
Chapter 2 17
Recent Advances in Cancer Genomics and Cancer-Associated Genes Discovery 17
2.1 .Introduction 17
2.2 .Array-Based Technologies 18
2.2.1 .Array Comparative Genomic Hybridization (aCGH) 18
2.2.2 .Representational Oligonucleotide Microarray Analysis 21
2.2.3 .SNP Arrays 21
2.3 .Sequencing-Based Technologies 22
2.3.1 .Mutational Analyses of Cancer Genome 22
2.3.2 .Digital Karyotyping 24
2.3.3 .Genomic DNA End-Sequencing: BAC, Fosmid and Paired-End 25
2.3.4 .Paired-End diTags (PETs) cDNA Sequencing 26
2.4 .Identification of Cancer-Associated Genes 27
2.5 .Strategy to Prioritize Candidate Genes for Validation and Functional Characterization 28
2.6 .Cancer Genomics from Bench to Bedside 30
2.7 .Concluding Remarks 32
References 32
Cho_Ch03_O.pdf 36
Chapter 3 36
An Integrated Oncogenomic Approach: From Genes to Pathway Analyses 36
3.1 .Introduction to Gene Expression Profiling 36
3.2 .Identification of Discriminative Genes 38
3.3 .Dissecting the Components of a Gene Expression Profile 41
3.3.1 .Effects of Genomic Structure 42
3.3.2 .Effects of Cellular Pathways 44
3.3.3 .Pathway Analysis Methodology 46
3.4 .Integration of Gene Expression Components for Discovery 48
3.5 .The Evolution of Pathway Analysis 49
References 51
Cho_Ch04_O.pdf 56
Chapter 4 56
The Epigenomics of Cancer 56
4.1 .Introduction 56
4.2 .Emerging Technologies for Genome-Wide Profiling of DNA Methylation 57
4.3 .Methylation-Sensitive Restriction Enzyme-Based Method 1 – RLGS 58
4.4 .Methylation-Sensitive Restriction Enzyme-Based Method 2 – MS-RDA and MCA-RDA 60
4.5 .Methylation-Sensitive Restriction Enzyme-Based Method 3 – MIAMI 60
4.6 .DNA Immunoprecipitation (MeDIP)-Based Method – MeDIP-Chip 63
4.7 .Bisulfite-Based Methods 63
4.8 .Comparison of the Methods 66
4.9 .Histone Modifications of Cancer 67
4.10 .Technologies for Genome-Wide Profiling of Histone Modifications 69
4.11 .Epigenetic Therapy of Cancer 69
4.12 .Perspectives and Conclusion 70
References 70
Cho_Ch05_O.pdf 73
Chapter 5 73
Involvement of MicroRNAs in Human Cancer: Discovery and Expression Profiling 73
5.1 .Introduction to MicroRNAs 73
5.2 .The Discovery of the Involvement of MicroRNAs in Human Cancer 75
5.3 .Numerous MicroRNAs Are Involved in Human Cancer 76
5.4 .MicroRNAs Are Central Players in Malignant Transformation Processes 85
5.5 .miRNA Expression Signatures for Cancer Classification, Prognostic Stratification and Therapy Response 96
5.6 .miRNAs in Anti-cancer Therapy 97
5.7 .Concluding Remarks 100
References 100
Cho_Ch06_O.pdf 109
Chapter 6 109
Functional Proteomics in Oncology: A Focus on Antibody Array-Based Technologies 109
6.1 .Functional Proteomics in Oncology: Concepts 109
6.2 .Antibody Array-Based Techniques in the Context of Other Functional Proteomic Approaches 111
6.3 .Antibody Array Formats 112
6.3.1 .Current Formats 112
6.3.2 .Emerging Formats for Functional Proteomics 115
6.4 .Strategies and Applications of Antibody Arrays for Functional Proteomics 116
6.4.1 .Cell Culture 116
6.4.2 .Tissue Specimens 119
6.4.3 .Body Fluids 121
6.5 .Conclusions 123
References 125
Cho_Ch07_O.pdf 128
Chapter 7 128
Protein Graphs in Cancer Prediction 128
7.1 .Introduction 128
7.2 .Materials and Methods 132
7.2.1 .Protein Database 132
7.2.2 .MS Database 133
7.2.3 .MS Data Coding 133
7.2.4 .MARCH-INSIDE Software 134
7.2.5 .Lattice Network Representations 134
7.2.6 .MS Star Graph Representation 135
7.2.7 .Protein Star Graph Representation 136
7.2.8 .Entropy Measurements 136
7.2.9 .Linear Discriminant Analysis (LDA) 137
7.3 .Results and Discussion 137
7.3.1 .Classification Function 137
7.4 .Conclusions 140
References 140
Cho_Ch08_O.pdf 144
Chapter 8 144
The Use of Metabolomics in Cancer Research 144
8.1 .Introduction 145
8.1.1 .General 145
8.1.2 .Metabolite Profiling of Liver Tumor Promoters 145
8.2 .Methods 146
8.2.1 .Metabolite Profiling – General 146
8.2.2 .NMR Metabonomics 147
8.2.3 .GC-MS/LC-MS Metabolomics 147
8.3 .Metabolite Profiling and Cancer 148
8.3.1 .Challenges 150
8.4 .Metabolite Profiles 152
8.4.1 .Control Animals 152
8.4.1.1 .NMR Metabonomics 152
8.4.1.2 .GC-MS/LC-MS Metabolomics 152
8.4.2 .Studies with Liver Enzyme Inducers 154
8.4.2.1 .NMR Metabonomics 154
8.4.2.2 .GC-MS/LC-MS Metabolomics 154
8.4.3 .Studies with Hepatotoxic Compounds 155
8.4.3.1 .NMR Metabonomics 155
8.4.3.2 .MS/LC-MS Metabolomics 158
8.4.4 .Studies with Peroxisome Proliferators 159
8.4.4.1 .NMR Metabonomics 159
8.4.4.2 .GC-MS/LC-MS Metabolomics 159
8.5 .Perspectives 160
8.6 .Integrated Approaches 163
References 166
Cho_Ch09_O.pdf 170
Chapter 9 170
Interactomics and Cancer 170
9.1 .Introduction 170
Box 1 Introduction to Graph Theory and Its Application to Network Biology 173
.Graph-Theoretical Description of Molecular Networks 173
9.2 .The Human Protein–Protein Interactome: Generation and Analysis 173
9.2.1 .Yeast-Two Hybrid System 174
9.2.2 .Literature Curation and Text-Mining 174
9.2.3 .Computational Prediction of Human Protein Interactions 175
9.2.4 .Databases for Human Protein Interactions 175
9.3 .Application of Interactomics to Cancer Research 176
9.3.1 .Network-Based Characterization of Cancer Genes 176
9.3.2 .Identification of New Cancer-Associated Genes and Processes Using Protein Interaction Networks 179
9.3.3 .Analysis of Transcriptional Regulatory Networks in Cancer Research 181
9.4 .Summary and Outlook 182
References 183
Cho_Ch10_O.pdf 186
Chapter 10 186
Cytomics and Predictive Medicine for Oncology 186
10.1 .Background 187
10.2 .Flow Cytometry 190
10.2.1 .Clinically Oriented Studies 190
10.2.2 .Multiparameter Data Mining 192
10.3 .Slide-Based Cytometry 193
10.3.1 .Predictive Medicine in Solid Tumors 195
10.3.1.1 .Diagnostic Cytomic Assays 195
10.3.1.2 .Predictive Cytomic Assays 197
10.3.2 .Future Aspects 197
10.4 .Conclusions 199
References 200
Cho_Ch11_O.pdf 203
Chapter 11 203
The Frontiers of Computational Phenomics in Cancer Research 203
11.1 .Introduction 203
11.2 .High-Throughput Collection of Phenotypes: Challenges 204
11.3 .Representation and Organization of Phenotypes for High-Throughput Analysis 205
11.3.1 .Ontologies Related to Cancers 205
11.3.1.1 .Gene Ontology (GO) 206
11.3.1.2 .The Medical Subject Headings (MeSH) 206
11.3.1.3 .The Systematized Nomenclature of Medicine (SNOMED) 206
11.3.1.4 .The Unified Medical Language System (UMLS) 206
11.3.1.5 .The Open Biomedical Ontologies (OBO) 207
11.3.1.6 .International Classification of Diseases for Oncology (ICD-O) 207
11.3.1.7 .The Medical Dictionary for Regulatory Activities (MedDRA) 207
11.3.2 .Phenotypic Databases Related to Cancers 207
11.3.2.1 .The Online Mendelian Inheritance in Man (OMIM) 208
11.3.2.2 .The Online Mendelian Inheritance in Animals (OMIA) 208
11.3.2.3 .The Mouse Genome Informatics (MGI) 208
11.3.2.4 .GeneCards 208
11.3.2.5 .Gene2Disease (G2D) 208
11.3.2.6 .PhenomicDB 208
11.3.2.7 .PhenoGO 209
11.4 .Phenomic Analyses 209
11.5 .Future Challenges 209
References 211
Cho_Ch12_O.pdf 213
Chapter 12 213
Application of Bioinformatics in Cancer Research 213
12.1 .The Multidisciplinary Nature of Bioinformatics 214
12.2 .Cancer Bioinformatics 216
12.3 .Large-Scale Approach to the Study of Cancer 219
12.3.1 .Genomics 219
12.3.2 .Transcriptomics 221
12.3.3 .Proteomics 222
12.4 .Techniques of Large-Scale Analysis and Their Application in Cancer Research 222
12.4.1 .Expressed Sequences Tags (ESTs) 222
12.4.2 .SAGE and MPSS 223
12.4.3 .Microarray 224
12.4.4 .Next-Generation Sequencing Technologies 225
12.4.5 .Mass Spectrometry 225
12.5 .The Integration of Omics Data 226
12.6 .Clinical Bioinformatics 227
12.6.1 .Identification of Gene and Protein Targets to Drugs and Vaccine Development 228
12.6.2 .Individualized Treatment Based on Molecular and Genetic Variation 229
12.7 .Final Remarks 231
References 232
Cho_Ch13_O.pdf 236
Chapter 13 236
Translational Medicine: Application of Omics for Drug Target Discovery and Validation 236
13.1 .Introduction 236
13.2 .Genomics in Drug Target Discovery and Validation 237
13.3 .Transcriptomics in Drug Target Discovery and Validation 238
13.4 .Proteomics in Drug Target Discovery and Validation 239
13.5 .Metabonomics in Drug Target Discovery and Validation 241
13.6 .Systems Biology in Drug Target Discovery and Validation 242
13.7 .Emerging Applications in Clinical Practice and Perspectives 244
13.8 .Conclusions 245
References 246
Cho_Ch14_O.pdf 249
Chapter 14 249
Integration of Omics Data for Cancer Research 249
14.1 .The Role of Data Integration in Cancer Research 250
14.1.1 .Types of Omics Data 250
14.1.2 .Need for Integration of Omics Data 252
14.2 .The Problem of Data Integration in Cancer Research 254
14.2.1 .Database Integration Approaches 255
14.2.1.1 .Centralized Approaches 255
14.2.1.2 .Query Translation Approaches 256
14.2.1.3 .Levels of Heterogeneity: Instance .versus. Schema 256
14.2.1.4 .Public Database Integration 257
14.2.2 .Techniques for Integrating Omics Data 258
14.2.3 .Omics Integration Algorithms 258
14.3 .Examples of Omics Integration: International Efforts 259
14.3.1 .ACGT 260
14.3.2 .caBIG 260
14.3.3 .HeC 260
14.3.4 .ONTOFUSION 261
14.3.5 .BIRN 261
14.3.6 .SIG 262
14.4 .Future of Data Integration in Genomics Medicine 262
14.4.1 .Personalized Genomic Medicine 262
References 263
Cho_Index_O.pdf 267

"Chapter 6 Functional Proteomics in Oncology: A Focus on Antibody Array-Based Technologies (p. 105-106)

Marta Sanchez-Carbayo

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)."