Computational Systems Biology of Cancer

456 Pages·2012·30.157 MB·\456

by  Emmanuel Barillot (Author), Laurence Calzone (Author), Philippe Hupe (Author), Jean-Philippe Vert (Author), Andrei Zinovyev (Author)

Systems Biology
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Computational
“An up-to-date, comprehensive and very readable overview, this book has u
plenty for everyone interested in computational systems biology of cancer. t
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… Think of this as your guide book to the field, as well as a way to get
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i Systems Biology of
started in it.” o
—Terry Speed, University of California, Berkeley, and Walter and Eliza Hall n
Institute of Medical Research a
Cancer
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“This book deals with an important and very timely topic … The authors
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have made a substantial and useful effort to describe the state of the art of y
these computational methods in an accessible and clear way. The book is a s
t
much-needed contribution to modern cancer analysis and to the emerging e
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discipline of systems biology.”
—Ron Shamir, Tel Aviv University, Israel s

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“This is the first book specifically focused on computational systems
i
biology of cancer with a coherent and proper vision on how to tackle this o
formidable challenge. I would like to congratulate the authors for their vision l
o
and dedication.”
g
—Hiroaki Kitano, The Systems Biology Institute, Sony Computer Science
y
Laboratories, Inc., and Okinawa Institute of Science and Technology
o
Drawn from the authors’ decade-long work in the cancer computational f

systems biology laboratory at Institut Curie (Paris, France), Computational C
Systems Biology of Cancer explains how to apply computational a
n
systems biology approaches to cancer research. Suitable for readers in
c
both computational and life sciences, this self-contained guide provides
e
proven techniques and tools for cancer bioinformatics and systems biology
r
research. It explores how computational systems biology can help fight
cancer in three essential aspects: categorising tumours, finding new
targets, and designing improved and tailored therapeutic strategies. Barillot
Calzone
Hupé Emmanuel Barillot Laurence Calzone
Vert
Philippe Hupé Jean-Philippe Vert
Zinovyev
K11531 Andrei Zinovyev
K11531_Cover.indd 1 7/24/12 3:16 PM
Computational
Systems Biology of
Cancer
K11531_FM.indd 1 7/18/12 12:34 PM
CHAPMAN & HALL/CRC
Mathematical and Computational Biology Series
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Department of Mathematical Sciences
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Xihong Lin
Department of Biostatistics
Harvard University
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School of Computer Science
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Maria Victoria Schneider
European Bioinformatics Institute
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Department of Computer Science
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Ralf Blossey
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An Introduction to Systems Biology:
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A Comprehensive Approach
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Oleg Demin and Igor Goryanin
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Rudy Guerra and Darlene R. Goldstein
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Differential Equations and Mathematical
Programming in Perl, Python, and Ruby
Biology, Second Edition
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D.S. Jones, M.J. Plank, and B.D. Sleeman
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Modeling and Simulation of Capsules Spatial Ecology
and Biological Cells Stephen Cantrell, Chris Cosner, and
C. Pozrikidis Shigui Ruan
Niche Modeling: Predictions from Spatiotemporal Patterns in Ecology
Statistical Distributions and Epidemiology: Theory, Models,
David Stockwell and Simulation
Horst Malchow, Sergei V. Petrovskii, and
Normal Mode Analysis: Theory and
Ezio Venturino
Applications to Biological and Chemical
Systems Statistics and Data Analysis for
Qiang Cui and Ivet Bahar Microarrays Using R and Bioconductor,
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Models Sorin Dra˘ghici
Suzanne Lenhart and John T. Workman Stochastic Modelling for Systems
Biology, Second Edition
Pattern Discovery in Bioinformatics:
Darren J. Wilkinson
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The Ten Most Wanted Solutions in
Quantitative Biology: From Molecular to
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Cellular Systems
Anna Tramontano
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Computational
Systems Biology of
Cancer
Emmanuel Barillot Laurence Calzone
Philippe Hupé Jean-Philippe Vert
Andrei Zinovyev
K11531_FM.indd 5 7/18/12 12:34 PM
Cover illustration © 2012 Philippe Hupé.
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Contents
List of Figures xv
List of Tables xix
List of Acronyms xxi
Preface xxvii
Acknowledgements xxix
1 Introduction: Why systems biology of cancer? 1
1.1 Cancer is a major health issue . . . . . . . . . . . . . . . . . 1
1.1.1 Bit of history . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Definition of cancer . . . . . . . . . . . . . . . . . . . 2
1.1.3 Few facts about a killer . . . . . . . . . . . . . . . . . 3
1.1.4 Progress in cancer treatment is real, but insufficient . 3
1.1.5 Progress in cancer drug development needs a
qualitative evolution . . . . . . . . . . . . . . . . . . . 4
1.2 From genome to genes to network . . . . . . . . . . . . . . . 5
1.2.1 Accumulation of alterations . . . . . . . . . . . . . . . 5
1.2.2 Cancer is a gene disease . . . . . . . . . . . . . . . . . 6
1.2.3 Cancer is a network disease . . . . . . . . . . . . . . . 7
1.3 Cancer research as a big science . . . . . . . . . . . . . . . . 7
1.3.1 Cancer research is technology-driven . . . . . . . . . . 7
1.3.2 Microarray era . . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 Next-generation sequencing era . . . . . . . . . . . . . 8
1.3.4 Cancer research and international data provider
consortia . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Cancer is a heterogeneous disease . . . . . . . . . . . . . . . 9
1.4.1 Cancer heterogeneity . . . . . . . . . . . . . . . . . . . 9
1.4.2 Sorting out tumour heterogeneity: Classifying tumours 10
1.5 Cancer requires personalised medicine . . . . . . . . . . . . . 11
1.5.1 Definition of personalised medicine . . . . . . . . . . . 11
1.5.2 Choosing the adequate treatment: Prediction and
prognosis . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5.3 Designing a personalised treatment . . . . . . . . . . . 12
vii
viii Contents
1.6 What is systems biology? . . . . . . . . . . . . . . . . . . . . 13
1.6.1 Operational definition for systems biology . . . . . . . 14
1.6.2 Systems biology: Is it data-driven or model-driven? . . 15
1.6.3 Systems biology: Yet another definition . . . . . . . . 17
1.6.4 Systems biology of cancer . . . . . . . . . . . . . . . . 18
1.7 About this book . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.7.1 What does this book try to achieve? . . . . . . . . . . 20
1.7.2 Who should read this book? . . . . . . . . . . . . . . . 20
1.7.3 How is this book organised? . . . . . . . . . . . . . . . 21
2 Basic principles of the molecular biology of cancer 25
2.1 Progressive accumulation of mutations . . . . . . . . . . . . 26
2.2 Cancer-critical genes . . . . . . . . . . . . . . . . . . . . . . . 27
2.2.1 Oncogenes. . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2.2 Tumour suppressor genes . . . . . . . . . . . . . . . . 30
2.2.3 Non-protein-coding cancer-critical genes . . . . . . . . 33
2.3 Evolution of tumour cell populations . . . . . . . . . . . . . 33
2.3.1 Clonal origin . . . . . . . . . . . . . . . . . . . . . . . 33
2.3.2 Stemness of cancer cells . . . . . . . . . . . . . . . . . 33
2.4 Alterations of gene regulation and signal transduction
mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.1 Modification of transcription factor activity . . . . . . 35
2.4.2 Epigenetic modifications . . . . . . . . . . . . . . . . . 37
2.4.3 Modification of the post-transcriptional regulations . . 38
2.4.4 Disruption of signal transduction . . . . . . . . . . . . 38
2.5 Cancer is a network disease . . . . . . . . . . . . . . . . . . . 39
2.6 Tumour microenvironment . . . . . . . . . . . . . . . . . . . 40
2.7 Hallmarks of cancer . . . . . . . . . . . . . . . . . . . . . . . 41
2.7.1 Hallmark capabilities. . . . . . . . . . . . . . . . . . . 41
2.7.2 Emerging hallmarks . . . . . . . . . . . . . . . . . . . 44
2.7.3 Enabling characteristics . . . . . . . . . . . . . . . . . 45
2.8 Chromosome aberrations in cancer . . . . . . . . . . . . . . . 45
2.8.1 Abnormal karyotype in cancer . . . . . . . . . . . . . 46
2.8.2 Impact of chromosome aberrations on cancer-critical
genes . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 Experimental high-throughput technologies for cancer
research 53
3.1 Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.1.1 General principles and microarray design . . . . . . . 54
3.1.2 DNA copy number study based on microarray
experiment . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.3 LOH study based on microarray experiment . . . . . . 62
3.1.4 RNA study based on microarray experiment. . . . . . 67
Contents ix
3.1.5 DNA–protein interaction study . . . . . . . . . . . . . 69
3.1.6 DNA methylation . . . . . . . . . . . . . . . . . . . . 71
3.2 Emerging sequencing technologies . . . . . . . . . . . . . . . 73
3.2.1 General principles of high-throughput sequencing . . . 74
3.2.2 Principles of high-throughput sequencing based on
amplification . . . . . . . . . . . . . . . . . . . . . . . 75
3.2.3 Principles of single-molecule sequencing . . . . . . . . 78
3.2.4 Targeted sequencing . . . . . . . . . . . . . . . . . . . 81
3.2.5 Application of high-throughput sequencing in oncology 84
3.2.6 Towards single-cell sequencing . . . . . . . . . . . . . 85
3.3 Chromosome conformation capture . . . . . . . . . . . . . . 85
3.4 Large-scale proteomics . . . . . . . . . . . . . . . . . . . . . 89
3.4.1 Microarray-based proteomics . . . . . . . . . . . . . . 89
3.4.2 Mass spectrometry proteomics . . . . . . . . . . . . . 91
3.4.3 Protein–protein interactions . . . . . . . . . . . . . . . 95
3.5 Cellular phenotyping . . . . . . . . . . . . . . . . . . . . . . 100
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4 Bioinformatics tools and standards for systems biology 105
4.1 Experimental design . . . . . . . . . . . . . . . . . . . . . . . 106
4.1.1 Choosing the optimal set of experiments . . . . . . . . 107
4.1.2 Efficient statistical inference . . . . . . . . . . . . . . . 108
4.1.3 Specific aspects in systems biology . . . . . . . . . . . 111
4.2 Normalisation . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.3 Quality control . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.4 Quality management and reproducibility in computational
systems biology workflow . . . . . . . . . . . . . . . . . . . . 114
4.5 Data annotations and ontologies . . . . . . . . . . . . . . . . 115
4.5.1 A priori biological knowledge . . . . . . . . . . . . . . 116
4.5.2 Standards for data and knowledge sharing . . . . . . . 118
4.6 Data management and integration . . . . . . . . . . . . . . . 119
4.7 Public repositories for high-throughput data . . . . . . . . . 120
4.8 Informatics architecture and data processing . . . . . . . . . 121
4.9 Knowledge extraction and network visualisation . . . . . . . 122
4.9.1 Charting a map of knowledge . . . . . . . . . . . . . . 122
4.9.2 Example of a map of knowledge: RB pathway . . . . . 123
5 Exploring the diversity of cancers 127
5.1 Traditional classification of cancer . . . . . . . . . . . . . . . 127
5.2 Towards a molecular classification of cancers . . . . . . . . . 129
5.3 Clustering for class discovery . . . . . . . . . . . . . . . . . . 131
5.3.1 Choosing a distance between samples . . . . . . . . . 133
5.3.2 Hierarchical clustering methods . . . . . . . . . . . . . 135
5.3.3 Partitioning methods. . . . . . . . . . . . . . . . . . . 137
5.3.4 Choosing the number of groups . . . . . . . . . . . . . 139