This document discusses systems biology approaches to studying cancer. It defines systems biology as studying organisms as interacting networks of genes, proteins, and reactions. Biological networks are constructed from different types of data and relationships. Integrating multiple data types into networks can provide a more complete understanding of cancer than single data types in isolation. Networks can be used to identify cancer driver genes, dysregulated pathways, and biomarkers for disease classification, understanding mechanisms, and drug development. While current biological networks are incomplete, systems approaches have already provided insights and are expected to be more powerful as networks become more comprehensive.

1. Systems Biology Approaches to
Cancer
Raunak Shrestha
14 May 2013

2. BACKGROUND
System Biology & Biological Networks
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3. What is systems biology?
• Systems biology is the study of an
organism, viewed as an
integrated and interacting
network of genes, proteins and
biochemical reactions which give
rise to life.
• Networks organize and integrate
information at different levels to
create biologically meaningful
models.
• Networks formulate hypotheses
about biological function and
provide temporal and spatial
insights into dynamical changes.
Oltvai and Barabási, Science. 2002Hood and Tian, Genomics, Proteomics & Bioinformatics, 2012
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4. How is a network constructed ?
Wang and Marcotte, J Proteomics. 2010
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5. Yeast: Transcription Factor-Binding Network Yeast: Protein–Protein Interaction Network
Yeast: Phosphorylation Network Yeast: Genetic NetworkE. coli Metabolic Network
Zhu et al. Genes Dev. 2007
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6. Moral of the Story
(from previous slide)
• Biological networks should not be used blindly
• Even a single organism can have multiple
types of networks
• The meaning or the edges in the network
(relationships) must be kept in mind while
analyzing the data
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7. Characteristics of a Biological Network
Elgoyhen et al., Front. Syst. Neurosci. 2012
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8. Barabási and Oltvai, Nature Reviews Genetics, 2004
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9. Summary of Prior Knowledge Sources
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Gene-sets Pathways Networks
Pros
• Many possible gene
sets (e.g. diseases,
biological processes,
molecular functions)
• Highly curated
• Captures cause and
effect relationship
• Highly curated
• Higher coverage of
genome
• Represent less well-
understood relationships
- Genetic interactions
- Physical interaction
- Coexpression
- Pathway cross-talk
Cons
• Highly overlapping
gene sets
• Sparse coverage of
genome
• Different definitions of
pathways/overlapping
pathways
• Captures only the
“well-understood”
biological processes
• Sparse coverage of
genome
• Less reliable
• False-relationships from
high-throughput
experiments and
computational predictions
Kendric Wang

10. DATA INTEGRATION
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11. Why data integration is required in for
cancer studies ?
Ding et al. Hum. Mol. Genet. 2010
Studying cancer dataset in isolation
will produce an incomplete story
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12. How networks plays a vital role in data
integration ?
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13. Different
Data Types
Interaction
Network
Model Outcome
Weischenfeldt et al. Cell. 2013
Data Labeling/Overlaying Vs Data Integration
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14. Different
Data Types
Interaction
Network
Model Sub-Networks that differentiate between the
sample class that is being compared
Strategies of Data Integration: Few Examples
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Class 1 Class 2

15. Cause Effect
Somatic Mutations
Structural Variations
Copy Number
Aberrations
Prioritize Candidate Driver Genes of Cancer
Gene Fusions
Alternative Splicing
DNA Methylation
? ?
Interaction Network
Gene Expression
miRNA Expression
Model
Strategies of Data Integration: Few Examples
Hypothesis:
Thus a perturbation in one
gene can be propagated
through the interactions,
and affect other genes in
the network.
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16. APPLICATIONS
What can we do with these molecular networks?
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17. Gene Marker Sets
• Examine genome-wide expression profiles
– Score individual genes for how well they discriminate
between different classes of disease
• Establish gene expression signature
– Problem: # genes >> # patients
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18. Pathway Expression vs. PPI
Subnetwork as Marker
• Score known pathways for
coherence of gene
expression changes?
– Majority of human genes not
yet assigned to a definitive
pathway
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• Large Protein-Protein
Interaction networks
recently became available
– Extract subnetworks from PPI
networks as markers

19. Chuang et al. Mol Syst Biol. 2007
• Subnetwork markers
correspond to the hallmarks
of cancer
• Subnetwork markers have
increased reproducibility
across data sets
• Subnetwork markers
increase the classification
accuracy of metastasis
• Subnetwork markers are
informative of non-
discriminative disease genes
Cho et al. PLoS Comput Biol. 2012
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20. Hubs tend to be essential
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21. Hubs tend to be essential
Massagué, Cell. 2008
Degree = How well a node is connected in a network
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24. System
Biology
in Cancer
Disease
Classification
Identify
Driver
Genes
Dysregulated
Gene
modules /
Pathway
Personalized
Medicine
Decipher
disease
biological
mechanisms
Biomarker
development
Drug Target
Identification
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25. Conclusion
• Present knowledge of the cellular
map (interaction network) :: tip
of an iceberg
• Still with the incomplete map
system biology has been able to
produce a lot of success stories.
• System biology techniques &
methods will even be more
efficient, robust and more
reliable in the future.
• Maps will be just as important to
biological discoveries as they
were to the discoveries in the era
of Columbus
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“Following the light of the sun, we left the
Old World.” –Christopher ColumbusFriend and Norman, Nat. Biotech. April 2013

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