Systems Biology Approaches to Cancer
Raunak Shrestha
14 May 2013
BACKGROUND
System Biology & Biological Networks
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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, 20123
How is a network constructed ?
Wang and Marcotte, J Proteomics. 20104
Yeast: Transcription Factor-Binding Network Yeast: Protein–Protein Interaction Network
Yeast: Phosphorylation Network Yeast: Genetic NetworkE. coli Metabolic Network
Zhu et al. Genes Dev. 20075
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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Characteristics of a Biological Network
Elgoyhen et al., Front. Syst. Neurosci. 20127
Barabási and Oltvai, Nature Reviews Genetics, 20048
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
DATA INTEGRATION
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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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How networks plays a vital role in data integration ?
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Different Data Types
Interaction Network
Model Outcome
Weischenfeldt et al. Cell. 2013
Data Labeling/Overlaying Vs Data Integration
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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
Cause Effect
Somatic MutationsStructural Variations
Copy Number Aberrations
Prioritize Candidate Driver Genes of Cancer
Gene FusionsAlternative SplicingDNA Methylation
? ?
Interaction Network
Gene ExpressionmiRNA 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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APPLICATIONS
What can we do with these molecular networks?
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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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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
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. 201219
Hubs tend to be essential
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Hubs tend to be essential
Massagué, Cell. 2008
Degree = How well a node is connected in a network21
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System Biology
in Cancer
Disease Classification
Identify Driver Genes
DysregulatedGene
modules / Pathway
Personalized Medicine
Decipher disease
biological mechanisms
Biomarker development
Drug Target Identification
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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
25“Following the light of the sun, we left the
Old World.” –Christopher ColumbusFriend and Norman, Nat. Biotech. April 2013
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