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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