Systems Biology Approaches to Cancer

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  • Although the individual components are unique to a given organism, the topologic properties of cellular networks share surprising similarities with those of natural and social networks
  • A yeast transcription factor-binding network, composed of known transcription factor-binding data collected with large-scale ChIP–chip and small-scale experiments.(B) A yeast protein–protein interaction network, containing protein–protein interactions identified by yeast two-hybrid and protein complexes identified by affinity purification and mass spectrometry(C) A yeast phosphorylation network comprised primarily of in vitro phosphorylation events identified using protein microarrays(D) An E. coli metabolic network with 574 reactions and 473 metabolites colored according to their modules(E) A yeast genetic network constructed with synthetic lethal interactions using SGA analysis on eight yeast genes
  • Systems Biology Approaches to Cancer

    1. 1. Systems Biology Approaches to Cancer Raunak Shrestha 14 May 2013
    2. 2. BACKGROUND System Biology & Biological Networks 2
    3. 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 3
    4. 4. How is a network constructed ? Wang and Marcotte, J Proteomics. 2010 4
    5. 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 5
    6. 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 6
    7. 7. Characteristics of a Biological Network Elgoyhen et al., Front. Syst. Neurosci. 2012 7
    8. 8. Barabási and Oltvai, Nature Reviews Genetics, 2004 8
    9. 9. Summary of Prior Knowledge Sources 9 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. 10. DATA INTEGRATION 10
    11. 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 11
    12. 12. How networks plays a vital role in data integration ? 12
    13. 13. Different Data Types Interaction Network Model Outcome Weischenfeldt et al. Cell. 2013 Data Labeling/Overlaying Vs Data Integration 13
    14. 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 14 Class 1 Class 2
    15. 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. 15
    16. 16. APPLICATIONS What can we do with these molecular networks? 16
    17. 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 17
    18. 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 18 • Large Protein-Protein Interaction networks recently became available – Extract subnetworks from PPI networks as markers
    19. 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 19
    20. 20. Hubs tend to be essential 20
    21. 21. Hubs tend to be essential Massagué, Cell. 2008 Degree = How well a node is connected in a network 21
    22. 22. 22
    23. 23. 23
    24. 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 24
    25. 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 25 “Following the light of the sun, we left the Old World.” –Christopher ColumbusFriend and Norman, Nat. Biotech. April 2013
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