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Systems biology in cancer research

Systems biology in cancer research . What is systems biology?. = Molecular physiology? “… physiology is the science of the mechanical, physical, and biochemical functions of humans … ” Wikipedia.

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Systems biology in cancer research

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  1. Systems biology in cancer research

  2. What is systems biology? = Molecular physiology? “…physiology is the science of the mechanical, physical, and biochemical functions of humans …” Wikipedia “Systems biology is a … study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (holism instead of reduction) to study them. … Because the scientific method has been used primarily toward reductionism, one of the goals of systems biology is to discover new emergent properties that may arise from the systemic view used by this discipline in order to understand better the entirety of processes that happen in a biological system.“ Wikipedia

  3. What is cancer? A disease of many genes and their interactions

  4. Cancer attractors: A systems view of tumors…Sui Huang, Ingemar Ernberg, Stuart Kauffman

  5. Biological complexity: reduction is crucial. Tool complexity ≠ Vision complexity Modelling. What is a model? • Topological vs. quantitative • Relevance vs. causality The Cancer Genome Atlas Research Network. 2008

  6. Wholesome vision: • All proteins? • All interactions? • All diseases? • All organisms?

  7. Human Protein Atlas

  8. Oncomine

  9. Amouse ? Human Rat * Bmouse Fly Yeast FunCoup: a data integration framework to discover functional coupling Andrey Alexeyenkoand Erik L.L. Sonnhammer. Global networks of functional coupling in eukaryotes from comprehensive data integration.Genome Research. Published in Advance February 25, 2009

  10. FunCoup: recapitulation of known cancer pathways Figure 5 from: The Cancer Genome Atlas Research Network Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Sep 4.[Epub ahead of print] The same genes submitted to FunCoup No TCGA data were used. Outgoing links are not shown.

  11. TGFβ<-> cancer pathway cross-talk FunCoup was queried for any links between members of TGFβ pathway (left blue circle) and habituées of known cancer pathways (members of at least 7 out of 18 groups; right blue circle). MAPK1 and MAPK3 belonged to both categories.

  12. What is FEASIBLE in systems biology? • Holistic view? • Comparison between healthy and ill? • Disease prevention? • Drug targets?

  13. Inositol phosphate metabolism Glioblastoma (TCGARN, 2008) From genes to pathways

  14. Enrichment of functional groups Group 1 Group 2 Enrichment analysis in the networks turns to be more powerful than on gene lists

  15. Discerning cancer-specific wiring • Pathway network of normal vs. tumor tissues • Edges connect pathways given a higher (N>9;p0<0.01; pFDR<0.20) number of gene-gene links (pfc>0.5) between them (seen as edge labels). • Known pathways (circles) are classified as: • signaling, • metabolic, • cancer, • other disease. • Blue lines: evidence from mRNA co-expression under normal conditions + ALL human & mouse data. • Red lines: evidence frommRNA co-expression in expO tumor samples + ALL human data + mouse PPI. • Node size: number of pathway members in the network. • Edge opacity: p0. • Edge thickness: number of gene-gene links.

  16. Level of functional groups Zebrafish transcriptome under dioxin treatment

  17. Accounting for edge features:dioxin- “enabled” vs. “sensitive” links Andrey Alexeyenko, Deena M Wassenberg, Edward K Lobenhofer, Jerry Yen, Erik LL Sonnhammer, Elwood Linney, Joel N Meyer Transcriptional response to dioxin in the interactome of developing zebrafish. PLoS One

  18. Biomarker signatures in the network Single molecular markers are often far from perfect. Combinations (signatures) should perform better. How to select optimal combinations? × Severity, Optimal treatment, Prognosis etc.

  19. Functional coupling transcription ? transcription transcription ? methylation methylation ? methylation mutation  methylation mutation  transcription mutation ? mutation + mutated gene Cancer data for basic research:a testbed Sonic hedgehog pathway

  20. Cancer individuality Tumourtcga-02-0114-01a-01w There is a CAUSATIVE gene network behind each individual cancer

  21. Functional coupling transcription ? transcription transcription ? methylation methylation ? methylation mutation  methylation mutation  transcription mutation ? mutation + mutated gene Cancer individuality in clinic

  22. Conclusions: • Cancer is a disease of multiple alternatives, hence PERSONALIZED medicine. • Systems biology: enormous complexity, great challenge. • Focus on feasible today, think of possible in the future. • Descriptive and analytic HUMAN language?

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