1. Andrey Alexeyenko M edical E pidemiology and B iostatistics Gene network approach in epidemiology

2. Network is just a graph! The fact that we can draw a network does not yet make it a biological reality!..

3. Why the network approach is an advancement compared to differential expression analysis? Functional context “Anchoring”, i.e. interdependence Biological interpretability Accounts for more statistical properties Data integration More data = flexibility!

4. Gene network discovery: high-throughput experiments

5. Bayesian inference: rps14 and rps8 functionally coupled rps14 and rps8 co- expressed P(C|E) = (P(C) * P(E|C)) / P(E) rps14 rps8 Gene network discovery: probabilistic data integration

6. FunCoup is a data integration framework to discover functional coupling A Human B Human ? Find orthologs* Mouse Worm Fly Yeast High-throughput evidence

7. Conversion “data pieces  confidence” in a Bayesian framework

8. Data components in FunCoup A

9. FunCoup: on-line interactome resource Andrey Alexeyenko and Erik L.L. Sonnhammer (2009) Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research. http://FunCoup.sbc.su.se

10. Gene network discovery: getting rid of spurious links 0.7 0.5 0.4 Data processing inequality: “Direct links convey more information than indirect ones”

11. Mutations: distinguishing drivers from passengers Functional coupling transcription  transcription transcription  methylation methylation  methylation mutation  methylation mutation  transcription mutation  mutation + mutated gene

12. Network curation: cancer viewed by KEGG database curators

13. Prostate cancer: recapitulated by FunCoup

14. Network reconstruction: combination of methods

15. Combination of methods: edges with different features A Alexeyenko, DM Wassenberg, EK Lobenhofer, J Yen, ELL Sonnhammer, E Linney, JN Meyer (2010) Transcriptional response to dioxin in the interactome of developing zebrafish. PLoS One.

16. Verification of single gene lists Yellow diamonds : somatic mutations in prostate cancer Pink crosses: also mutated in glioblastome (TCGA)

17. Subtyping cancer. Personalized medicine. Power of clinical trials AZD 2281 Salt Lake City, UT, June 19, 2007— Myriad Genetics, Inc. today announced the start of two Phase II trials for a new compound being tested to treat patients with BRCA1 & BRCA2 positive breast and ovarian cancer.

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

19. Candidate signature in the network Biomarker candidates

20. Ready network-based signature RELAPSE = γ 1 EIF3S9 + γ 2 CRHR1 + γ 3 LYN + … + γ N KCNA5

21. Identical genotypes can go different ontogenetic ways Disease Gene a Protein A

22. Current gene expression results from inherited genotype, ontogenesis, and disease etiology Disease Physiological condition Birth Development Adult Gene a Gene Protein A

23. Pathway cross-talk

24. Analysis of cancer-specific wiring Pathway network of normal vs. tumor tissues Edges connect pathways given a higher ( N>9; p 0 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 from mRNA co- expression in expO tumor samples + ALL human data + mouse PPI. Node s i z e : number of pathway members in the network. Edge opacity : p 0. Edge t hi ck ne ss : number of gene- gene links.

25. Arrow of time: network prospective A Alexeyenko, DM Wassenberg, EK Lobenhofer, J Yen, ELL Sonnhammer, E Linney, JN Meyer (2010) Transcriptional response to dioxin in the interactome of developing zebrafish. PLoS One.

26. Thanks to: Erik Sonnhammer Martin Klammer Sanjit Roopra Joel Meyer Thank you for listening! http://FunCoup.sbc.su.se

27. Summary: Predicting gene networks is realistic. Proposed applications: – Genetic heterogeneity of cancer – Communication between different cells, tissues, processes etc. – Evaluation of candidate biomarkers – Expression signatures Ask concrete practical questions, not global ones!

28. Prostate cancer a cancer regulatory network + proteomics (HPA) data Cyan: up-regulated in normal glandular cells Red/green: up/down-regulated in malignant cells Yellow&magenta: (potential) regulators of prostate cancer