Andrej Bugrim GeneGo, Inc

1. Andrej Bugrim GeneGo, Inc Elucidation of differential response networks from toxicogenomics data.

2. Old and new ways to analyze data Current way of analysis: all significance calculations done before mapping onto network Statistical procedures, thresholds of fold, p-value either in MC or 3rd party tools Full data tables Connect them on network by one way or another: Too many choices, no clear way to choose Sets of genes New way of analysis: significance calculations follow the mapping onto network Statistical procedures based on concurrent analysis of expression profiles and connectivity Full data tables Apply to global network Sets of network modules

3. What we are looking for? Finding common and different pathway modules • Looking for modules on which samples are particularly “close” • Looking for modules that distinguish one group from another • Analysis of predefined groups and grouping in the unstructured population

4. “Network signatures” concept Gene expression linked with protein interactions information Conserved and variable modules between conditions

5. Gene expression clustering is not sufficient Experimental design1: MCF-7 breast cancer cells Two treatments: - Estrogen - 4-hydroxytamoxifen Five time points: 0.5, 2, 4, 12, 24 hours Clustering shows no clear separation between treatments 1We use published data from NIEHS study described in: Hodges et al., Molecular cancer research, V1, pp. 300-311, 2003

6. Time points for estrogen treatment .5h 2h 4h 12h 24h

7. C-Myc Intersection: core modules

8. G1/S G2/M M phase S phase Network assembly around the core

9. Cyclin D1 C-Fos C-Myc Cyclin B1 cdc25 cdc18L MCM complex Network core for estrogen-caused cell proliferation

10. Network modularization approach new files or HT datasets: CEBS, EDGE Iconix GeneLogic ~40-50K overlapping modules Visualization on networks

11. Conserved pathways generated from maps

12. Pathway’s gene expression space

13. Distances between samples in pathways’ expression space

14. Algorithm validation: distinguishing drug effects • Female rats were administered the AIN-76A basal diet or a diet containing the drugs admixed into the basal diet at • 500 mg Phenobarbital per kg diet, • 2 mg mestranol per kg diet, • Tamoxifen at either 250 mg per kg diet (designated as low) or 500 mg per kg diet (designated as high). •  All samples were co-hybridized with Stratagene universal rat reference RNA. The design is a 2-color common reference design experiment with the samples labeled with Cy5 and the reference always labeled with Cy3.  The 10,000 rat oligonucleotide probes were purchased from MWG and printed onto poly-L-lysine slides at NCTR Center for Functional Genomics. For each treatment group data for five biological repeats were assayed.

15. Differentiating pathways

16. Differential response for compounds effects Mestranol Phenobarbital Tamoxifen Phenobarbital

17. Gene content of differential response networks

18. Properties of differential response networks

19. Major hubs

20. Functional interpretation

21. Functional interpretation

22. Top-scoring map for start of mitosis

23. Clustering genes based on expression

24. Extension to multiple datasets 1 Pathway 1 2 Protein abundance space 3 1 1 Pathway 2 2 3 1 4 5 4 5 5 3 2 5 3 1 4 2 1 4 Gene expression space

25. Pathway 1 Pathway 2 Kinase 1 Kinase 2 Proteomics data TF TF Gene expression data Target 2 Target 1 Target 2 Target 1

26. Summary • New method allows concurrent analysis of expression and connectivity data • Differential response networks can distinguish functional differences missed by traditional statistical methods • They provide functional insights into mechanisms of compound action • The method could be extended to concurrent analysis of multiple data-types

27. Acknowledgements GeneGo: Zoltan Dezso Yuri Nikolsky Tatiana Nikolskaya Valery Kazandaev University of Michgan Ryan Welch FDA: A. Naito Jim Fuscoe Chris Melvin Yvonne Dragan