Wieslawa Mentzen, Matteo Floris and Alberto de la Fuente

1. Network oncology: Analysis of cancer gene expression in the context of protein interaction network and miRNA target motifs Wieslawa Mentzen, Matteo Floris and Alberto de la Fuente

2. Analysis of cancer gene expression in the context of protein interaction network and miRNA target motifs Problem Alternative approach to cancer gene expression analysis using PIN and miRNA target motifs Goal To outline processes and associated miRNAs that are involved in progression of cancer Approach -defining the ‘pathways’, or functionally coherent processes, as densely interconnected modules in Protein Interaction Network -identification of differentially expressed modules of PIN cancer-specific up- or downregulated processes -identification of differentially coexpressed modules processes dysregulated or invoked in cancer - analysis of miRNA targets overrepresented in DE and DC modules possible common regulatory themes that might be responsible for the observed patterns and differences in gene expression

3. Data Li et al. (2007) ETV6-NTRK3 Fusion oncogene initiates breast cancer from committed mammary progenitors via activation of AP1 complex. Cancer Cell 12, 542-558. mRNA expression data contains samples from three stages of the disease: healthy epithelial cells of a mouse mammary gland, hyperplastic cells expressing TV6-NTRK3 oncoprotein (marked blue due to fused lacZ promoter) and tumor cells. oncogene transformed cell WT cell tumor hyperplastic healthy mammary tumor, sorted 15 microarrays WT mammary gland, unsorted 5 microarrays hyperplastic, sorted 4 microarrays

4. Network modules as an alternative to ‘textbook’ pathways • Proteins heavily interconnected by a network of mutual interactions are likely to be involved in the same biological process. • Topology-based designation of modules is not constrained by existing framework of biological processes, and thus allows finding novel disease-specific modules • Especially useful in the study of cancer, since this disease proceeds through step-wise accumulation of defects in biological processes, whose nature is often not known.

5. Network modules as an alternative to ‘textbook’ pathways Mouse Protein Interaction Network (PIN) from IntNetDB (Xia et al., 2006 ) • Optimization of the network partition in the respect to the functional coherence of clusters. • Setting the granularity of the partition (controlled by inflation parameter in MCL) so as to maximize mutual information content between clustering and the Gene Ontology annotations, MI(C,A) Markov Clustering (MCL) van Dongen, 2000 Steuer et al., 2006 σrandom - standard deviation of the MI(C,A) in the randomized data network modules

6. expression Differentially expressed modules 5 WT samples 15 tumor samples • identification of the gene sets (modules) that are differentially expressed between these states (GSEA software, Subramanian et al., 2005 ) Comparisons: healthy (WT) – hyperplastic healthy (WT) - tumor hyperplastic – tumor WT tumor hyperplastic

7. healthy tumor hyperplastic UP in WT UP in WT heme biosynthetic process (71) cell adhesion (35) UP in hyperplastic lysosome (24) antigen processing and presentation (50) monooxygenase (60) oxidoreductase activity (72) dynein complex (88) integrin complex (40) protein transport (33) UP in tumor monooxygenase (60) blood vessel development (47) UP in hyperplastic apoptosis (32), H+ transport (22), receptor activity (57), rotamase (97) lipid biosynthetic process (51) antigen processing and presentation (50), integrin complex (40)

8. Differentially coexpressed modules Condition 1 (WT) Condition 2 (tumor) Condition 1 (WT) Condition 2 (tumor) Differential expression analysis explores the differences in gene mean expression level between conditions Changes in relationships among transcript levels within the same condition are not commonly explored Genes might be coexpressed in one condition and not in the other, changing their alliances according to the dynamically arising demands of the organism, that recruits and dissolves teams of coregulated genes for currently required tasks. We identified modules that lose or gain correlation during tumorigenesis with CoXpress package for R (Watson, 2006).

9. Differentially coexpressed modules WT h t WT h t Modules which are coexpressed in healthy cells, but lose their correlation in tumor cells, represent processes that are dysregulated in cancer. n c c n c n module 65 module 119 Modules that are coexpressed in cancer cells, but not in healthy ones, indicate processes invoked in cancer. c c n c n n module 92 module 51 Processes specifically activated or deactivated in hyperplastic cells. c n n c c n module 74 module 24 c: correlated n: non correlated

10. c: correlated n: non correlated

11. Dynamics of differential expression and differential coexpression 50, immune response 51, lipid biosynthesis 40, integrin complex 35, apoptosis WT hyperplastic tumor not correlated correlated Apoptosis and lipid biosynthesis processes are inactivated through combination of dysregulation and downregulation Integrin complex and immune system response are activated by coregulation and upregulation

12. Dynamics of differential expression and differential coexpression 24, hydrolase 33, protein transport 51, lipid biosynthesis 22, H+ transport 40, integrin complex 35, apoptosis WT hyperplastic tumor not correlated correlated Combined differential expression/ differential coexpression analysis offers additional insight into mechanism of modulating activity of processes.

13. Prediction of associated miRNA • Are small regulatory RNA (miRNA) responsible for observed changes in expression? • Calculation of the p-value for overrepresentation of miRNA targets (from miRBase Targets database, Griffiths-Jones et al., 2008) in modules • Scoring the miRNAs for the specificity of their association with differentially coexpressed modules, or with set of modules differentially expressed in a particular comparison. pc – p-values for overrepresentation of miRNAj in C differentially expressed modules pi – p-values for overrepresentation of miRNAj in all n modules • The significance of the S score has been assessed by comparison to the distribution of the similarly obtained S scores from random data.

14. Prediction of associated miRNA Differentially expressed modules: has-miR-200b targets genes in modules upregulated in tumor Indicated in several types of cancer and downregulated in breast cancer (Fang et al., 2008). Regulates epithelial-mesenchymal transition, a key step in the metastasis of epithelial-derived tumors (Bracken et al., 2008). Differentially coexpressed modules: module p-value miRNA 54 0 mmu-miR-183* 131 0.02 hsa-miR-642 42 0.02 mmu-miR-101a* 74 0 mmu-miR-433* 102 0.01 mmu-miR-325* No known association with cancer.

15. Summary • Network - guided designation of the pathways provides a useful way of delineating new disease-specific processes • Processes up- or down- regulated, as well as processes disordered or invoked in cancer, can be found with differential expression and differential coexpression analysis of network modules • The integrated differential expression-differential coexpression analysis allows for dissecting the dynamics of activation or deactivation of cellular processes • Particularly important in cancer, which develops through step-wise acquisition of abilities • Prediction of role in tumorigenesis for miRNAs that have not been previously associated with breast cancer

16. Thank you