1. Distinguishing Regulators of Biomolecular Pathways Mentor: Dr. Xiwei Wu City of Hope Sean Caonguyen SoCalBSI 8/21/08

2. Expression Pattern Analysis Microarray technology is a powerful tool for investigating cellular activity at different levels DNA microarrays can be used to identify genetic ‘‘signatures’’ for disease http://www.sciencedaily.com/images/20 07/09/070912102212.jpg Pan et al. (2005)

3. A Traditional Approach to DNA Microarray Analysis Gene Expression Data Gene Selected Biological Interpretation Threshold Individual Gene Analysis Two step process Selects genes from an arbitrarily chosen cut-off From the selected genes, one infers biological meaning of gene expression data Jiang Z and Gentlemen R. (2006) and Nam D, et al. (2007)

4. Emerging Approach to DNA Microarray Analysis Gene Set Analysis (GSA) Rank all genes based on their phenotype association Calculate a maximal enrichment score for each gene set Rank each gene set score for biological interpretation Gene Expression Data Gene Set Database Biological Interpretation Assess gene set directly Jiang Z and Gentlemen R. (2006) and Nam D, et al. (2007)

5. Biological Significance of Gene Set Analyses Ability to identify subtle changes in gene expression that are undetectable by traditional approaches No arbitrary threshold Generate results that are easier to interpret

6. Current Problem with GSA Reduces gene set into a list of names No difference in up- regulation and down- regulation Directionality is lost A B D E F C P G A D F P E B Suggests that the pathway is activatedSuggests a lower probability of pathway activation HIGHER up-regulation down- regulation up-regulation

7. Enriched Gene Set Analysis Gene Set Database Biological Interpretation Assess gene set directly Curated Analysis Gene Expression Data

8. Useful Tools for the Pathway Analysis Program National Cancer Institutes (NCI) Pathway Interaction Database (http://pid.nci.nih.gov/PID/index.shtml)http://pid.nci.nih.gov/PID/index.shtml contains information about molecular interactions and biological processes in signaling pathways focuses on cancer research in human cells searches for biomolecules, processes, or by viewing pathways Data format Graphics: SVG or GIF Texts: XML or BioPax

9. Segment of the Phosphoinositide 3- Kinases (PI3K) Signaling Pathway Key to Icons non-lipid kinase pathway of Class IB PI3K XML Script

10. Project Objective Create a program to distinguish the activators and inhibitors in each signaling pathway Requires extensive use of XML Parser in Python

11. Approach to Project 1. Identify all the elements in the pathway 2. Record the pairwise interactions Linking each interaction 3. Determine the role of each molecule Finding each leaf node Using a traceback method A B D E F C P G

12. 1) Identify the Elements in the Pathway Properly assign each ID to reference a “preferred symbol” Locate each interaction ID

13. 2) Record the Pairwise Interactions How to can we store each interaction? Memory efficient Easy extraction of data A B D E F C P G Sparse Matrix!

14. Sparsing Matrix Initialization A B D E F C P G ABCDEFGP A00000000 B10000000 C00000000 D01100000 E0000000 F00010000 G0000 000 P00000100 Sparse Matrix Regulators Output 1 1 1 1 1

15. 3) Determine the Role of Each Molecule A B D E F C P G ABCDEFGP A00000000 B10000000 C00000000 D01100000 E0000000 F00010000 G0000 000 P00000100 Regulators Output Traceback each leaf node 1 1 1 1 1 Leaf Node P RoleActivatorInhibitor ProteinA,B,C,D,F Identify each leaf node Leaf Node G ActivatorInhibitor A,BE

16. Locate Activated Pathways for Better Biological Interpretation Gene Expression Data Up-regulation of B and D Down-regulation of E Enriched Gene Set Analysis Leaf Node PLeaf Node G RoleActivatorInhibitorActivatorInhibitor ProteinA,B,C,D,FA,BE Possible activation of Pathway A B D E F C P G D E B down- regulation up-regulation

17. Results For each pathway menu, one can: find a list of proteins with associated roles for each node look at each protein in an interaction find a list of all interactions in a pathway

18. Percentage of Inhibitors Number of PathwaysPercentage 0%5546.6% 0-5%3832.2% >=5%2521.2% >=10%86.8% >=20%21.7% Total118100%

19. Conclusion Successfully parse XML files Pathway analysis program works ~50% of pathways include inhibitors 20% of the pathways contains >=5% of inhibitors Average total molecules = 60

20. Future Directions Improvements to Software Ambiguous roles Proteins in different Complex may have different roles Fine tune the overall role of proteins in each pathway Run program with real expression data set Improve prognoses and drugs for diseases A B D E F C P G

21. References Pan KH, Lih Cj, Cohen SN. Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. Proc Natl Acad Sci 2005, 102:8961-5. Subramanian A, Tamayo P, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 2005, 102:15545-50. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform 2008, 9:189-97. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007, 99:147-57. Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007,23:306-13. Dinu I, Potter JD, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics 2007, 8:242. Liu Q, Dinu I, et al. Comparative evaluation of gene-set analysis methods. BMC Bioinformatics 2007,8:431.

22. Acknowledgements Mentor Xiwei Wu SoCalBSI Faculty and Staff Jamil Momand Sandy Sharp Nancy Warter-Perez Wendie Johnston Funding for SoCalBSI: DOE and NASA LA / Orange County Biotechnology Center NSF, NIH, and Economic & Workforce Development Funding at City of Hope: National Cancer Institute National Institute of Health