1. Pathway Ranking Tool Dimitri Kosturos Linda Tsai SoCalBSI, 8/21/2003

2. Project Overview BioDiscovery, Inc. at Marina del Rey BioDiscovery, Inc. Analyzing microarray data on pathway level instead of individual gene level Methods: -Enrichment Analysis -Permutational Statistics -S. Metric -Multivariate test Project Overview

3. Validation of statistical methods 2 data sets: Brain Tumor, Interferon-gamma. Sources of annotation: BioCarta, Kegg, Gene Ontology. Project Overview, cont.

4. phenotype microarrayalgorith m pathway Dimitri, (Computer Scientist) Linda (biologist) Project Flowchart

5. GeneSight is a data analysis software Feature: -Statistical significance testing -Multiple Data Visualizations -Automated gene annotation -Complete result reports -Pathway analysis (?) Research and Development in GeneSight

6. Glioblastoma multiforme(GBM) is the most malignant of the glial tumors, classified as grade IV. Many brain tumors are currently incurable. Average survival time: 1 year Biology of Brain Tumor

7. Oncogenes: promote normal cell growth Tumor suppressor genes: retard cell growth http://www.med.harvard.edu/publications/On_The_Brain/Volume4/Number2/SP95Awry.html Bad Genes Foment Trouble

8. Interferon is a class of cytokines that mediate antiviral, antiproliferative, antitumor activites, etc. IFN gamma is produced by T lymphocytes in response to mitogens or to antigens. IFNs bind to their receptors and initiate JAK- STAT signaling cascade. Biology of Interferon

9. http://www.grt.kyushu-u.ac.jp/eny-doc/pathway/ifn_gamma.html Biology of Interferon, cont.

10. Grouping related genes together into pathways (A)BioCartaBioCarta Ex: p53 Signaling Pathway (B)KEGGKEGG Ex:Citrate cycle (TCA cycle) Grouping genes into structured, controlled vocabularies (ontologies) Gene Ontology -Biological Process. Ex: angiogenesis, apoptosis -Molecular Function. Ex: DNA binding activity -Cellular Component.Ex: nucleus, mitochondria Gene Annotations

11. Traditional method of ranking gene pathways Steps: 1. Mann-Whitney Test: obtain list of probe sets that satisfy a certain p-value. 2. Cluster analysis: see how many of listed probe occur in a cluster (pathway). Example: 1. Original data: 12,625 genes. Select genes p-value <0.001. =>narrow to 927 genes. 2.Cluster those 927 genes into clusters.

12. 4 of the genes in SODD/TNFR1 Signaling Pathway satisfy p-value<0.001 Mann-Whitney Test, Denovo Glioblastoma p<0.001

13. How Affy. Microarray Chips Work http://www.ucl.ac.uk/oncology/MicroCore/HTML_resource/Norm_Affy1.htm Best results: Genes hybridize perfectly with Perfect Match, and not at all with Mismatch. PM: Perfect Match MM: Mismatch

14. Example of GeneSight PlotData Normal Tumor Probe Set A4.53.810.211.1 Probe Set B2.32.713.513.6 Probe Set C7.88.21.41.8 Probe Set A3.54.28.99.6 Theoretical Tumor Expression Levels (Log Transformed) Conditions Genes Notice column replicates, Probe Set replicates.

15. Given Data Sets Given two data sets: Brain Tumor, IFN- γ Brain Tumor Data Set has 5+ tumor types,however, only 2 Tumor types were used (Denovo Glioblastoma, Progressive Glioblastoma) IFN- γ Data Set: the entire data set was used.

16. What and why? Goal: write a prototype extension to GeneSight that uses permutational statistics to develop a custom distribution for a given Microarray data set. Overall significance: the software provides a list of (potentially) significant pathways that enables researchers to focus their work.

17. What is permutational statistics? EECC 1234 Choose different Control and Experiment groupings (permute). ECEC 1234 By iterating through an adequate number of permutations, we can determine if a pathway is likely to be significant (p-value). (In this context.)

18. Permutational Stats. There are two versions of the S. Metric currently implemented. S. Metric I = S. Metric II = M = Number of Genes flagged as significant Total = Total number of Genes in the Pathway

19. (Layman's) How Statistics Works DataStatisticP-Value Permute Here S. Metric I, II After all permutations are done, calculate the p-Value

20. Algorithm Take at least 10,000 unique permutations. A unique permutation is determined by a Permute class. For each condition For each permutation For each gene Calc. Mean diff. Calc. T-stat End For For each pathway store the statistic End for End for calcPvalue(stored statistic) End For S. Metric Initial Significance Flagging pValue

21. Limitations Computational Power (Memory, CPU) Required number of replicates (8,8)

22. Output of result

23. Validation of pathway analysis Method 1 Problem: lack of insignificant pathways ????

24. Validation of pathway analysis Method 2 Best algorithm Random Worst Comparision of Prediction Methods 0 2 4 6 8 10 12 14 16 16 111621263136414651566166717681869196 # of Pathways in BioCarta sorted by P-value # of identified significant pathways

25. Result Brain Tumor-BioCarta

26. Result IFNG-Molecular Function (GO)

27. Biological Limitations Prediction of pathways to be significant in the conditions of interest is subjective. Assumption of similar biological states between Denovo Glioblastoma and Progressive Glioblastoma.

28. Future Direction Finish modifying the Multivariate Statistic for use in the permutational method. This method uses PCA and Multivariate statistics. Finish Validating the data produced using the Multivariate Statistic.

29. Initial Results of Multivariate Stat. Sorted by p-value.

30. Conclusion It is not clear which is better the S. metric or traditional Enrichment Analysis. Improvements can be made to the S. metric.

31. Acknowledgements Dr. Bruce Hoff Dr. Anton Petrov SoCalBSI: Dr. Jamil Momand, Dr. Sandra Sharp, Dr. Nancy Warter-Perez, Dr. Wendie Johnston National Science Foundation National Institute of Heath