1. 1 Masterseminar „A statistical framework for the diagnostic of meningioma cancer“ Chair for Bioinformatics, Saarland University Andreas Keller Supervised by: Professor Doktor H. P. Lenhof

2. 2 Outline Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

3. 3 What are meningiomas  Benign brain tumors  Arising from coverings of brain and spinal cord  Slow growing  Most common neoplasm (brain)  Genetic alterations Introduction

4. 4

5. 5 meningioma in proportions Two times more often in women as in men More often in people older than 50 years

6. 6 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

7. 7 SEREX serological identification of antigens by recombinant expression cloning se r ex

8. 8 SEREX – Identification expression of a human fetal brain library pooled sera 2nd antibody detection proteins bind on membrane

9. 9 SEREX – Screening patients serum 2nd antibody detection agar plate specific genes

10. 10 SEREX – Results

11. 11 Microarrays System:  cDNA microarrays  55.000 spots  Whole Genome Array Data:  8 samples per WHO grade  2 dura as negative controle  2 refPools as negative controle

12. 12 Microarrays

13. 13 Statistical Learning Supervised Learning  Bayesian Statistics  Support Vector Machines  Discriminant Analysis Unsupervised Learning (Clustering) Feature Subset Selection Component Analysis (PCA, ICA)

14. 14 Statistical Learning Crossvalidation Error Rates  Training Error  CV Error  Test Error Specificity vs. Sensitivity tradeoff  Receiver Operating Caracteristic Curve

15. 15 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

16. 16 Data situation:  p = 57  n = 104 SEREX Goal:  Predict meningioma vs. non meningioma  Predict WHO grade

17. 17 Bayesian Approach 101111211210301310000000010000000000101111211210301310000000010000000000 classgene Agene B serum 1 serum 2 serum 3 serum 4 serum 5 serum 6 serum 7 serum 8 serum 9 serum 10 serum 11 serum 12

18. 18 Bayesian Approach 101111211210301310000000010000000000101111211210301310000000010000000000 classgene Agene B serum 1 serum 2 serum 3 serum 4 serum 5 serum 6 serum 7 serum 8 serum 9 serum 10 serum 11 serum 12 46 44661066446610666 461 4466116744661167

19. 19 Bayesian Approach

20. 20 Bayesian Approach 101111211210301310000000010000000000101111211210301310000000010000000000 classgene Agene B serum 1 serum 2 serum 3 serum 4 serum 5 serum 6 serum 7 serum 8 serum 9 serum 10 serum 11 serum 12 26 22665666226656666

21. 21 Bayesian Approach

22. 22 Bayesian Approach

23. 23 SEREX Conclusion Separation meningioma vs. non meningioma seems very well possible Separation into different WHO grades seems to be possible with a certain error

24. 24 SEREX Conclusion Extend to other  Brain tumors (glioma)  Human cancer  Disease Simplify experimental methods Develop a prediction system

25. 25 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

26. 26 Data situation:  p = 53423  n = 26 Microarray 2 goals:  Find significant genes  Classify into WHO grades

27. 27 Component analysis Take genes which differ from DURA Take genes which differ from refPool Take genes which differ between grades Take „publicated“ genes Split into chromosomes Dimension reduction 6 approaches

28. 28 Component analysis Principal component analysis Independant component analysis

29. 29 Analysis of grades genes tissues

30. 30 Dura and refPool Justification for Dura  Wherefrom to take?  How to take?  Genes different from normal tissue  Good to classify into meningioma vs. healthy Justification for refPool  Genes different between WHO grades  Good to classify into grades

31. 31 Published genes Several 100 genes are connected with meningioma in several publications Find these genes and investigate them example: Lichter 2004 – 61 genes with different expression WHOI in contrast to WHOII and III

32. 32 Split into chromosomes As mentioned: often karyotypic alterations => Split genes into different chromosomes => Compare to karyotype losses:  22  1p  6q  10q  14q  18q gains:  1p  9q  12q  15q  17q  20q

33. 33 Split into chromosomes

34. 34 Classification Classification:  Clustering  SVM  Discriminant Analysis  Least Squares

35. 35 SEREX derived genes

36. 36 BN++ BN++ as a statistical tool  Build a C++/R interface??  Use MatLab??  Use C++ librarys??

37. 37 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

38. 38 Workflow Large scale investigation of suspicious people by antigen analysis. If a positive prediction is made do further analysis (CT or similar). If necessary surgory. Further examinations with the gained tissue.

39. 39 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline

40. 40 Introduction Materials and Methods SEREX Microarray Conclusion Discussion Outline