Masterseminar „A statistical framework for the diagnostic of meningioma cancer“

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

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

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

4. Introduction

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

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

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

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

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

10. SEREX – Results

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. Microarrays

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

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

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

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

17. Bayesian Approach class gene A gene 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 1 0 1 1 1 1 2 1 1 2 1 0 3 0 1 3 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

18. Bayesian Approach class gene A gene 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 1 0 1 1 1 1 2 1 1 2 1 0 3 0 1 3 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4 4 6 6 1 1 6 7 4 4 6 6 1 0 6 6

19. Bayesian Approach

20. Bayesian Approach class gene A gene 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 1 0 1 1 1 1 2 1 1 2 1 0 3 0 1 3 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 2 6 6 5 6 6 6

21. Bayesian Approach

22. Bayesian Approach

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

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

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

27. Dimension reduction 6 approaches • 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

28. Component analysis • Principal component analysis • Independant component analysis

29. Analysis of grades tissues genes

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

33. Split into chromosomes

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

35. SEREX derived genes

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

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

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

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