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Statistical Framework for Meningioma Cancer Diagnostic

Explore statistical methods for detecting meningioma tumors. Analyze SEREX and microarray data. Study genetic alterations and use Bayesian statistics. Develop prediction models for tumor classification.

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Statistical Framework for Meningioma Cancer Diagnostic

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  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

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