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Masterseminar

„A statistical frameworkfor the diagnostic of meningioma cancer“

Chair for Bioinformatics, Saarland University

Andreas KellerSupervised by: Professor Doktor H. P. Lenhof

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Outline

IntroductionMaterials and MethodsSEREXMicroarrayConclusionDiscussion

Outline

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What are meningiomas

Benign brain tumors

Arising from coverings of brain and spinal cord

Slow growingMost common

neoplasm (brain)Genetic alterations

Introduction

4Introduction

5Introduction

meningioma in proportions

Two times more often in women as in menMore often in people older than 50 years

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

Outline

7SEREX

serological identification of antigens

by recombinant expression cloning

se

r ex

8SEREX – Identification

expression of a human fetal brain library pooled sera

2nd antibody detection

proteins bind on membrane

9SEREX – Screening

patients serum 2nd antibody detection

agar plate specific genes

10SEREX – Results

11Microarrays

System:cDNA microarrays55.000 spotsWhole Genome Array

Data:8 samples per WHO grade2 dura as negative controle2 refPools as negative controle

12Microarrays

13Statistical Learning

Supervised LearningBayesian StatisticsSupport Vector MachinesDiscriminant Analysis

Unsupervised Learning (Clustering)

Feature Subset Selection

Component Analysis (PCA, ICA)

14Statistical Learning

Crossvalidation

Error RatesTraining ErrorCV ErrorTest Error

Specificity vs. Sensitivity tradeoffReceiver Operating Caracteristic Curve

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

Outline

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Data situation:p = 57n = 104

SEREX

Goal:Predict meningioma vs. non meningiomaPredict WHO grade

17Bayesian Approach

1 0 11 1 12 1 12 1 03 0 13 1 00 0 00 0 00 1 00 0 00 0 00 0 0

class gene A gene B

serum 1serum 2serum 3serum 4serum 5serum 6serum 7serum 8serum 9serum 10serum 11serum 12

18Bayesian Approach

1 0 11 1 12 1 12 1 03 0 13 1 00 0 00 0 00 1 00 0 00 0 00 0 0

class gene A gene B

serum 1serum 2serum 3serum 4serum 5serum 6serum 7serum 8serum 9serum 10serum 11serum 12

4 46 61 06 6

4 46 61 16 7

19Bayesian Approach

20Bayesian Approach

1 0 11 1 12 1 12 1 03 0 13 1 00 0 00 0 00 1 00 0 00 0 00 0 0

class gene A gene B

serum 1serum 2serum 3serum 4serum 5serum 6serum 7serum 8serum 9serum 10serum 11serum 12

2 26 65 66 6

21Bayesian Approach

22Bayesian Approach

23SEREX Conclusion

Separation meningioma vs. non meningioma seems very well possible

Separation into different WHO grades seems to be possible with a certain error

24SEREX Conclusion

Extend to otherBrain tumors (glioma)Human cancerDisease

Simplify experimental methods

Develop a prediction system

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

Outline

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Data situation:p = 53423n = 26

Microarray

2 goals:Find significant genesClassify into WHO grades

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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

28Component analysis

Principal component analysis

Independant component analysis

29Analysis of grades

genes

tissues

30Dura and refPool

Justification for DuraWherefrom to take?How to take?Genes different from normal tissueGood to classify into meningioma vs. healthy

Justification for refPoolGenes different between WHO gradesGood to classify into grades

31Published 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

32Split into chromosomes

As mentioned: often karyotypic alterations

=> Split genes into different chromosomes

=> Compare to karyotype

losses:221p6q10q14q18q

gains:1p9q12q15q17q20q

33Split into chromosomes

34Classification

Classification:

ClusteringSVMDiscriminant AnalysisLeast Squares

35SEREX derived genes

36BN++

BN++ as a statistical tool

Build a C++/R interface??Use MatLab??Use C++ librarys??

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

Outline

38Workflow

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.

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

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Introduction

Materials and Methods

SEREX

Microarray

Conclusion

Discussion

Outline

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