급성백혈병에서의 Gene Expression Profiling

1. 급성백혈병에서의 Gene Expression Profiling 포천중문의대 분당차병원 혈액종양내과 정 소 영

2. Backgroud • Many stages of normal hematopoietic differentiation -> many biologically and clinically distinct cancers • Acquired genetic alterations - chromosome translocations - mutations - deletions • Alterations of gene - activation of critical pathways - changes expression of other genes

3. Backgroud • Diagnosis of Leukemia - Morphology - Chromosome - Cell marker - Molecular markers: limited No. • Markedly heterogeneous Response to Tx in some diagnostic categories -> Molecularly distinct disease w/i the same category

4. Gene Expression Profiling • Abundance of mRNA of each gene - Cell lineage - Stage of differentiation - Activity of intracellular regulatory pathways - Influence of the extracellular stimuli • Gene expression: major determinant of biology of normal or cancer cells

5. Gene Expression Profiling • Gene expression profiling • Microarray chip: cDNA chip, oligonucleotide chip robotically printed probes on solid surfaces - Relatively small No. of sample Large No. of mRNAs at a time - Effective method to study diversities

6. Microarray processing sequence

7. Analytical technique Unsupervised analysis • Using pattern-recognition algorithm - Clustering or class discovery according to similar gene expression patterns - Hierarchial clustering -> dendrogram(tree view) • Descovery of subgroups of tumors

8. Supervised analysis - Correlation with clinical data(survival..) - Identify genes w/ patterns of expression that predict the clincal outcome - Class prediction : distinction between known phenotype.. Gene A Gene B Tumor biopsy specimen

9. Statistical significance Validation of results - Essential - Separate sample set - Sample-> training set & validation set training set -> classifier -> validation set - Validation using independent sample set - Accuracy

10. Chromosomal abnormalites Most important prognostic factor Intensive consolidation or HSCT according to cytogenetic risk groups Substantial heterogeneity within these risk groups Intermediate risk group (normal karyotype) 1/2 of patients Various treatment response, prognosis GEP in Acute Myelogenous Leukemia

12. Other genetic abnomalities - tyrosine kinase: FLT3, c-KIT, N-RAS - transcription factor: AML1, GATA1, CEBPA, EVI1 - FLT3 ITD: MC. molecular abnormality in AML - FLT3, MLL mutation, EVI1 overexpression: poor Px Need for refined AML classification More accurate prognostic marker GEP in AML

13. GEP in AML Valk PJM et al. N Engl J Med, 2004 • 285 primary AML pts, PB or BM • Oligonucleotide array (Affymetrix U133A GeneChip) 13,000 genes • Biotin- labeled cDNA • Data analysis: significance analysis prediction analysis software • To identify establised or novel subclasses of AML • To determine prognostic significance of clusters

14. Pearson’s correlation view Red: positive pairwise correlation Blue: negative pairwise correlation

15. FLT3 TKD FAB Karyotype FLT3 ITD N-RAS K-RAS EVI1 Cluster No. CEBPA Adapted correlation view Adapted correlation view

16. Level of expression of top 40 genes that characterized each clusters Sp. w/ normal Karyotype(%) Sp. w/ the Abn.(%) Cluster No. MC Abn. Genes   Patients with AML CD34+ NBM

17. Valk PJM et al, N Engl J Med, 2004 • Unsupervised cluster analysis 16 clusters • Clustering was driven by • Chromosomal lesion • Particular genetic mutations (CEBPA) • Abnormal oncogene expression (EVI1) • Several novel clusters, nSome consisting of normal karyotype

18. t(8;21) t(8;21) Inv(16) Inv(16) t(15;17) t(15;17) M4,5 M4,5 EVI1 EVI1 Survival rate of 5 most common unsupervised clusters Valk et al, N Engl J Med, 2004

19. Valk PJM et al, N Engl J Med, 2004 Conclusion: • GEP allows a comprehensive classification of AML • that includes previously genetically defined subgroups • and a novel cluster with an adverse prognosis Prognostically useful GEP in AML

20. GEP in AML Bullinger et al. N Engl J Med, 2004 • 116 AML pts, PB or BM • Cytogenetics, FISH, analysis of FLT3, MLL • cDNA microarray (26,260 genes) • Cy5- AML sample, Cy-3 reference sample • To identify molecular subgroups • To devise a gene-expression based clinical outcome predictor

21. Unsupervised clustering Bullinger, N Engl J Med, 2004

22. Distribution of prognostically relevant clinical & molecular genetic variables Male >60 ≥100,000 LDH >400 secondary FAB FLT3 mut. MLL PTD Bullinger, N Engl J Med, 2004

23. Group I, II; Similar clinical variables Group I: more FLT3 mutation (p=0.005) more M1, M2 Group II: more M4, M5 (p=0.013) Normal karyotype

24. Biologic insights

25. Outcome prediction 133 gene clinical outcome predictor

26. Normal karyotype AML 2 groups based on GEP Different outcomes Unequal distribution of FLT3 mutation & FAB subtype • t(8;21), inv(16) Separate into different subgroups  Cooperative mutations and dysregulation • Homeobox-gene dysregulation across diverse cytogenetic groups  coregulated pathway with pathogenetic relevance • GEP -> improve mol. Classification & outcome prediction

27. GEP in ALL Yeoh et al, Cancer cell, 2002 • 360 childhood ALL • Unsupervised clustering -> 6 known clinical subtypes - T-cell ALL, E2A-PBX1, BCR-ABL - TEL-AML1, MLL rearrangement - Hyperdiploid ( > 50 chromosome) -> Novel subgroup • Significantly different GEP • Robust classifier w/ 95% accuracy • Predict relapse w/i certain subgroups

28. GEP can group virtually all cases of T-ALL

29. GEP in ALL Ferrando, Cancer cell, 2002 T-cell ALL • HOX11 overexpression cases - w/ or w/o Chromosomal translocation - Common gene-expression signature - Biological similarity • HOX11(+) T-ALL - Good prognosis w/ or w/o Ch’ translocation • GEP is more useful than karyotyping

30. GEP in ALL Willenbrock et al, leukemia, 2004 Prediction of immunophenotype, Tx respons, and relapse • 45 childhood ALL, 8763 genes • Aim: prediction of relapse & classification of subtypes • GEP at diagnosis - Prediction of relapse (78% classification accuracy, 87% sen. 60 spec.) - Precursor-B ALL: Prediction of level of MRD at D29 high( > 0.1%) or low( < 0.01%) (100% accuracy)

31. GEP in ALL Teuffel et al, haematologica, 2004 GEP and risk stratification in childhood ALL • GEP at diagnosis • 31 ALL BM • GEP –current risk assessment (cytogenetics, in vivo initial response) • Oligonucleotide microarray • Screening for risk group associated genes - 106 discriminating probe sets

32. Ribosomal protein Cyclin H LRDD/Pidd

33. Risk group prediction by selected signature genes

34. GEP in ALL, Drug resistance Prognosis of ALL • MDR, cell cycle, DNA repair, drug metabolism, apoptosis gene • Chemosensitivity & role of these genes ? Holleman et al, N Engl J Med, 2004 • 173 childhood ALL blasts • In vitro sensitivity test: PD, VCR, Asp, DNR • GEP with 14,500 probe set • GEP according to drug sensitivity or resistance • Combined drug-resistance gene-expression score

35. Holleman, N Engl J Med, 2004

36. Supervised hierarchial clustering Sample PD VCR Gene

37. Supervised hierarchial clustering Sample ASP DNR Gene

38. DFS according to combined drug-resistance gene-expression score Validation group Study group Holleman, N Engl J Med, 2004

39. Use of GEP in Acute Leukemia

40. Problems of Microarray Technique

41. Application in clinical diagnostics