Differentiation Arrest and Leukemogenesis

1. Differentiation Arrest and Leukemogenesis Group Meeting Dvir Netanely - June 21st, 2005

2. Normal Blood Cells

3. Normal Blood Cells • The bone marrow produces stem cells (immature cells) that develop into mature blood cells. • There are 3 types of mature blood cells: • White blood cells (leukocytes) are part of the immune system. • Red blood cells (erythrocytes) carry oxygen from the lungs to the body's tissues. • Platelets (thrombocytes) form blood clots that control bleeding. • Normally, blood cells are produced in an orderly, controlled way as the body needs them. This process is called hematopoiesis.

4. Hematopoietic Differentiation

5. Hematopoietic Differentiation

6. Hematopoietic Differentiation • Hematopoietic stem cells in the bone marrow can either self-renew or give rise to progenitor cells that generate precursors of the myeloid or the lymphoid lineage. • The commitment process is characterized by massive cell proliferation in the early phase followed by successive restriction to distinct cell lineages and to cell differentiation. • These processes are regulated by trans-acting factors which activate or repress genes • Leukemic mutations interfere with transcription factor functions, abrogate cell differentiation, and support proliferation. As a consequence, the blood is flooded with immature, non-functional cell types. http://www.mdc-berlin.de/englisch/research/research_areas/cancer/leutz.htm

7. Leukemia • The term leukemia refers to cancers of the white blood cells. • Leukemia is a very heterogeneous disease, composed of many subtypes.

8. Leukemia – Acute vs. Chronic • In general, leukemias are classified into acute (rapidly developing) and chronic (slowly developing) forms.

9. Leukemia – ALL vs. AML • Leukemia is also divided by which type of white blood cell is affected: ALL(Acute Lymphoid Leukemia) vs. AML(Acute Myeloid Leukemia). • AML is more difficult to treat in comparison to ALL, overall cure rates for AML remain below 60%.

10. Acute Leukemias - Statistics • Combining childhood and adult cases, there are 11,000 new cases per year in the U.S.A. • Overall, acute leukemia strikes 5 out of 100,000 people each year. • If untreated, 95% of patients will die within one year of diagnosis. • Acute leukemia is the most common cancer of childhood. • AML is 5 times more common than ALL but ALL represents 85% of cases in children. • Thus, the average ALL patient is 4 years old while the average AML patient is 60 years old.

11. AML • Acute myeloid leukemia (AML)is a cancer of the myeloid line of white blood cells. • The malignant myeloid cells, called myeloblasts, fail to mature into the different types of blood cells. • The myeloblasts proliferate rapidly, accumulate and overtake the number of healthy blood cells, spreading into the bloodstream and other vital organs. The lack of healthy blood cells results in symptoms such as anemia and abnormal bleeding.

12. Leukemia subtypes Leukemia Acute Chronic Myeloid Myeloid Lymphoid Lymphoid AML (ALL) (CML) (CLL) FAB: M0 M1 M2 M3 M4 M5 M6 M7

13. FAB classification system for AML subtypes • Acute myelogenous leukemia have been divided into 8 subtypes, M0 through to M7 under the FAB (French-American-British) classification system based on the type of cell from which the leukemia developed and degree of maturity. • This is done by examining the appearance of the malignant cells under light microscopy or cytogenetically by characterization of the underlying chromosomal abnormality. • Each subtype is characterized by a particular pattern of chromosomal translocations and have varying prognoses and responses to therapy.

14. FAB classification system for AML subtypes • The eight different subtypes are: • M0 (undifferentiated AML) • M1 (myeloblastic, immature) • M2 (myeloblastic, mature) • M3 (promyelocytic), or acute promyelocytic leukemia (APL) • M4 (myelomonocytic) • M5 (monocytic) • M6 (erythroid) • M7 (megakaryoblastic)

15. FAB Classification

16. FAB Classification

17. AML subtypes M2 M3 M5 M1 t(9;22) t(8;21) t(15;17) t(9;11) AML1-ETO PML-RARα acute promyelocytic leukemia FAB, Translocation and Fusion Proteins “Acute myelogenous leukemias (AMLs) are genetically heterogeneous and characterized by chromosomal rearrangements that produce fusion proteins with aberrant transcriptional regulatory activities.” Myriam Alcalay et. al., 2003 www.med-ed.virginia.edu/. ../wcd/myeloid1.cfm

18. Genetic Abnormalities

19. Genetic Abnormalities Chromosomal Translocation

20. Major Prognostic AML Sub types “Chromosomal translocations resulting in specific fusion genes are a hallmark of the leukemias” Z Xiao et. al., Leukemia (2001) 15, 1906–1913

21. t(15;17) – [PML-RAR] KNOW THE SUBTYPES ! • promyelocytic leukemia–retinoic acid receptor • This fusion PML-RAR protein is responsible for preventing immature myeloid cells from differentiating into more mature cells. “Chromosomal translocations resulting in specific fusion genes are a hallmark of the leukemias” Z Xiao et. al., Leukemia (2001) 15, 1906–1913

22. t(8;21) - [AML1-ETO] • The AML1 gene encodes the DNA-binding subunit of the AML1/CBFb core binding factor transcription complex, whereas ETO encodes the mammalian homologue of the Drosophila protein Nervy. • AML1 and ETO are both involved in transcriptional regulation of genes in hematopoietic precursor cells. • AML1-ETO fusion protein represses genes whose transcription is normally activated by AML/CBFb. British Journal of Haematology, 1999, 106, 296±308

23. t(8;21) - [AML1-ETO]

24. Inv16 - [CBF-MYH11] • core-binding factor – smooth muscle myosin heavy chain • The fusion protein blocks transcription of differentiation control genes.

25. The AML1-CBFß Transcription Factor In normal cells, heterodimeric AML1-CBFß transcription-factor complex binds to the DNA sequence TGTGGT in the transcriptional regulatory region of AML1-regulated target genes and activates transcription through the recruitment of coactivators.

26. The AML1-CBFß Transcription Factor • In AML cells with the t(8;21) translocation, the N-terminal part of AML1 fuses with the C-terminal portion of ETO. • The resultant chimeric protein continues to interact with CBFß and to bind to the core enhancer sequence; however, ETO recruits a nuclear corepressor complex and results in the dominant repression of AML1-regulated target genes.

27. The AML1-CBFß Transcription Factor • Similarly, the CBFß-MYH11 chimeric protein encoded by the inv(16) mutation continues to interact with AML1; however, instead of allowing AML1 to interact with DNA, this chimeric protein recruits AML1 into functionally inactive complexes in the cytoplasm.

28. MLL fusion genes • Mixed-lineage leukemia (MLL) fusion proteins. • There are more than 40 proteins that have been found fused to MLL in leukemia patients, and different ones can cause leukemia by different mechanisms. • When the transcription factor MLL functions as it should, without a fusion partner, it binds to and controls the expression of Hox genes, which in turn control cell growth and maturation.

29. Proliferation Legend: (A) In a normal resting cell the intracellular signaling proteins and genes that are normally activated by extracellular growth factors are inactive. (B) When the normal cell is stimulated by an extracellular growth factor, these signaling proteins and genes become active and the cell proliferates. (C) In this cancer cell, a mutation in a proto-oncogene that encodes an intracellular signaling protein that is normally activated by extracellular growth factors has created an oncogene. The oncogene encodes an altered form of the signaling protein that is active even in the absence of growth factor binding.

30. Cooperating mutations in acute leukemia • No single mutation is sufficient to cause acute leukemia. • Accumulating experimental and epidemiologic evidence suggests a model of cooperation between two classes of mutations in acute leukemia: • Mutations that confer a proliferative and/or survival benefit to hematopoietic progenitors but does not affect differentiation. • Mutations that impair hematopoietic differentiation. • Acute leukemia, characterized by enhanced proliferation and survival of cells and impaired differentiation, is the consequence of expression of both classes of mutations. Tallman et. Al., Focus on acute leukemias, Cancer Cell, 2002

31. Pathogenesis and treatment of acute leukemias • As indicated by the yellow star, intensive cytotoxic chemotherapy remains the mainstay of treatment for all acute leukemias. • Good prognosis leukemias are indicated in blue, poor prognosis leukemias are in red, and intermediate or unknown are in white. • There are two classes of cooperating mutations in acute leukemia, those that confer proliferation and/or survival and those that impair hematopoietic differentiation. • Targeted therapies have been developed or are being tested for many of these, such as ATRA. Tallman et. Al., Focus on acute leukemias, Cancer Cell, 2002

32. Pediatric Leukemia • Leukemia is the most common childhood cancer (25% of all childhood cancers in the US are leukemias). • Approximately 60% of children with leukemia have ALL(Acute Lymphoid Leukemia), and about 38% have AML(Acute Myeloid Leukemia).

33. Gene expression profiling of pediatric acute myelogenous leukemia Blood, 1 December 2004, Vol. 104, No. 12, pp. 3679-3687 Mary E. Ross, RamiMahfouz, MihaelaOnciu, Hsi-Che Liu, Xiaodong Zhou, Guangchun Song, Sheila A. Shurtleff, Stanley Pounds, Cheng Cheng, Jing Ma, Raul C. Ribeiro, Jeffrey E. Rubnitz, Kevin Girtman, W. Kent Williams, Susana C. Raimondi, Der-CherngLiang, Lee-Yung Shih, Ching-Hon Pui, and James R. Downing

34. Pediatric AML subtypes • The reviewed paper focuses on utilizing gene expression technology to identify sub-types of Pediatric Acute Myeloid Leukemia (AML).

35. Gene expression profiling of pediatric acute myelogenous leukemia • Motivation: • Identification of pediatric AML subtypes based on gene expression profiles. • Seek insights regarding the underlying biological process of each subtype. • Sub type identification will enable the development tailored treatment protocols customized to a certain genetic lesion which will hopefully significantly improve AML patient cure rates.

36. Samples (Patients) • 150 samples used: • 130 pediatric • 20 adult Major Prognostic sub types

37. Unsupervised cluster analysis of pediatric AMLs “..relatively tight grouping was observed for the genetic subgroups AML1-ETO, PML-RAR , and MLL chimeric fusion genes, and for the morphologic subgroups FAB-M3, M7, and M4/M5. Unexpectedly, however, AMLs that expressed the inv16-encoded CBF-MYH11 failed to cluster … indicates significant heterogeneity within the gene expression profile of these cases.”

38. Expression profiles of pediatric AMLs • Looking for subtype expression signatures using Supervised analysis – Trying to find genes that discriminate between subtypes. • Applying SAM with FDR=5% yielded only 63 discriminating genes for CBF-MYH11.

39. Expression profiles of pediatric AMLs Hierarchical clustering of the top 50 discriminating genes for each subtype – Some clusters are more distinct than others.

40. Similarity plot • Pair-wise comparisons calculated for 130 pediatric AML samples using the top 50-ranked genes for each subgroup as selected by SAM. CBFb-MYH11,MLL Heterogeneity among genes “Observed variation could not be completely explained by differences in the structure of chromosomal rearrangements, extent of differentiation, or presence of specific secondary mutations”

41. Examination of the discriminating genes • Gene annotation may provide biological insights. • Discriminating genes may be used as therapeutic targets, or as unique class-specific diagnostic targets.

42. AML subtype-specific class discriminating genes Representatives of genes significantly characterizing one specific subtype

43. Building a subtype classifier • Randomly divided the samples to TRAINING and TEST sets. • TRAINING set was used to train a neural network in classifying AML subtypes, based on gene expression data for the 250 discriminating genes identified by SAM. • Testing the classifier on the test set, overall Prediction accuracy of 93% achieved (100% on non-MLL samples).

44. Applying the classifier on Adult samples • The 20 adult samples were used to test the classifier, and yielded overall prediction accuracy of 90%. • ->Pediatric and adult samples are similar for these subtypes, and therefore the classifier is useful for adults as well.

45. Prediction of outcome was not significant • “Identifying a gene expression–based outcome predictor that could provide additional prognostic information, either independent of or within a genetic subtype, would be a significant advance.”

46. Gene expression profiles of pediatric acute leukemia with MLL chimeric fusion genes 130 AML 132 ALL 5 T-ALL w. MLL t Identification of expression signatures associated with MLL fusion genes irrespective of lineage (AML/ALL) A – Unsupervised PCA for the 267 samples – samples cluster according to lineage.

47. Gene expression profiles of pediatric acute leukemia with MLL chimeric fusion genes 130 AML 132 ALL 5 T-ALL w. MLL Identification of expression signatures associated with MLL fusion genes irrespective of lineage (AML/ALL) B – same PCA, MLL rearrangements are colored in red C – Supervised DAV analysis – Separation over gene space between MLL and non-MLL

48. Gene expression profiles of pediatric acute leukemia with MLL chimeric fusion genes 130 AML 132 ALL 5 T-ALL w. MLL t Identification of expression signatures associated with MLL fusion genes irrespective of lineage (AML/ALL) D – Top 50 separating genes (MLL vs. non-MLL) ranked by SAM.

49. Summary I • Distinct gene expression signatures associated with the most common AML translocations were identified. • Gene expression–based classifier performs with 93% accuracy in predicting specific • The classifier performs equally well on AML samples obtained from adults.

50. Summary II • The classifier’s inability to correctly classify a few samples appears to be a result of molecular heterogeneity in AMLs with either CBF -MYH11 or MLL translocations. This raises interesting questions for further study. • DAV analysis showed that ALL and AML samples that include MLL rearrangements are clustered together– implying that MLL-rearranged T-ALL and AML are biologically more similar to other leukemias of similar lineage.