1. Cancer Genome Atlas and Functional Systems Biology Wei Zhang, Ph.D. Professor Department of Pathology Director Cancer Genomics Core Laboratory M. D. Anderson Cancer Center The 6 th Chinese Conference on Oncology May 21-23, 2010, Shanghai, China

2. Complexity of Cancer Complexity of Cancer Cancers have heterogeneous etiology. One patient’s cancer is different from another patient’s cancer. Cancers have heterogeneous genetic defects. Cancers are results of combinations of multiple genetic and molecular alterations.

3. Personalized medicine Targeted therapy

5. Complexity of Human Genome 30-40,000 genes 1-10 millions of Single Nucleotide Polymorphisms (SNPs) 10-20 millions of proteins One gene  different spliced mRNAs  different proteins  different modified forms of proteins

6. Genomics and Proteomics Broad-scope, large-scale measurement of gene copy number, gene expression, gene methylation, and protein expression. Data interpretation or signal processing in pursuit of biological understanding.

7. DNA RNA Splice Variants ChIP protein/DNA interactions transcription DNA replication DNA repair splice forms of specific genes downstream effects on translation GX high sensitivity measurements of transcription correlate results with genomic data mRNA Agilent Technologies Microarray Portfolio… CGH chromosomal aberrations gene copy number Copy number CH 3 methylation patterns downstream transcriptional effects MethylationTranscription Factors mRNA isoforms miRNA presence of microRNAs knockout analysis correlate results with transcription data RNA interference

8. What is TCGA? The Cancer Genome Atlas (TCGA). The first phase is a 3-year, 100 million pilot project of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) focusing on glioblastoma and ovarian cancer. The second phase will cover 25 major cancer types. TCGA Mission: Increase scientific understanding of the molecular basis of cancer and apply this information to improve our ability to diagnose, treat, and prevent cancer. TCGA Purpose: Develop a complete “ atlas ” of all genomic alterations involved in cancer.

9. TCGA Pilot Project Milestones Collect/Utilize tumor tissue samples and medical information from cancer patients during treatment. Catalog and store samples at a centralized facility and send genetic material to research centers involved in the project. Identify genomic changes associated with cancer in individual patients. Identify genomic patterns associated with the disease, and use that information to inform cancer diagnosis, treatment, and prevention. Make information available to scientists as it is produced, to speed treatment and prevention research and help doctors and patients make treatment decisions. 123 45 Graphics credit: The Washington Post, December 14, 2005

10. How TCGA Functions Data Management, Bioinformatics, and Computational Analysis (GDAC) An integrated database providing access to all of the information generated by the TCGA pilot project Technology Development Throughout the pilot project, technology development will enable improvements to genomic analysis Cancer Genome Characterization Centers Technologies to investigate and characterize genes that may be associated with cancer High-throughput sequencing of genes identified through cancer genome characterization centers Genome Sequencing Centers Human Cancer Biospecimen Core Resource Centralized facility to catalog and store tumor samples, and distribute genetic material to TCGA research centers

11. Our GDAC Center Center for Systems Analysis of the Cancer Regulome Directors: Ilya Shmulevich (Institute for Systems Biology); Wei Zhang (M.D. Anderson Cancer Center) Bioinformatic researchers at MDACC: Da Yang, Yuexin Liu Focuses are on prognosis markers, systems understanding and functional validation

12. Copy NumberMethylation mRNA expression BiomarkerSystematic Network Prediction Analysis of Microarray Top Scoring Pair Algorithm Co-occurrence Copy Number Alterations Bayesian Network Tumor Subgroup Distinct Dosage Sensitive Expression Patterns

13. Consensus Copy Number Altered Regions

14. Survival Classification > 3 yr survival < 3 yr survival Top 200 pairs

15. The Cancer Genome Atlas: Glioblastoma The Cancer Genome Atlas Research Network, Nature, 2008

16. Statistical analysis of mutation significance identified Eight Genes as Significantly Mutated and P53 Mutation Is a Common Event in Primary Glioblastoma. TCGA., “Comprehensive Genomic Characterization Defines Human Glioblastoma Genes And Core Pathways,” Nature, 455(23), 1061- 1068, 2008

17. Genomic and transcriptional aberration analysis detected New Recurrent Focal Alterations such as Homozygous Deletions involving NF1 and PARK2, and Amplifications of AKT3. TCGA., “Comprehensive Genomic Characterization Defines Human Glioblastoma Genes And Core Pathways,” Nature, 455(23), 1061- 1068, 2008

18. ILK PI3K IntegrinsRTK IGFBP2 AKT P PTEN P P P P P P P Survival Growth Metabolism Migration Proliferation Akt Cell Signaling

19. Kinase domainRDPH 308473 Akt1 Kinase domainRDPH 309474 Akt2 Kinase domainRDPH 305472 Akt3 Chromosome location 14q32 19q13 1q44 Homology 75-84%90-95%73-79% Akt Isoforms Adapted from Cheng GZ et al. 2008.

20. Developmental Roles of Akt1/2/3 Akt1Akt2 Akt3 Neuronal development Glucose homeostasis Cellular growth Angiogenesis Postnatal Survival Embryonic Development and Survival Whole body weight and size Adapted from Gonzalez and McGraw. Cell Cycle. 2009.

21. Differential Roles of Akt Isoforms in Cancer Differential roles of Akt1 and Akt2 in breast cancer (Hutchinson et al. Can Res. 2004; Arboleda et al. Can Res. 2003) Akt2 predominant in ovarian cancer (Noske et al. Cancer Letters. 2007) Akt3 important in melanoma (Robertson. Can and Met Rev. 2005) Akt activation in glioma correlates with higher tumor grade (Wang et al. Lab Invest. 2004)

22. Differential Akt2 and Akt3 Levels in Oligodendroglioma AKT1 N O/AO AKT2 N O/AO AKT3 N O/AO

23. Hypothesis Akt3 is the dominant Akt isoform which preferentially induces Oligodendroglioma progression Is there a hierarchy in the ability of Akt isoforms to promote oligodendroglioma development and progression? Kristen Holmes

24. RCAS/tv-a Glial-specific Transgenic Mouse Model Begemann, M., Uhrbom, L., Rajasekhar, V.K., Fuller, G.N., and E.C. Holland. 2004 Dissecting Gliomagenesis Using Glial-Specific Transgenic Mouse Models. In Zhang, W. and G.N. Fuller (Ed.) Genomic and Molecular Neuro-Oncology. Sudbury: Jones and Bartlett. p233-278

25. WHO Grade II Oligodendroglioma Normal Brain WHO Grade III Anaplastic Oligo Histologic Criteria for Oligodendroglioma Progression

26. Akt3 Promotes Oligodendroglioma Progression Tumor Penetrance Anaplastic Oligodendroglioma Gene Combination PDGFB 81% (35/43) 11% (4/35) PDGFB / Akt1 77% (42/57) 16% (7/42) PDGFB / Akt2 39% (11/28) 9% (1/11) PDGFB / Akt3 100% (35/35) 100% (35/35) GFP 0% (0/22) 0% (0/22) AKT1 N O/AO AKT2 N O/AO AKT3 N O/AO

27. PDGFB + Akt3PDGFB + Akt1PDGFB + Akt2 Akt3 Promotes Oligodendroglioma Progression

28. Challenge How do we better understand cancer systems? Systems biology

29. Systems Biology Systems biology is an emerging field that aims at system-level understanding of biological systems. Unlike molecular biology which focus on molecules, such as sequence of nucleotide acids and proteins, systems biology focus on systems that are composed of molecular components. Although systems are composed of matters, the essence of system lies in dynamics and it cannot be described merely by enumerating components of the system. At the same time, it is misleading to believe that only system structure, such as network topologies, is important without paying sufficient attention to diversities and functionalities of components. Both structure of the system and components play indispensable role forming symbiotic state of the system as a whole. - H Kitano

30. Probabilistic Boolean network 1.Shmulevich I, Dougherty ER, Kim S, and Zhang W. Probabilistic Boolean network: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18:261-274, 2002. 2.Shmulevich I, Dougherty ER, and Zhang W. Gene perturbation and intervention in probabilistic Boolean network. Bioinformatics 18:1319-1331, 2002. 3.Shmulevich I, Lahdesmaki H, Dougherty ER, Astola J, Zhang W. Proc. Natl. Acad. Sci. USA 100 (16) 2003. US Patent # 7,257,563 (Shmulevich, Dougherty, and Zhang)

31. Such relationships should also be validated experimentally. The networks built from our models should provide valuable theoretical guidance to experiments.

32. Cancer tissues need nutrients. Gliomas are highly angiogenic. Expression of VEGF is often elevated.

33. VEGF protein is secreted outside the cells and binds to its receptor on the endothelial cells to promote their growth.

34. GRB2 FGF7 FSHR PTK7 VEGF Member of fibroblast growth factor family Follicle-stimulating hormone receptor Tyrosine kinase receptor The protein products of all four genes are part of signal transduction pathways that involve surface tyrosine kinase receptors. These receptors, when activated, recruit a number of adaptor proteins to relay the signal to downstream molecules GRB2 is one of the most crucial adaptors that have been identified. GRB2 is also a target for cancer intervention because of its link to multiple growth factor signal transduction pathways.

35. GRB2 GNB2 Molecular studies have demonstrated that activation of protein tyrosine kinase receptor- GRB-2 complex activates ras-MAP kinase-NF  B pathway to complete the signal relay from outside the cells to the nucleus. GNB2 is a ras family member. MAP kinase 1 c-rel GNB2 influences MAP kinase 1, which in turn influences c-rel, an NF  B component.

36. IGFBP-2 in Glioma Progression Up-regulation of IGFBP2 is one of the most consistent and distinctive gene expression changes in high-grade gliomas (Fuller et al., 1999)

37. IGFBP2 is a poor prognosis factor Rembrandts Data TCGA Data All gliomas Glioblastomas

38. IGFBP-2 Promotes Motility & Invasion IGFBP2 activates expression of invasion enhancing genes and promotes glioma invasion in vitro (Hua Wang et al., Cancer Res., 2003) MMP2 CD10 TIMP-1 Fibronectin Integrin  5 Integrin  6 Vinculin ILK-FAK-PI3K-AKT Regulated matrix degradation Guiding migration Actin stress fiber Cytoskeleton reorganization Migration & survival Thrombospondin 2 Filamin ABcl-2 PUMA p21/WAF1 XRCC2 TGF beta R invasion Bradykinin R B2 Centaurin IGFBP2 Thrombin R survival Hua Wang, PhD First Prize poster Competition at MDACC Trainee Recognition Day

39. George Wang, M.D., Ph.D. Limei Hu, M.D., M.S. Resident at Mt Sinai Med Ctr.

40. IGFBP2 is an Oncogene Proc Natl Acad Sci USA 104(28):11736-41, 2007 First prize in 2007 Trainee Recognition Day at MDACC American Legion Auxiliary Fellowship NIH Training grant Pharmacoinformatics fellowship Sarah Dunlap (now Sarah Smith)

41. +1 -561 c-Myc AP2 NF  B NF  B IGFBP2 A review of the literature showed that Cazals et al. (1999) indeed demonstrated that NF  B activated the IGFBP2 promoter in lung alveolar epithelial cells.

42. Higher NF  B activity in IGFBP2 overexpressing cells was also found. NF  B IGFBP2 TNFR2 ILK Our real-time PCR data showed that in stable IGFBP2- overexpressing cell lines, IGFBP2 indeed enhances ILK expression. In addition, IGFBP2 contains an RGD domain, implying its interaction with integrin molecules. ILK is in the integrin signal transduction pathway. OCT1 p65/p50 p50/p50 IGFBP2 clone Parental IGFBP2 clone Parental Non-specific

43. ILK is elevated in high-grade glioma and correlates with shorter survival

44. NH2 COOH IGF binding domains 5’ TCCAGGGAGCCCCCACCATCCGGGGGGACCCCGAGTGTCATCTCTTCT 3’ R G D 306 GAA E 306 D306E-IGFBP2 (RGE mutant) Thyroglobulin type-1 motif (TG domain) DXXD motif RGD domain RGE mutational substitution on IGFBP2

45. Integrin Linked Kinase IKKα IGFBP2 ILK PI3K Akt P PH Receptor Tyrosine Kinase NFĸB IĸB U U U NFĸB Nucleus RGDRGD Target genes P NFĸB Ligand 1 Ser 473 GSK3 P Cyclin D Proliferation PIP3 Integrins

46. Integrin Binding is Required for IGFBP2- mediated Progression n=50 n=42 n=32n=50 GFP PDGFB PDGFB IGFBP2 PDGFB IGFBP2(RGE) n=22

47. IGFPB2 Drives Progression via ILK n=42n=50 n=28 n=26n=22 PDGFB PDGFB ILK PDGFB IGFBP2 PDGFB ILK-KD PDGFB ILK-KD IGFBP2

48. IGFBP2-ILK-AKT pathway Critical for cancer development and progression Opportunities for drug development

49. Systems understanding to cancer and cancer therapeutics Predictive instead of reactive medicine TCGA is making a major impact on individualized medicine Future Cancer Biology

50. Acknowledgment NIH/NCI GDAC Center grant (Shmulevich/Zhang) NIH/NCI RO1 CA098503 (Zhang/Fuller) NIH/NCI NIH R01 CA141432 (Zhang/Fuller) NIGMS/NIH R01 GM072855 (Shmulevich/Zhang) Goldhirsh Foundation (Zhang) James S McDonald Foundation (Zhang) National Foundation for Cancer Research (Zhang/Hamilton) NFCR Hope Fund (Zhang) Anthony Bullock III Research fund (Zhang/Fuller/Sawaya) The Oreffice Foundation (Zhang/Fuller/Sawaya) Commonwealth Foundation for Cancer Research (Zhang/Trent) NIH/NCI NIH R01 CA098570 (Zhang/Pollock, completed) NIH R21 GM070600 (Shmulevich/Zhang/Kauffman, completed) Department of Defense (Zhang, completed) Texas Higher Education Coordinating Board ARP and ATP grants (Zhang/Fuller, Zhang/Holland, completed) RGK Foundation (Zhang, completed) NIH/NCI P30 CA016672-28 (CCSG) Tobacco Settlement Fund Kadoorie Foundation Goodwin Fund