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Cancer Genome Atlas and Functional Systems Biology

The 6 th Chinese Conference on Oncology May 21-23, 2010, Shanghai, China. 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. Complexity of Cancer.

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Cancer Genome Atlas and Functional Systems Biology

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  1. The 6th Chinese Conference on Oncology May 21-23, 2010, Shanghai, China 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

  2. 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

  4. 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

  5. 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.

  6. Agilent Technologies Microarray Portfolio… RNA DNA Splice Variants GX CGH CH3 ChIP miRNA Copy number Methylation Transcription Factors mRNA mRNA isoforms RNA interference • chromosomal aberrations • gene copy number • methylation patterns • downstream transcriptional effects • protein/DNA interactions • transcription • DNA replication • DNA repair • high sensitivity measurements of transcription • correlate results with genomic data • splice forms of specific genes • downstream effects on translation • presence of microRNAs • knockout analysis • correlate results with transcription data

  7. 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.

  8. TCGA Pilot Project Milestones 4 5 1 2 3 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. 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. Graphics credit: The Washington Post, December 14, 2005

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

  10. 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

  11. Copy Number Methylation mRNA expression Tumor Subgroup Systematic Network Biomarker • Distinct Dosage Sensitive Expression Patterns • Co-occurrence Copy Number Alterations • Bayesian Network • Prediction Analysis of Microarray • Top Scoring Pair Algorithm

  12. Consensus Copy Number Altered Regions

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

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

  15. 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

  16. 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

  17. Akt Cell Signaling Integrins RTK IGFBP2 P P P P PI3K P P ILK PTEN P AKT P Proliferation Survival Growth Migration Metabolism

  18. Akt Isoforms 308 473 PH Kinase domain RD Akt1 309 474 Akt2 PH Kinase domain RD 305 472 Akt3 PH Kinase domain RD Chromosome location 14q32 19q13 1q44 75-84% 90-95% 73-79% Homology Adapted from Cheng GZ et al. 2008.

  19. Developmental Roles of Akt1/2/3 Postnatal Survival Cellular growth Angiogenesis Glucose homeostasis Akt1 Akt2 Akt3 Embryonic Development and Survival Whole body weight and size Neuronal development Adapted from Gonzalez and McGraw. Cell Cycle. 2009.

  20. 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)

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

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

  23. 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

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

  25. Akt3 Promotes Oligodendroglioma Progression AKT1 AKT3 AKT2 O/AO N O/AO N O/AO N Tumor Penetrance Anaplastic Oligodendroglioma Gene Combination GFP 0% (0/22) 0% (0/22) 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)

  26. Akt3 Promotes Oligodendroglioma Progression PDGFB + Akt1 PDGFB + Akt2 PDGFB + Akt3

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

  28. 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. Systems Biology - H Kitano

  29. Probabilistic Boolean network US Patent # 7,257,563 (Shmulevich, Dougherty, and Zhang) • Shmulevich I, Dougherty ER, Kim S, and Zhang W. Probabilistic Boolean network: a rule-based uncertainty model for gene regulatory networks. Bioinformatics18:261-274, 2002. • Shmulevich I, Dougherty ER, and Zhang W. Gene perturbation and intervention in probabilistic Boolean network. Bioinformatics 18:1319-1331, 2002. • Shmulevich I, Lahdesmaki H, Dougherty ER, Astola J, Zhang W. Proc. Natl. Acad. Sci. USA 100 (16) 2003.

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

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

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

  33. Member of fibroblast growth factor family FGF7 VEGF PTK7 Tyrosine kinase receptor GRB2 • 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. FSHR Follicle-stimulating hormone receptor

  34. 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. GRB2 GNB2 MAP kinase 1 • GNB2 influences MAP kinase 1, which in turn influences c-rel, an NFB component. c-rel

  35. 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)

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

  37. Regulated matrix degradation MMP2 CD10 TIMP-1 Guiding migration Fibronectin IGFBP2 Thrombospondin 2 TGF beta R Bradykinin R B2 invasion Thrombin R Integrin a 5 Integrin a 6 ILK-FAK-PI3K-AKT Migration & survival Centaurin Bcl-2 Filamin A PUMA p21/WAF1 XRCC2 Vinculin Actin stress fiber Cytoskeleton reorganization survival 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) Hua Wang, PhD First Prize poster Competition at MDACC Trainee Recognition Day

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

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

  40. c-Myc AP2 NFB -561 +1 • 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. IGFBP2 NFB

  41. IGFBP2 clone IGFBP2 clone Parental Parental p65/p50 p50/p50 OCT1 Non-specific • Higher NFB activity in IGFBP2 overexpressing cells was also found. IGFBP2 TNFR2 • 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. ILK NFB

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

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

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

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

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

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

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

  49. 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

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