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Interrogating High-Grade Glioma Regulatory Networks to Identify :

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Presentation on theme: "Interrogating High-Grade Glioma Regulatory Networks to Identify :"— Presentation transcript:

1 Interrogating High-Grade Glioma Regulatory Networks to Identify :

2 Genomics Epigenomics Cell Regulatory Logic ProteinProteinProteinMembraneProteinDNA Transcriptomics Other Omics: Metabolomics Metabolomics Proteomics Proteomics Glycomics Glycomics … Other Omics: Metabolomics Metabolomics Proteomics Proteomics Glycomics Glycomics … Drugs & Biomarkers Clinical Trials PreventionDiagnosisTreatment Cancer

3 Developmental Zhao X et al. (2009) Dev Cell. 17(2): Mani KM et al. (2008) Mol Syst Biol. 4:169 Palomero T et al., Proc Natl Acad Sci U S A 103, (Nov 28, 2006). Margolin AA et al., Nature Protocols; 1(2): (2006) Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006). Basso K et al. (2005), Nat Genet.;37(4): (Apr. 2005) Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9): Zhao X et al. (2009) Dev Cell. 17(2): Wang K et al. (2009) Pac Symp Biocomput. 2009: Mani KM et al. (2008) Mol Syst Biol. 4:169 Wang K et al. (2006) RECOMB Basso et al. Immunity May;30(5): Klein et al, Cancer Cell, 2010 Jan 19;17(1): Sumazin et al. 2011, in press The CTD 2 Network (2010), Nat Biotechnol Sep;28(9): Floratos A et al. Bioinformatics Jul 15;26(14): Lefebvre C. et al (2010), Mol Syst. Biol, 2010 Jun 8;6:377 Carro MS et al. (2010) Nature 2010 Jan 21;463(7279): Mani K et al, (2008) Molecular Systems Biology, 4:169

4 Joe Mary Tony MYCTERT GSK3 Degradation Signal MYC TERT GSK3

5 STK38 (serine-threonine kinase 38, NDR1) 1) Protein-Protein interaction with MYC 2) STK38 silencing in ST486 decreases MYC stability 3) MYC mRNA is not affected 3) MYC targets are consistently affected ~400 Gene Expression Profiles for Normal and Tumor Related Human B Cells Wang K, Saito M, et al. (2009) Nat. Biotechnol. 27(9):829-39

6  Cancer  B Cell interactome (BCi)  Breast Cancer Cell interactome (BCCi)  T-ALL interactome (TALLi)  AML  Prostate Cancer interactome (Pci mouse/human)  Glioblastoma Multiforme interactome (GBMi)  Ovarian  Non-small-cell Lung Cancer  Colon Cancer  Hepatocellular Carcinoma  Neuroblastoma  NET  Stem Cells  Mouse EpiSC and ESC  Human ESC  Germ Cell Tumors (Pluripotency, Lineage Differentiation)  Neurodegenerative Disease  Human and Mouse Motor Neuron (ALS)  Human and Mouse whole brain (Alzheimer’s) Interactomes are generated from primary tissue profiles and thus reflect cell regulation in vivo, in the presence of all relevant paracrine, endocrine, and contact signals

7 Cell Regulatory Logic Pluripotency and Lineage Differentiation Drug MoA and Resistance Disease Initiation & Progression Mechanism of Action Biomarkers Therapeutic Targets Small-molecule Modulators

8 Over-expressed in Tumor Under-expressed in Tumor A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event. Phenotype 2 (Neoplastic) Phenotype 1 (Normal) MR x ? 1.Carro, M. et al. (2010). "The transcriptional network for mesenchymal transformation of brain tumours." Nature 463(7279): Lefebvre C. et al. (2009). "A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers." Mol Syst Biol, in press 3.Lim, W. et al. (2009). "Master Regulators Used As Breast Cancer Metastasis Classifier." Pac Symp Biocomp 14: TF2: Repressed: 1/5 Repressed: 1/5 Activated: 1/6 Activated: 1/6 Coverage: 2/18 (11%) Coverage: 2/18 (11%)TF1: Repressed: 5/7 Repressed: 5/7 Activated: 5/7 Activated: 5/7 Coverage: 10/18 (55%) Coverage: 10/18 (55%) Tumor Signature

9 Unsupervised clustering of 176 high grade tumors by expression of 108 genes that are positively or negatively associated with survival reveals 3 tumors classes (Proneural (PN), Mesenchymal (Mes) and Proliferative (Prolif). Phillips et al., Cancer Cell, 2006 Malignant gliomas belonging to the mesenchymal sub-class express genes linked to the most aggressive properties of glioblastoma (migration, invasion and angiogenesis). MGES PNGES PROGES

10 Mes signature genes Activator Repressor Biochemical Validation of ARACNe Inferred Targets of Stat3, C/EBPb, FosL2, and bHLH-B2 Master Regulators control >75% of the Mesenchymal Signature of High-Grade Glioma Hierarchical Regulatory Module

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12 Control Vector Stat3- C/EBPb- Stat3-/C/EBPb- Human Survival Data Mouse Survival Data Mouse immunohistochemistry Carro, M. et al. (2010). Nature 463(7279):

13 Distinct Programs with significant overlap across distinct datasets Phillips (2) Sun (3) TCGA (1) x10 -7 E Cancer Genome Atlas Research Network, Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature Oct 23;455(7216): Phillips, H.S., et al., Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, (3): p Sun, L., et al., Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell, (4): p `

14 G1G1 G1G1 G2G2 G2G2 G3G3 G3G3 G4G4 G4G4 G5G5 G5G5 G6G6 G6G6 G7G7 G7G7 G8G8 G8G8 G 10 G 11 G 12 Patient X Molecular Phenotype E.g. GBM subtypes G9G9 G9G9 X X Y Y Z Z W W V V Master Regulator Module(s) Disease Stratification Biomarkers Therapeutic Targets … = EGFR = PDGFRA = p16 = p53 = PTEN = MDM2 = MDM4 = MYC = NF1 = ERBB2 = RB1 = CDK4 G 1 G 2 G 3 G 4 G 5 G 6 G 7 G 8 G 9 G 10 G 11 G 12 Glioblastoma:  Carro MS et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature Jan 21;463(7279):  Master Regulators: C/EBP + Stat3 Diffuse Large B Cell Lymphoma:  Compagno M et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature Jun 4;459(7247):  Master Regulator: Nf-kB pathway GC-Resistance in T-ALL:  Real PJ et al. Gamma-secretase inhibitors reverse glucocorticoid resistance in T cell acute lymphoblastic leukemia. Nat Med Jan;15(1):50-8.  Master Regulator: NOTCH1 pathway

15 MGES STAT3 C/EBP MnMn MnMn M1M1 M1M1 M2M2 M2M2 MnMn MnMn M1M1 M1M1 M2M2 M2M2 (c) MINDy Analysis Comp 1 Comp n Comp 1 Comp n (a) Protein Binding Assays (b) High Throughput Screening Collaboration with: S. Schreiber (a) B. Stockwell (b) A. Iavarone and A. Lasorella (a, b, c)

16 mature miRNA miR target Mod Sumazin et al. Cell, 2011 Oct 14;147(2):

17  Analysis of TCGA data for GBM and Ovarian Cancer  including matched gene and miRNA expression profiles for 422 and 587 samples  Modulation of miRNA activity on targets  7,000 Sponge modulators, participating in 248,000 miR-mediated mRNA-mRNA interactions  148 Non-sponge modulators affecting more than 100 miRs (using only experimentally validated miRs targets)  17/430 are RNA-binding proteins or a component of the spliceosome

18 G1G1 G2G2 G3G3 G4G4 G5G5 G6G6 G8G8 G9G9 G 10 G 11 G 13 G 14 – G 563 PTEN G7G7 564 node, 111 core sub-graph p < 5 x p < 2 x

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20 Silencing PTEN mPR regulators affects SNB19 cell growth rate Proliferation fold change Days PTEN over expression and silencing effects on SNB19 cell growth rate Proliferation fold change AB Days Silencing PTEN mPR regulators affects SF188 cell growth rate PTEN over expression and silencing effects on SF188 cell growth rate CD Proliferation fold change

21 Hit compound Compound Mechanism of Action Biomarkers: Response/Efficacy Molecular Target(s) Clinical Trials CTD 2 Network

22  Current emphasis on genes harboring genetic and epigenetic alterations may not be sufficient  We should also focus on Master Regulator and Master Integrator genes  Current approach to biomarker discovery should be re-evaluated in a molecular interaction network context.  It is not the genes/proteins that change the most but rather those that change most consistently. (mRNA is not informative)  From GWAS (Genome-Wide Association Studies) to NBAS (Network-Based Association Studies)  Califano A, Butte A, Friend S, Ideker T, and Schadt EE, Integrative Network-based Association Studies: Leveraging cell regulatory models in the post-GWAS era, Nat. Genetics, in press. Accessible in Nature Preceedings:  One disease – One target – One drug Multi-target combinations  Optimal combination of drugs selected from a repertoire of safe, target-specific compounds using predictive tools.  Identification of genetic dependencies (addictions) from Ex Vivo Models  Identification of candidate therapeutic agents from In Vitro mechanistic models.

23 Funding Sources: NCI, NIAID, NIH Roadmap  Califano Lab Experimental  Gabrielle Rieckhof, Ph.D. (Exec Director)  Mariano Alvarez, Ph.D.  Brygida Bisikirska, Ph.D.  Xuerui Yang, Ph.D.  Yao Shen, Ph.D.  Presha Rajbhandari, M.A. (Sr. Res. Worker)  Jorida Coku, M.A. ( Staff Associate )  Hesed Kim, (Staff Associate)  Sergey Pampou, Ph.D.  A. Iavarone & A. Lasorella (CU)  Maria Stella Carro  K. Aldape (MD Anderson)  R. Dalla Favera (CUMC)  Katia Basso  Ulf Klein  R. Chaganti (MSKCC)  M. White & J. Minna (UTSW)  J. Silva (CU)  C. Abate-Shen & M. Shen (CU)  D. Felsher (Stanford)  Califano Lab (Computational)  Mukesh Bansal, Ph.D.  Archana Iyer, Ph.D.  Celine Lefebvre, Ph.D.  Yishai Shimoni, Ph.D.  Maria Rodriguez-Martinez, Ph.D.  Antonina Mitrofanova, Ph.D.  Jose’ Morales, Ph.D.  Paola Nicoletti, Ph.D.  Pavel Sumazin, Ph.D.  Gonzalo Lopez, Ph.D.  James Chen, (GRA)  Hua-Sheng Chu (GRA)  Wei-Jen Chung (GRA)  In Sock Jang (GRA)  William Shin (GRA)  Jiyang Yu (GRA)  Wei-Jen Chung (GRA)  Alex Lachman (GRA)  Pradeep Bandaru M.A.  Manjunath Kustagi (Programmer)  Software Development  Aris Floratos, Ph.D. (Exec. Director)  Ken Smith, Ph.D.  Min Yu, (Programmer)


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