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Drug Activity and Sensivity

A Systems Approach to Elucidate Mechanisms of. Drug Activity and Sensivity. Computational U01. AIM 1: Developing new graph-theoretical methods for the analysis of LINCS profiles to establish relationships between mechanisms that are conserved/divergent in vitro and in vivo .

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Drug Activity and Sensivity

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  1. A Systems Approach to Elucidate Mechanisms of Drug Activity and Sensivity

  2. Computational U01 • AIM 1: Developing new graph-theoretical methods for the analysis of LINCS profiles to establish relationships between mechanisms that are conserved/divergent in vitro and in vivo. • AIM 2: Developing new tools to elucidate • (a) cell line specific compound MoA • (b) genes/drugs that can modulate drug-sensitivity or resistance • (c) genes/drugs that can induce specific phenotypes • AIM 3: design of novel algorithm for the inference of gene-gene, gene-compound, and compound-compound synergy Our center will develop algorithms to help elucidate how response to small-molecule and biochemical perturbations is mediated by the genetic and molecular context of the cell. These algorithms will establish a predictive framework for the dissection of synergistic (i.e., non additive) perturbations.

  3. Developmental Transcriptional Interactions POST-TRANSCRIPTIONAL INTERACTIONS Zhao X et al. (2009) Dev Cell. 17(2):210-21. Mani KM et al. (2008) Mol Syst Biol. 4:169 Palomero T et al., Proc NatlAcadSci U S A 103, 18261 (Nov 28, 2006). Margolin AA et al., Nature Protocols; 1(2): 662-671 (2006) Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006). Basso K et al. (2005), Nat Genet.;37(4):382-90. (Apr. 2005) Basso et al. Immunity. 2009 May;30(5):744-52 Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40. Sumazin et al. 2011, in press Master regulators and mechanism of action Post-translational Interactions Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39 Zhao X et al. (2009) Dev Cell. 17(2):210-21. Wang K et al. (2009) Pac SympBiocomput. 2009:264-75. Mani KM et al. (2008) Mol Syst Biol. 4:169 Wang K et al. (2006) RECOMB The CTD2 Network (2010), Nat Biotechnol. 2010 Sep;28(9):904-906. Floratos A et al. Bioinformatics. 2010 Jul 15;26(14):1779-80 Lefebvre C. et al (2010), Mol Syst. Biol, 2010 Jun 8;6:377 Carro MS et al. (2010) Nature 2010 Jan 21;463(7279):318-25 Mani K et al, (2008) Molecular Systems Biology, 4:169

  4. Post-Translational Network Validation (MINDy) 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 1 3 2 Wang K, Saito M, et al. (2009) Nat. Biotechnol. 27(9):829-39

  5. Mapping in Vitro to in Vivo Behavior Ex Vivo Data Master Regulator of Cellular Phenotype Ex Vivo Interactome Human Studies In Vivo and In Vitro Drug Activity and PhenotypicSignatures PC3: Prostate MCF7: Breast A549: Lung H1: Mouse SC STAT3 C/EBP In Vivo Validation Compound MoA In Vitro Data In Vitro Interactome

  6. IDEA: Drug Mechanism of Action Analysis Drug-Induced Phenotype WT Phenotype t1 t1 t8 t8 t2 t2 t7 t7 R R t3 t3 t6 t6 t4 t4 t5 t5 Are dysregulated interactions more than expected by chance?

  7. 1. Are GEP Signatures (cMap) representative of the MoA? Diffuse Large B Cell Lymphoma cell line (Ly7) • 270 GEPs: 14 compounds + vehicle X 3 replicates X 3 time points (6h, 12h, 24h)X 2 concentrations (IC20 and 10% of IC20) • 11 of 14 compounds in cMap 6h treatment, IC20 concentration • 5/11 (Camptothecin, Cycloheximide, Etoposide, Rapamycin, Geldanamycin) matched cMap profile in top 5 • 1/11 (Trichostatin) matched cMap profile of compounds with same MoA in top 5 • 5/11 (Doxorubicin, H-7, Methotrexate, Monastrol, Doxorubicin, Blebbistatin) matched unrelated compounds Time: 12h/24h treatment • Performance deteriorated (4/1/6 and 3/2/6) Concentration: 10% of IC20 • Performance deteriorated (3/1/7) A1 + A2 + A3 (IC20)

  8. Geldanamycin Geldanamycin binds to HSP-90 (Heat shock protein- 90) which acts as a scaffold for protein folding. As a result the proteins undergo degradation.

  9. Cycloheximide Cycloheximideinhibits protein synthesis by binding to the 60S subunit of ribosome and  inhibiting translational elongation (the process in which amino acids are added by tRNAs)

  10. Camptothecin: Topoisomerase I inhibitor ATF2, RBL2, NCOA1, NFYB, SMAD2, YWHAZ,NR3C1,APP,MAP3K5, RB1,MEF2A, SOS1, RASA1, BRCA1,NFKB1,KLF12,TP53, EPS15, GSK3B, CASK, VAV2,MCM7,FOSL1,AKAP13,ATF3, IRF5,ETS1,BUB1, BCL2

  11. Application: From Drugs to Network Address DHFR MTX and PDX are both DihydrofolateReductase (DHFR) inhibitors. IDEA network shows ~50% overlap in MoA, including DHFR.

  12. MARINa: Master Regulator Inference algorithm A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event. Phenotype 1 (Control) Phenotype 2 (Drug) MRx? TF1: Repressed: 5/7 Activated: 5/7 Coverage: 10/18 (55%) TF2: Repressed: 1/5 Activated: 1/6 Coverage: 2/18 (11%) Under-expressed in Tumor Over-expressed in Tumor Tumor Signature • Carro, M. et al. (2010). "The transcriptional network for mesenchymal transformation of brain tumours." Nature463(7279): 318-325 • Lefebvre C. et al. (2009). "A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers." Mol SystBiol, in press • Lim, W. et al. (2009). "Master Regulators Used As Breast Cancer Metastasis Classifier." Pac SympBiocomp14: 492-503

  13. Mes signature genes Activator Repressor Identification of a mesenchymal regulatory module Master Regulators control >75% of the Mesenchymal Signature of High-Grade Glioma Mouse Survival Control Vector Stat3- C/EBPb- Stat3-/C/EBPb- Hierarchical Regulatory Module

  14. Inhibitors of C/EBP Activity (c) MINDy Analysis (a) Protein Binding Assays M2 M1 Mn M2 M1 Mn C/EBP Comp1 Comp1 STAT3 Compn MGES Compn (b) High Throughput Screening Collaboration with: S. Schreiber (a) B. Stockwell (b) A. Iavarone and A. Lasorella (a, b, c)

  15. Analytical Approaches • Adapt current algorithms to using sparse LINCS molecular profile data: • Mapping L1000 signatures to IDEA and MARINa • E.g. can we extrapolate from the L1000 landmark gene signatures? • E.g. can we design context specific, network based extrapolation methods? • Mapping phospho-profiles to signaling networks • Mapping in vitro to in vivo drug behavior • Inferring Master Regulators of phenotypic signatures (in vivo) • Mapping drugs and drug combinations to these Master Regulators (in vitro) • Explore algorithms for the inference of synergistic drug combinations • Signature of phenotype of interest (e.g. loss of pluripotency in H9 cells) • Master Regulators of phenotype of interest • Post-translational modulators of inferred MRs • Synergy • Drug modulating distinct MRs • Drugs effecting non-overlapping subset of the desired signature • Drugs that affect MRs of Drug Resistance

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