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A Systems Approach to Elucidate Mechanisms of.  AIM 1: Developing new graph-theoretical methods for the analysis of LINCS profiles to establish relationships.

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Presentation on theme: "A Systems Approach to Elucidate Mechanisms of.  AIM 1: Developing new graph-theoretical methods for the analysis of LINCS profiles to establish relationships."— Presentation transcript:

1 A Systems Approach to Elucidate Mechanisms of

2  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 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 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

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

6 Drug-Induced Phenotype WT Phenotype R t1t1t1t1 t5t5t5t5 t3t3t3t3 t7t7t7t7 t2t2t2t2 t4t4t4t4 t8t8t8t8 t6t6t6t6 R t1t1t1t1 t5t5t5t5 t3t3t3t3 t7t7t7t7 t2t2t2t2 t4t4t4t4 t8t8t8t8 t6t6t6t6 Are dysregulated interactions more than expected by chance?

7 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 (IC 20 and 10% of IC 20 )  11 of 14 compounds in cMap 6h treatment, IC 20 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 IC 20  Performance deteriorated (3/1/7) A1 + A2 + A3 (IC 20 )

8 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 inhibits 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 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 DHFR MTX and PDX are both Dihydrofolate Reductase (DHFR) inhibitors. IDEA network shows ~50% overlap in MoA, including DHFR.

12 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 (Drug) Phenotype 1 (Control) 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

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

14 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)

15  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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