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Transcript of A Systems Approach to Elucidate Mechanisms of. AIM 1: Developing new graph-theoretical methods for...
DRUG ACTIVITY AND SENSIVITYA Systems Approach to Elucidate Mechanisms of
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
Computational U01
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.
POST-TRANSLATIONAL INTERACTIONS
TRANSCRIPTIONAL INTERACTIONS
Zhao X et al. (2009) Dev Cell. 17(2):210-21.Mani KM et al. (2008) Mol Syst Biol. 4:169Palomero T et al., Proc Natl Acad Sci 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)
Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39Zhao X et al. (2009) Dev Cell. 17(2):210-21.Wang K et al. (2009) Pac Symp Biocomput. 2009:264-75.Mani KM et al. (2008) Mol Syst Biol. 4:169Wang K et al. (2006) RECOMB
POST-TRANSCRIPTIONAL INTERACTIONS
Basso et al. Immunity. 2009 May;30(5):744-52Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40. Sumazin et al. 2011, in press
MASTER REGULATORS AND MECHANISM OF ACTION
The CTD2 Network (2010), Nat Biotechnol. 2010 Sep;28(9):904-906.Floratos A et al. Bioinformatics. 2010 Jul 15;26(14):1779-80Lefebvre C. et al (2010), Mol Syst. Biol, 2010 Jun 8;6:377Carro MS et al. (2010) Nature 2010 Jan 21;463(7279):318-25Mani K et al, (2008) Molecular Systems Biology, 4:169
Post-Translational Network Validation (MINDy)
WB: STK38
WB: c-Myc
IP:
C-M
yc
IP:
Mo
use
IgG
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
1
2
NT STK38 (B11)
1 1 2 2 3 3 4 4 5 5 MW
WB: STK38
WB: ACTIN
WB: MYC
3
~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
Mapping in Vitro to in Vivo BehaviorEx Vivo Data Master Regulator
of Cellular Phenotype
In Vitro Interactome
In Vivo Validation
In Vitro Data
Human Studies
Ex Vivo Interactome
PC3: ProstateMCF7: BreastA549: LungH1: Mouse SC
In Vivo and In VitroDrug Activity and
Phenotypic Signatures
STAT3 C/EBP
CompoundMoA
Drug-Induced PhenotypeWT Phenotype
IDEA: Drug Mechanism of Action Analysis
R
t1
t5
t3t7
t2
t4
t8
t6
R
t1
t5
t3t7
t2
t4
t8
t6
Are dysregulated interactions more than expected by chance?
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)
1. Are GEP Signatures (cMap) representative of the MoA?
A1 + A2 + A3 (IC20)
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.
Cycloheximide
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)
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
Application: From Drugs to Network Address
DHFR
Both Mtx and PdxPdx Only
Mtx Only
Top 20
MTX and PDX are both Dihydrofolate Reductase (DHFR) inhibitors. IDEA network shows ~50% overlap in MoA, including DHFR.
MARINa: Master Regulator Inference algorithm
Over-expressed in TumorUnder-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)
MRx ?
1. Carro, M. et al. (2010). "The transcriptional network for mesenchymal transformation of brain tumours." Nature 463(7279): 318-3252. Lefebvre C. et al. (2009). "A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers." Mol Syst Biol, in press3. Lim, W. et al. (2009). "Master Regulators Used As Breast Cancer Metastasis Classifier." Pac Symp Biocomp 14: 492-503
TF2: Repressed: 1/5 Activated: 1/6 Coverage: 2/18 (11%)
TF1: Repressed: 5/7 Activated: 5/7 Coverage: 10/18 (55%)
Tumor Signature
Mes signature genesActivatorRepressor
Identification of a mesenchymal regulatory module
Master Regulators control >75% of the Mesenchymal Signature of High-Grade Glioma
Hierarchical Regulatory Module
100
Cu
mu
lati
ve S
urv
ival
Days post-injection
80
60
40
20
0120806040 100
**
Control VectorStat3-C/EBPb-Stat3-/C/EBPb-
Mouse Survival
Inhibitors of C/EBP Activity
MGES
STAT3
C/EBP
MnM1
M2
MnM1
M2
(c) MINDy Analysis
Comp1
Compn
Comp1
Compn
(a) Protein Binding Assays
(b) High Throughput Screening
Gene ID Modul ator11130 ZWINT3148 HMGB22146 EZH25984 RFC4890 CCNA26790 AURKA1894 ECT27298 TYMS780 DDR151512 GTSE129899 GPSM229097 CNIH45902 RANBP1998 CDC4210549 PRDX423228 PLCL24862 NPAS29308 CD8351285 RASL121389 CREBL2
Collaboration with: S. Schreiber (a) B. Stockwell (b) A. Iavarone and A. Lasorella (a, b, c)
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
Analytical Approaches