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

-Myc

IP: M

ouse

IgG

STK38 (serine-threonine kinase 38, NDR1)

1) Protein-Protein interaction with MYC2) STK38 silencing in ST486 decreases MYC stability3) 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 moduleMaster Regulators control >75% of the Mesenchymal Signature of High-Grade Glioma

Hierarchical Regulatory Module

100

Cum

ulat

ive

Surv

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