Rationalizing CMap Gene Expression Readouts via … CMap Gene Expression Readouts via Target Prediction Nolen Joy Perualila Non-Clinical Statistics Conference 2014 9 October 2014 RESEARCH

Rationalizing CMap Gene Expression Readoutsvia Target Prediction

Nolen Joy PerualilaNon-Clinical Statistics Conference 20149 October 2014

RESEARCH GROUP

Hasselt University,BelgiumNolen Joy PerualilaZiv Shkedy

Durham University,UKAdetayo Kasim

Cambridge University,UKAakash Chavan RavindranathAndreas Bender

Janssen Pharmaceutica NV,BelgiumLuc BijnensWillem TalloenHinrich W.H. Gohlmann

QSTAR http://qstar-consortium.org

N. J. Perualila NCS2014 2/18

http://qstar-consortium.org

OUTLINE

1 BackgroundMechanism of Action (MoA) of Compounds

2 Data SourcesTarget prediction DataGene expression Data

3 Analysis Flow

4 ResultsAssociating Protein Targets to compoundsAssociating Genes with compoundsGene-set EnrichmentUsing Pathways to understand MoABiclustering of CMap Gene expression data

5 Discussion


MOA OF COMPOUNDS

Aim: To find subsets of compounds that share similar target prediction andgene expression profiles.


Connectivity Map In silico

EARLY DRUG DISCOVERY

The development ofevery drug begins withthe search for a target onwhich the drug can act.

Lead compounds mustbe able to bind well tothe target protein like akey into a lock.

If a drug binds to oneprotein, its drug target,it may also bind toanother one (or many)(non-selective ligands).


Compound Protein Target






Compound - Protein Target






Compound - Protein Target

EARLY DRUG DISCOVERYThe development ofevery drug begins withthe search for a target onwhich the drug can act.



Which drugs will bind to which protein?


image source: http://vds.cm.utexas.edu/

OVERVIEW: TARGET PREDICTION TOOL

Calculates the likelihood of binding for every protein target (seeKoutsoukas,2011).


.

COMPOUNDS, TARGETS, AND GENES

Ligand-bindingmodifies thebiological functions ofprotein target, a seriesof target-relateddownstream genes arethen influenced.

Drugs sharingcommon targets resultin similargene-expressionprofiles.


image source: http://cc.scu.edu.cn/G2S/eWebEditor/uploadfile/20120810155023582.jpg

COMPOUNDS, TARGETS, AND GENES

Ligand-bindingmodifies thebiological functions ofprotein target, a seriesof target-relateddownstream genes arethen influenced.

Drugs sharingcommon targets resultin similargene-expressionprofiles.


image source: http://cc.scu.edu.cn/G2S/eWebEditor/uploadfile/20120810155023582.jpg

DATA SOURCES

T =

T11 T12 . . . T1I

T21 T22 . . . T2I

. . . .

. . . .

. . . .

TJ1 TJ2 . . . TJI

X =

X11 X12 . . . X1I

X21 X22 . . . X2I

. . . .

. . . .

. . . .

XG1 XG2 . . . XGI

.

1 The target prediction scorematrix (binary)(J targets x I compounds)

Tji =

{1 compound i hit target j,0 otherwise.

2 The gene expression matrix(G genes x I compounds)

Xgi = expression level ofgene g for compound i

.


APPLICATION: CONNECTIVITY MAP

I = 35 compounds.MC7 cell line,6 hours after exposure,dose at 10 micromolars.

G' 2400 genes afterpre-processing.

J = 477 protein targets.


ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds

a cluster of compounds

Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes

Pathways/ MLPoverlap

For every target-driven compoundcluster

What are theshared targets?

What genes aredifferentiallyexpressed?

Whichbiologicalpathways areaffected?


ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes







ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes







inout

1 0Target j

cluster

ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes







ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes







ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes

Pathways/ MLP

overlap






ANALYSIS FLOWStep I

Step II

Target-prediction

based Clustering

of Compounds


Fisher’s Exact

Test: top K targets

Pathways

LIMMA: top N

differentially

expressed genes







TARGET PREDICTION-BASED CLUSTERING

Similarity matrix based on Tanimoto scores.

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tioguanin

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imatinib

compounds

0 0.6

Value

Color Key


ASSOCIATING TARGETS TO COMPOUNDS

Identify the top predicted protein targets of compounds in cluster 1.

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X5.hydroxyr_6

D.1B._dopator

Muscarinic_M3

Muscarinic_M1

Cytochrome2D6

NADPH_oxide_1

Histamine_tor

D.2._dopamtor

Targ

ets

Compounds


ASSOCIATING GENES WITH COMPOUNDS

Identify the most differentially expressed genes for compounds in cluster 1.

log fold change

−lo

g p

−va

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−0.4 −0.2 0.0 0.2

02

46

810

12 IDI1

SQLEMSMO1

INSIG1

MNT

SRSF7HMGCS1

CCR1CCNG2 KLHL24 PPIFSLC38A2 NPC2SGCE

PNO1BARD1

LPIN1HMGCRTGS1LDLR

log

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0.2

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0.8

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IDI1

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ASSOCIATING GENES WITH COMPOUNDS

Identify the most differentially expressed genes for compounds in cluster 1.

log fold change

−lo

g p

−va

lue

−0.4 −0.2 0.0 0.2

02

46

810

12 IDI1

SQLEMSMO1

INSIG1

MNT

SRSF7HMGCS1

CCR1CCNG2 KLHL24 PPIFSLC38A2 NPC2SGCE

PNO1BARD1

LPIN1HMGCRTGS1LDLR

log

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BIOLOGICAL PATHWAYS: CLUSTER 1Compounds Pathway Target Genesclozapine

Steroid metabolic process Cytochrome P450 2D6 INSIG1

thioridazinechlorpromazine

LDLRtrifluoperazineprochlorperazinefluphenazine

GO pathways containing the topgene sets with MLP analysis.

GO:0006695\

cholesterol biosynthetic

GO:0016126\

sterol biosynthetic

GO:0008203\

cholesterol metabolic

GO:0016125\

sterol metabolic

GO:0006694\

steroid biosynthetic

GO:0006066\

alcohol metabolic

GO:0008202\

steroid metabolic

GO:0008610\

lipid biosynthetic

GO:0046165\

alcohol biosynthetic

Top genes contributing to choles-terol biosynthetic process.

DHCR24:24−dehydrocholesterol reductase

G6PD:glucose−6−phosphate dehydrogenase

HMGCR:3−hydroxy−3−methylglutaryl−CoA reductas

HMGCS1:3−hydroxy−3−methylglutaryl−CoA synthas

IDI1:isopentenyl−diphosphate delta isomerase

INSIG1:insulin induced gene 1


PEX2:peroxisomal biogenesis factor 2

MSMO1:methylsterol monooxygenase 1

SOD1:superoxide dismutase 1, soluble

SQLE:squalene epoxidase

CNBP:CCHC−type zinc finger, nucleic acid bind

Sig

nific

an

ce

of te

ste

d g

en

es

invo

lve

d in

ge

ne

se

t GO

:00

06

69

5

Significance

0 1 2 3 4 5


INSIG1LDLR CYP450 2D6

Steroid MetabolicProcess

clozapine, thioridazine,chlorpromazine, trifluoperazine,prochlorperazine,fluphenazine

BIOLOGICAL PATHWAYS: CLUSTER 1Compounds Pathway Target Genesclozapine

Steroid metabolic process Cytochrome P450 2D6 INSIG1

thioridazinechlorpromazine

LDLRtrifluoperazineprochlorperazinefluphenazine

GO pathways containing the topgene sets with MLP analysis.

GO:0006695\

cholesterol biosynthetic

GO:0016126\

sterol biosynthetic

GO:0008203\

cholesterol metabolic

GO:0016125\

sterol metabolic

GO:0006694\

steroid biosynthetic

GO:0006066\

alcohol metabolic

GO:0008202\

steroid metabolic

GO:0008610\

lipid biosynthetic

GO:0046165\

alcohol biosynthetic

Top genes contributing to choles-terol biosynthetic process.

DHCR24:24−dehydrocholesterol reductase

G6PD:glucose−6−phosphate dehydrogenase

HMGCR:3−hydroxy−3−methylglutaryl−CoA reductas

HMGCS1:3−hydroxy−3−methylglutaryl−CoA synthas

IDI1:isopentenyl−diphosphate delta isomerase



PEX2:peroxisomal biogenesis factor 2

MSMO1:methylsterol monooxygenase 1

SOD1:superoxide dismutase 1, soluble

SQLE:squalene epoxidase

CNBP:CCHC−type zinc finger, nucleic acid bind

Sig

nific

an

ce

of te

ste

d g

en

es

invo

lve

d in

ge

ne

se

t GO

:00

06

69

5

Significance

0 1 2 3 4 5


GENE EXPRESSION DATA ANALYSIS

X =

X11 X12 . . . X1I

X21 X22 . . . X2I

. . . .

. . . .

. . . .

XG1 XG2 . . . XGI

.

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proc

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CFLARARL4CHMOX1SAE1HMGXB4HIST1H1CHCG9CDH11PMAIP1ZMPSTE24TSPAN5MICAMRPS31SERPINE1RBM4BTOM1L1HIST1H3DPOP7SH2B3EIF1TAF15LPAR6OSGIN1SETXSLC7A11NAMPTSTARHMGCS1TSPAN1LOC100505761ADCK3SF3B4HIST1H3BNQO1MAPRE2IDI1LOC100506963CYP46A1NPC1UBA2NEAT1CDK2AP2CEBPZPDCD6IPATP9ACDK7CALHM2FABP4LOC100506469TXNDC9LOC100507619HIST1H2AEKDM3AHBP1HIST1H2BKDHRS9BCAP31TOB1INSIG1PELOGIT1CDKN1AHMGCRFGL2LOC100129361KIF20ARBM5RBM7BHLHE40PPIFSPRY2MED6MIR22HGGCLMGCLCHIST2H2BELPIN1SQLECDKN1BSLC6A8SPATA1PDIA6DHRS2GADD45AIRX5RTN2DDIT4AKR1C2MSMO1LOC100506168PRMT3CNIHTRIM13NET1HNRNPRSMC4FLOT1ARPC5TOMM6LDLRAKR1C3LOC100293516CDK4SPRY1FAM13AFAM117ATXNRD1LRPPRCZNF586TRIB1HIST1H2BDCCDC28AUSPL1HIST1H2BGRASGRP1BET1AKAP9MPHOSPH10PQBP1STAG1

COMPOUNDS

GE

NE

S

Biclustering of gene expression data provides a simultaneous localsearch of a subset of genes for which a similar expression profiles weredetected across a subset of compounds


Heatmap of the Expression Profiles

GENE EXPRESSION DATA ANALYSIS

X =

X11 X12 . . . X1I

X21 X22 . . . X2I

. . . .

. . . .

. . . .

XG1 XG2 . . . XGI

.

trifl

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proc

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BET1CDKN1BNET1STAG1CDKN1AZMPSTE24IRX5CDK2AP2SF3B4HCG9SPRY2SPRY1POP7MRPS31HNRNPRCDK7TRIB1FLOT1EIF1USPL1TRIM13DHRS2CDK4MPHOSPH10PRMT3TXNDC9RBM5RBM7CNIHDHRS9LPAR6CEBPZFAM13AAKAP9TOB1NAMPTBCAP31PDIA6LRPPRCRASGRP1ARL4CKIF20APPIFTSPAN1CDH11TSPAN5ARPC5PQBP1ATP9ASAE1UBA2SMC4LOC100507619MICALOC100506963LOC100506469LOC100506168LOC100505761SPATA1HMGXB4TOM1L1LOC100293516SH2B3TOMM6PDCD6IPLOC100129361MED6HIST1H2BGKDM3AADCK3CALHM2HIST1H2BKHIST1H1CHIST1H2AEHIST1H3BRTN2HBP1CCDC28AHIST1H3DHIST2H2BEHIST1H2BDFGL2SETXCYP46A1CFLARSLC6A8TAF15GIT1MAPRE2STARRBM4BFABP4ZNF586GADD45APMAIP1PELONQO1GCLCSERPINE1MIR22HGOSGIN1GCLMAKR1C2AKR1C3SLC7A11TXNRD1HMOX1NEAT1LDLRDDIT4FAM117ANPC1BHLHE40HMGCS1HMGCRLPIN1IDI1SQLEMSMO1INSIG1

COMPOUNDS

GE

NE

S

Biclustering of gene expression data provides a simultaneous localsearch of a subset of genes for which a similar expression profiles weredetected across a subset of compounds


BICLUSTERING OF GENE EXPRESSION DATA

Target prediction-based clusteringof compounds + identification ofdifferentially expressed genes for acompound cluster of interest.⇒ Gives a subset of genesexhibiting consistent patterns over asubset of compounds.

⇒ A bicluster

Biclustering on Gene expressiondata using FABIA

identifies similar cluster ofcompounds and subset of genes

log

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⇒ A bicluster



log

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tein

be

nse

razid

e

LY−

29

40

02

tio

gu

an

ine

fasu

dil

ima

tin

ib

IDI1

INSIG1

MSMO1

LPIN1

SQLE

HMGCS1

NPC2

BHLHE40




⇒ A bicluster



log

2 c

oncentr

ation

−0

.20

.00

.20

.40

.60

.8

thio

ridazin

e

chlo

rpro

mazin

e

pro

chlo

rpera

zin

e

clo

zapin

e

triflu

opera

zin

e

fluphenazin

e

halo

peri

dol

fasudil

imatinib

vera

pam

il

dexve

rapam

il

felo

dip

ine

nife

dip

ine

nitre

ndip

ine

ara

chid

onyltri

fluoro

meth

ane

15−

delta p

rosta

gla

ndin

J2

ara

chid

onic

acid

cele

coxib

W−

13

metform

in

tetr

aeth

yle

nepenta

min

e

phenfo

rmin

phenyl big

uanid

e

rofe

coxib

LM

−1685

SC

−58125

dic

lofe

nac

4,5

−dia

nili

nophth

alim

ide

flufe

nam

ic a

cid

N−

phenyla

nth

ranili

c a

cid

pro

bucol

bute

in

bensera

zid

e

LY−

294002

tioguanin

e


FABIA Bicluster 1



⇒ A bicluster



log

2 c

oncentr

ation

Genes

HC

Fabia

−0

.20

.00

.20

.40

.60

.8

thio

ridazin

e

chlo

rpro

mazin

e

pro

chlo

rpera

zin

e

clo

zapin

e

triflu

opera

zin

e

fluphenazin

e

halo

peri

dol

fasudil

imatinib

vera

pam

il

dexve

rapam

il

felo

dip

ine

nife

dip

ine

nitre

ndip

ine

ara

chid

onyltri

fluoro

meth

ane

15−

delta p

rosta

gla

ndin

J2

ara

chid

onic

acid

cele

coxib

W−

13

metform

in

tetr

aeth

yle

nepenta

min

e

phenfo

rmin

phenyl big

uanid

e

rofe

coxib

LM

−1685

SC

−58125

dic

lofe

nac

4,5

−dia

nili

nophth

alim

ide

flufe

nam

ic a

cid

N−

phenyla

nth

ranili

c a

cid

pro

bucol

bute

in

bensera

zid

e

LY−

294002

tioguanin

e


FABIA Bicluster 1

DISCUSSION

The similarity of the biclustering results with the integrated approachshows that accounting for another source of information in the analysisof gene expression data gives a more refined search of ‘biclusters’containing a subset of ‘mechanistically’ related compounds regulatinga subset of functionally related genes.

Combining two sources of data provides a better understanding of themechanism of action of a compound cluster.

The approach is not only limited to the use of gene expression andtarget prediction data.


THANK YOU!


Documents

Rationalizing CMap Gene Expression Readouts via … CMap Gene Expression Readouts via Target Prediction Nolen Joy Perualila Non-Clinical Statistics Conference 2014 9 October 2014 RESEARCH