atlas of cancer signalling network and navicell · 2017-10-26 · “computational systems biology...

“ComputationalSystemsBiologyofCancer”U900InstitutCurie/INSERM/Ecole desMinesParisTech,Paris,France

InnaKuperstein

ATLASOFCANCERSIGNALLINGNETWORKANDNAVICELL

SYSTEMSBIOLOGYRESOURCESFORSRUDYINGCANCERBIOLOGY

CANCER:ACOMPLEXSYSTEM

AtlasofCancerSignalling NetworkResourceofknowledgeonmolecularmechanismsandanalyticaltool

Atlas of Cancer Signaling Networks

[email protected]

http://acsn.curie.fr

MapandMap

PresentPast


AtlasofCancerSignalling NetworksResourceofknowledgeonmolecularmechanismsandanalyticaltool

AgilentprojectGARUDAproject

AtlasofCancerSignalling Network:navigatingcancerbiologywithGoogleMapsKupersteinI,BonnetE, NguyenHA,CohenD,Grieco L,Viara E,Fourquet S,CalzoneL,RussoC,Kondratova M,Dutreix M,Barillot EandZinovyev A.Oncogenesis, 2015

[email protected]


VisualsyntaxSystemsBiologyGraphicalNotation(SBGN)

Biologicalmoleculesandinteractionsrepresentation

Standardsandtoolsforsignalling networksconstruction

Tool:CellDesignerDiagrameditorforsignalling networksrepresentation

SystemsBiologyMarkupLanguage(SBML)Computationalrepresentationofbiochemicalprocesses

K1*X0

Standardsandtoolsforsignalling networksconstruction

ü Cancer-relatedü Manuallycuratedü Comprehensiveandup-to-dateü Interconnectedü Browsable andzoomable

AtlasofCancerSignalling Network:navigatingcancerbiologywithGoogleMapsKupersteinI,BonnetE, NguyenHA,CohenD,Grieco L,Viara E,Fourquet S,CalzoneL,RussoC,Kondratova M,Dutreix M,Barillot EandZinovyev A.Oncogenesis, 2015

Applicablefor:ü Dataintegrationü Network−baseddataanalysisü Modelingsyntheticinteractionsü Predictiondrugresistancemechanisms

ü 5mapsofbiologicalprocessesü 52functionalmodulesü 4826reactionsü 2371proteinsü 5979chemicalspeciesü 2822references

[email protected]



Ongoing:signalingmapsinconstructionØ ImmuneresponseandtumormicroenvironmentØ RegulatedcelldeathØ TelomeremaintenanceØ AngiogenesisØ CentrosomeregulationØ CellpolarityØ DNAreplication

Intercellularnetwork Intracellularnetwork

AtlasofCancerSignalling NetworkResourceofknowledgeonmolecularmechanismsandanalyticaltool


Regulatedcelldeathmap:manymodestodie

https://navicell.curie.fr/maps/pcd/master [email protected]


Initiation(reversible)STRESSRESPONSEANTIOXIDANTRESPONSEDNADAMAGERESPONSEERSTRESSSTARVATION-AUTOPHAGY

LIGAND-RECEPTORDEATHRECEPTORPATHWAYSTRAILRESPONSEFASRESPONSETNFRESPONSE

DEPENDENCERECEPTORMETABOLISMCELLMETABOLISMFATTYACIDBIOSYNTHESISGLUCOSEMETABOLISMGLUTAMINEMETABOLISMPENTOSEPHOSPHATEPATHWAYPORPHYRINMETABOLISM

MITOCHONDRIALMETABOLISMOXIDATIVEPHOSPHORYLATIONANDTCACYCLEMITOCHONDRIALGENES

Signaling (rewirable)APOPTOSISNECROPTOSISFERROPTOSISPARTHANATOSPYROPTOSIS

Execution(irreversible)MOMPREGULATIONMITOCHONDRIALPERMEABILITYTRANSITIONCASPASESRCDGENES

Layers- 3Metamodules- 15Modules- 14

Mapcontent

Chemicalspecies- 2657Proteins- 1008Reactions- 2020Articles- 738

Networkofmodules

Annotatedcelltypes

ü Macrophagesü MDSCü Neutrophilsü Dendriticcellsü Neutrophilsü Mastcells

Pro-tumor Anti-tumorpolarization

Metamapofinnateimmuneresponseincancer


TUMORRECOGNITION

IMMUNESTIMULATION

TUMORKILLING

TUMORGROWTH

IMMUNESUPPRESSION

RECRUITMENTINHIBITIONOFTUMORRECOGNITION

CORESIGNALING

Annotatedarticles

Layers- 3Metamodules - 4Modules- 20

Mapcontent

Googlemap

NaviCell:aweb-basedenvironmentfornavigation,curation andmaintenanceoflargemolecularinteractionmapsKupersteinI,CohenDP,Pook S,Viara E,CalzoneL,Barillot E,Zinovyev A. BMCSystemsBiology,2013

Dataintegration

[email protected]

http://navicell.curie.fr

üGoogleengine(navigation,search,markers,calloutwindow)ü Semanticzoomingü Entityannotationpost

üDataintegrationandvisualization(online)ü Entityneighborhoodstudyü Functionalanalysis(enrichmentofmodules)

Blog

Semanticzoom

NaviCell WebServiceforNetwork-basedDataVisualizationBonnetE,Viara E,KupersteinI,CalzoneL,CohenDPA,Barillot E,Zinovyev A.NucleicAcidResearch,2015

NaviCell = Map(GoogleMapsengine)+Blog(WordPress)+ToolBox

Awebtoolfornavigation,curationanddataanalysisinthecontextofsignalling networks

Towardsgeographicinformationsystemformolecularbiology

DatatypesContinuousdata:mRNAexpressionmicroRNAexpressionProteinexpressionCopynumber

Discretedata:Post-translationalmodificationsMutationdataGenelistCopynumber

NaviCom:Pythonpackageandwebinterfacetocreateinteractivemolecularnetworkportraitsusingmulti-levelomicsdataDorelM,Viara E,Barillot E,Zinovyev AandKuperstein I.DATABASE,Biocuration issue,2017 [email protected]

http://navicell.curie.fr

NaviCell WebServiceforNetwork-basedDataVisualizationBonnetE,Viara E,KupersteinI,CalzoneL,CohenDPA,Barillot E,Zinovyev A.NucleicAcidResearch,2015

http://[email protected]

Data$type$ Visualiza.on$mode$ Data$display$ Units$

mRNA%expression% Map%staining% Level%

Gene%copy%number% Heat%map% Count%

Muta<on%data% Glyph%1% Frequency%

Methyla<on%data% Glyph%2% Intensity%

miRNA%expression% Glyph%3% Level%%

Protein%expression%% Glyph%4% Level%

Up

Down

MolecularportraitofBreastInvasiveCarcinomavisualizedonCellcyclemap

expression-mapstainingcopynumber-heatmapmutations-bluetrianglemethylation-pinkdiamondproteomics-yellowcircle

Visualizationsettings

NETWORKSINPRE-CLINICALRESEARCH

Cisplatin DNAcross-links

DNAdamageaccumulation Tumorcell

death

!!!Resistance DNArepairmachinery

TreatmentapproachesincancerGenotoxic drugs

ApproachExploitspecificitiesintumorcellswhichdisplayabnormalexpressionorfunctionofonegenefromsyntheticlethalpair.Targetingsyntheticlethalpartnerallowsselectivekillingoftumorcells.

BRCA/PARPsyntheticlethalpair

TreatmentapproachesincancerTargeteddrugs:syntheticlethalityparadigm

!!!Resistance DNArepairmachinery

SyntheticlethalitybetweentwogenesExtremecaseofnegativegeneticinteractions

Dobzhansky Genetics(1946)

Syntheticlethality:mutationsinnumberofgenesproducingaphenotypethatissignificantlydifferentfromeachmutation'sindividualeffects

GeneA GeneB Cellfate

Syntheticgeneticinteractions

Negativegeneticinteraction:aggravating effectPositivegeneticinteraction:ameliorating effect

Syntheticlethalgeneset=interventiongeneset

Questions:-Whatexplainsresistancetogenotoxic treatmentincancer?-Canwerestoresensitivitytotreatment?-Howtoidentifypatient-specifictargetstorestoresensitivitytogenotoxic treatment?

Interventiongenesetsforcancerpatientsresistanttogenotoxic treatment

Signalling networks forinterventionstrategydesignStructuralanalysis

IntactDNAlocus

RepairedDNA

Doublestrandbreak

Drug1

Drug2

Drug n

PointsofPARPinvolvementinDNArepairnetwork

DNArepairnetwork

OCSANA:anintegrativepathwayanalysisto revealinterventiongenesets

PrioritizingthelistofmasterregulatorsIdentifyingpointsoffragilityinthenetworkIdentifyingsyntheticlethalcombinations

Target Elementary Pathways

Elementary Nodes

Computation Time

Total Number of MinHitSets

MinHitSetsSize 1

MinHitSets Size 2

MinHitSets Size 3

MinHitSets Size 4

MinHitSets Size 5

Comments

KPNA2 2300 198 5.51 252 0 0 108 42 102

MAD2L1 2214 131 7.43 74 9 5 12 7 42

CFL1 (Cofilin)

15 71 7.40 38336 6 0 160 4848 33322

CDC20 198 126 2.04 74 8 5 12 7 42

XPO1 529 171 2.40 112 0 6 30 32 44 Antiapoptotic

CyclinB1 1476 121 1.21 74 1 5 12 7 42 Downregulated by BRCA1 and p53. Upregulated by USF-1 (which is upregulated by BRCA2)

MAD2L1&BUB1B

246 119 0.24 86 21 0 12 7 42 Study of Combinations

Conf.information

Minimalcutsets

OCSANA:optimalcombinationsofinterventionsfromnetworkanalysisVera-Licona P,BonnetE,Barillot E,Zinovyev A.Bioinformatics,2013

Survival to DT01 (%/NT)

Surv

ival

to O

lapa

rib (%

/NT)

60 80 100 1200

105060708090

100110120

MDAMB436

HCC1937BC227

HCC38HeLa BRCA1

HeLa BRCA2

BC173

MDAMB468

HCC1143

BT20 MDAMB231

MCF7

HCC1187

HCC70

HeLa CTL

184B5

MCF10AMCF12A

Trypan blue staining andcounting 10dafter treatment

Survival of Triple negative Breast cancer cell lines to Dbait or Olaparib

Tumorheterogeneity:explainingresistancetotargetedtreatment

DNArepairinhibitorsDbait andOlaparib

DrugDrivenSyntheticLethality:bypassingtumorcellgeneticswithacombinationofDbait andPARPinhibitorsJdey W,ThierryS,Russo C,Devun F,AlAbo M,Noguiez-Hellin P,Sun JS,Barillot E,Zinovyev A,KupersteinI,Pommier Y,Dutreix M.ClinicalCancer Research,2016

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

p−value (a.u.)

Prec

enta

ge o

f ove

rlapp

ing

gene

s (%

)

●

●

●

●

●

●

●

0.000 0.002 0.004 0.006 0.008

0.00

00.

001

0.00

20.

003

p−value (a.u.)

Prec

enta

ge o

f ove

rlapp

ing

gene

s (%

)

!

Gene

Correlation with Survival

to DT01 ACSN

modules Gene

Correlation with Survival to Olaparib

ACSN modules

PPP2R5C 0.8725275 4 MYH6 0.8406593 2 RAP1GDS1 0.8637363 2 CSK 0.83296704 4 PTEN 0.83516484 4 PFKFB3 0.83296704 3 CCNA1 0.8186813 3 MAPK6 0.8065934 3 NOTCH1 0.8186813 2 UBE2Q2 0.8021978 3 TEAD1 0.7758242 3 FECH 0.789011 2 PIK3CA 0.7692308 3 TP53 0.7846154 9 STK11 0.7582418 2 COPS2 0.7802198 2 ROCK1 0.75384617 3 CTNND2 0.7802198 2 HRAS 0.73626375 5 GNB5 0.7714286 3 DEF6 0.73186815 2 TAB2 0.7692308 5 CAPN2 0.72747254 4 NFE2L1 0.7582418 3 CUL1 0.72307694 5 PRKCH 0.75274724 3 ARHGEF1 0.70357144 2 PPP2R5E 0.74945056 3 BCL2L11 -0.72747254 6 UBE2Z 0.74945056 3 GADD45B -0.73186815 2 FKBP8 0.74505496 2 FZD5 -0.73186815 2 PIAS1 0.73626375 3 NGEF -0.73186815 2 ITGB4 0.72747254 2 GNG7 -0.73626375 3 GCLC 0.72747254 2 NR4A1 -0.75384617 2 RPS6KA5 0.7214286 3 MYCN -0.7714286 2 ATP5J -0.74505496 2 FYN -0.7802198 4 ARHGAP31 -0.75384617 2 GRK6 -0.7978022 2 CDH2 -0.7692308 5 PPP2R5D -0.8131868 4 PRKAG3 -0.7802198 4 NDUFA6 -0.8153846 2 LAMA4 -0.789011 2 HSPA1A -0.81978023 2 PRDX2 -0.8021978 2 FLT1 -0.82417583 4 ACTA1 -0.8131868 6 COL1A1 -0.82417583 2 GADD45G -0.8131868 2 AKT3 -0.8296703 4 PRKCB -0.82857144 4 ROR2 -0.85494506 2 TCF3 -0.83296704 5 COL6A2 -0.88461536 2

UniquegenesrobustlycorrelatedwithresistancetoDbait (left)orOlaparib (right)inTNBCcelllines.ThelistsarerestrictedtogenesenrichedatleastintwoACSNmodules.

Red - positivecorrelation, green- negativecorrelationwithsurvivaltodrugs.Thevaluesarecorrelationcoefficients(Spearmanr;Pvalue<0.004)betweengeneexpressionandsurvivaltoDbait orOlaparib

UniquegenesrobustlycorrelatedwithresistancetoDbait orOlaparib

intriplenegativebreastcancercelllines


ACSNmodulesenrichment:differentregulationbyDbait orOlaparibintriplenegativebreastcancercelllines

CoverageofACSNmodulesbygenesrobustlycorrelatedwithresistancetoDbait

CoverageofACSNmodulesbygenesroubastly correlatedwithresistancetoOlaparib

Correlationwithsurvivaltotreatment:Red– positiveGreen - negative


Dbait resistant Olaparib resistant

mRNAexpression

Up

DownMutationsCopynumber loss

gain

MolecularportraitsofresistancetoDbait orOlaparib:multi-omics dataintegrationintriplenegativebreastcancercelllines


Mechanismsofaction:trapDNArepairmachinerywithcombinationofinhibitorsDbait andOlaparib

Olaparib Dbait

SurvivalofbreastcancercelllinestocombinedtreatmentwithDbail andOlaparib

SurvivalofcontrolcelllinestocombinedtreatmentwithDbail andOlaparib

Olaparib

DT014.8µM+Olaparib

Exp.Add.DT014.8µM+Olaparib

Olaparib([µM](

0(20(40(60(80(

100(120(

0( 0.1( 1(

MDAMB231(

0(20(40(60(80(

100(120(

0( 0.1( 1(

BC173(

0"20"40"60"80"100"120"

0" 0.1" 1"

MCF10A'

0"20"40"60"80"

100"120"

0" 0.1" 1"

MCF12A'

Olaparib"[µM]"""""

"""""""""""""""""""""Living"cells"(%

/NT)"


Livingcells(%

/NT)

Macrophage

TumormicroenvironmentCell-typespecificmaps

Dendritic cell

Cancer-associated fibroblast

Naturalkiller

Multi-cellular system

Networkofmodules

Annotatedcelltypes

ü Macrophagesü MDSCü Neutrophilsü Dendriticcellsü Neutrophilsü MastcellsPro-tumor Anti-tumorpolarization

Metamapofinnateimmuneresponseincancer


TUMORRECOGNITION

IMMUNESTIMULATION

TUMORKILLING

TUMORGROWTH

IMMUNESUPPRESSION

RECRUITMENTINHIBITIONOFTUMORRECOGNITION

CORESIGNALINGLayers- 3Metamodules - 4Modules- 20

Mapcontent

Mixtureofindependentsources:cocktailpartyproblem

å=

×»m

i

gFi ssampleFiActivityassampleggeneExpression

1),(),(

Factor 1

Gene 1 Gene 2 Gene 3 Gene n…

21Fa 3

1FanFa 1

11Fa

Factor 2 Factor m…

12Fa 2

2Fa32Fa

nFa 2

1Fma

2Fma3Fma

nFma m << n

• Performsmatrixfactorizationbyminimizingmutualinformationbetweenfactors

• Stabilityanalysis,determiningtheoptimalnumberofcomponents(Kairov etal,2017)

• Toolboxofmethodsforinterpretingtheresultingmetagenes andmetasamples

• GraphicaluserinterfaceimplementedinJavaPossibilitytovisualizeICAcomponentsontopofdiseasemapsinNaviCell

IndependentComponentAnalysis(ICA)deconvolutionofomicsprofiles

https://github.com/LabBandSB/BIODICA

Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypesBiton A,Bernard-Pierrot I,LouY,Krucker C,Chapeaublanc E,Rubio-PérezC,López-Bigas N,Kamoun A,Neuzillet Y,Gestraud P,Grieco L,Rebouissou S,deReyniès A,Benhamou S,Lebret T,SouthgateJ,Barillot E,AlloryY,Zinovyev A,Radvanyi F.CellReports,2014

Meta-analysisofindependentfactorsinsolidcancers

6671 tumor samples (20000-30000 genes in each sample), 22 datasets, 9 cancer types

Metaanalysisofmultipleindependentdatasets

Transcriptome data set 1



metagene 1metagene 2

metagene 3 metagene Imetagene II

metagene III metagene Ametagene B

metagene C

Hospital1 Hospital2 Hospital3

1

I

A

2 C

II

B

Metagene correlationgraph Visualisation inNaviCell

ROMA:calculatingpathwayactivitiesfromomicsdata

• Quantifiesgenesetoverdispersion• Basedonsimplelinearmodelofgene

regulation• Assignsascoretoeachsample

(weightedmean)• Candefinepositiveandnegative

pathwayregulatorsaspriorknowledge• Thescorescanbevisualizedontopof

thediseasemapsinNaviCell• JavaandRimplementation

http://sysbio.curie.fr/softwareROMA:RepresentationandQuantificationofModuleActivityfromTargetExpressionDataMartignetti L,CalzoneL,BonnetE,Barillot E,Zinovyev A.FrontiersinGenetics,2016

Class2:Cytokinesand

chemokinesproduction

up-regulation

down-regulation

updown

Cancerassociatedfibroblastsheterogeneityinmetastaticmelanomasamples

Class1:Growthfactorsproduction

Class3:Matrixregulation

CLASS1

CLASS2

CLASS3

CLASS4

Singlecell transcriptome datafrom:Dissecting themulticellular ecosystem ofmetastatic melanoma bysingle-cell RNA-seqTirosh Ietal,Science2016

IdentificationoffourCAFclassesusingtranscriptomicdata

IC1_minus

IC1_plus

ICAmethod:BitonAetal,Independentcomponentanalysis uncovers thelandscape ofthebladder tumor transcriptomeandreveals insightsinto luminal andbasalsubtypes.Cell Rep 9(4):1235-45,2014

CancerassociatedfibroblastcellmapVisualizationofmoduleactivitydifferencebetweenthreeclasses

PC1

PC2

NaturalkillercellmapVisualizationofmoduleactivitydifferencebetweenNK1andNK2classes

MetamapofinnateimmuneresponseincancerVisualizationofmoduleactivitydifferencebetweenNK1andNK2classes

Innateimmunecellpolarizationandheterogeneityinmetastaticmelanomasamples

Naturalkillercells

up-regulation

down-regulation


IdentificationoftwoNKclassesusingtranscriptomicdata

CLASS1

CLASS2

ICAmethod:BitonAetal,Independentcomponentanalysisuncovers thelandscape ofthebladder tumortranscriptome andreveals insightsinto luminal andbasalsubtypes.Cell Rep 9(4):1235-45,2014

Innateimmunecellpolarizationandheterogeneityinmetastaticmelanomasamples

Macrophages

up-regulation

down-regulation

PC1

PC2

MacrophagecellmapVisualizationofmoduleactivitydifferencebetweenMK1andMK2classes

MetamapofinnateimmuneresponseincancerVisualizationofmoduleactivitydifferencebetweenMK1andMK2groups

IdentificationoftwoMKclassesusingtranscriptomicdata

CLASS1

CLASS2


ICAmethod:BitonAetal,Independentcomponentanalysis uncovers thelandscape ofthebladder tumor transcriptomeandreveals insightsinto luminal andbasalsubtypes.Cell Rep 9(4):1235-45,2014

Colon cancer genetics

?

From Fearon&Vogelstein, Cell 1990

Coloncancerisassociatedwith:MutationsinAPC gene(b-catenin/WNT pathway)MutationsinRASandp53 genesLessfrequentmutationsinmanyotherpathways(Notch,MLH,PTEN,SMAD,etc.

Experimentalsystem:mousewithapossibilityofconditionalmutationsingut(villin-CreERT2tamoxifen-dependentintestine-specificrecombination)

Question:Canwefindacombinationofmutationsinpathwaysleadingtorapidmetastasis?

Predictionofinvasivephenotypeinmicemodelofcoloncancer

Fromsignalingnetworkanalysistohypothesisandtoexperimentalvalidation

LineagetracinginmouseintestinerevealsEMTandmetastasisinvivofollowingNotchactivationandp53deletionChanrion M*,KupersteinI*,Barrière C,ElMarjou F,CohenD,Vignjevic D,Stimmer L,Paul-Gilloteaux P,Bièche I,DosReisTavaresS,Boccia GF,Cacheux W,Meseure D,Fre S,Martignetti L,Legoix-NéP,GirardE,Fetler L,Barillot E,Louvard D,Zinovyev A andRobine S.NatureCommunications,2014NetworkbiologyelucidatesmetastaticcoloncancermechanismsKupersteinI,Robine SandZinovyev A.CellCycle,2015

Epithelial-Mesenchymal Transition(EMT)anecessaryconditiontoappearanceofmetastases

From Friedl and Alexander, Cell, 2011


Visualizingcentralplayers

Pathanalysis

Modelreduction

Comprehensivenetworkofepithelialtomesenchymal transition(EMT)regulation Hubregulators

StructuralanalysisofEMTregulationnetwork


Syntheticinteractionbetweenp53andoverexpressionofNotch(NICD)leadstoEMT

NICD

NICD is up p53 is down


EMT

NORMAL

MicemodelofcoloncancerwithmetastasesindistantorgansNICD++/p53-- mice

• Allmice developed adenocarcinoma 15months postinduction• Compared tosinglemutants,showdrastic decrease insurvival• 50%developed peritoneal carcinomatosis,23.3%infiltrated lymph nodes,10%liver metastases

• Patternsofdistantmetastases resembles primary tumours• GFPstaining confirms theintestinalorigin ofthecancercells from primary andsecondary organs


Invasivecoloncancermodelrelevantforhumandisease

Transcription factor

significant differential expression

module activity

ExpressionofNotch,P53andWnt downstream targets inTCGAcolontumours


Conclusions:toolsACSN is a resource of cancer signalling knowledgeComprehensive map of molecular interactions in cancer based on the latest scientific literature

NaviCell: Interactive web-based environment for navigation data integration and visualizationTool for navigation and curationGoogle Maps engine and semantic zooming mechanismAssociated with blog system for discussion forum around the ACSN contentVisualization of omics data

OCSANA: tool for network analysisSuggest intervention gene setsRationalize drug combination

Independent component analysis: deconvolution of complex dataMeta data analysisCancer heterogeneity

ROMA: calculating pathway activities from omics dataDeregulated functional modulesMolecular portraits of cancer

FromaBiologicalHypothesistotheConstructionofaMathematicalModelCohenD,KupersteinI,BarillotE,ZinovyevA,CalzoneL.Methodsinmolecularbiology,2013BiologicalnetworkmodellingandprecisionmedicineinoncologyCalzoneL,KupersteinI,CohenD,GriecoL,BonnetE,ServantN,HupeP,ZinovyevA,BarillotE.Bull.Cancer,2014Theshortestpathisnottheoneyouknow:applicationofbiologicalnetworkresourcesinprecisiononcologyresearchKupersteinI,GriecoL,CohenD,ThieffryD,ZinovyevA,BarillotA.Mutagenesis,2015Network-basedapproachesfordrugresponsepredictionandtargetedtherapydevelopmentincancerDorelM,BarillotE,ZinovyevAandKupersteinI.BiochemBiophysResCommun,2015

Conclusions:applications

APPLICATION 3: Tumor heterogeneity: find and characterize cells sub-populationCell type-specific maps together with meta-map allow to study heterogeneity of cell populations intumor microenvironment.There are multiple sub-classes in the population of TME cells demonstrating subtle differences in the setof implicated functional modules that dictate the heterogeneity.

FromaBiologicalHypothesistotheConstructionofaMathematicalModelCohenD,KupersteinI,BarillotE,ZinovyevA,CalzoneL.Methodsinmolecularbiology,2013BiologicalnetworkmodellingandprecisionmedicineinoncologyCalzoneL,KupersteinI,CohenD,GriecoL,BonnetE,ServantN,HupeP,ZinovyevA,BarillotE.Bull.Cancer,2014Theshortestpathisnottheoneyouknow:applicationofbiologicalnetworkresourcesinprecisiononcologyresearchKupersteinI,GriecoL,CohenD,ThieffryD,ZinovyevA,BarillotA.Mutagenesis,2015Network-basedapproachesfordrugresponsepredictionandtargetedtherapydevelopmentincancerDorelM,BarillotE,ZinovyevAandKupersteinI.BiochemBiophysResCommun,2015

APPLICATION1:Resistancetogenotoxicdrugs:InterventiongenesetsRetrieveprinciplesofsignalingcoordinationandsyntheticinteractionsFindsignalingnetworkfragilitiesinthediseaseSuggestinterventiongenesets

APPLICATION 2:SynergybetweentargeteddrugsInterpretomicsdatafromcelllineswithdifferentsensitivitytotargeteddrugsinthecontextofsignalingnetworkRetrievederegulatedfunctionalmodulesSuggestsynergybetweendrugs

APPLICATION4:InvasivephenotypeincoloncancerinmicemodelPredictionofnon-intuitivecombinationofsyntheticallyinteractinggenesFormulatinghypothesisonmolecularmechanismandexperimentalvalidationofprediction

ComputationalSystemsBiologyofCancerGroupInstitutCurie,Paris,FranceBarillot EmmanuelBiton AnneBonnetEricCalzoneLaurenceCohenDavidCzerwinska UrszulaDorelMathurinFourquet SimonGrieco LucaKondratova MariaMonraz GLCristobalNguyễn Hiển AnhPicat LeoSompairac NicolasPook StuartRavelJean-MarieRussoChristopheVera-Licona PaulaViara EricZinovyev Andrei

AcknowledgementsInstitutdeRecherches Servier,Croissy sur Seine,FranceGordonTuckerFranciscoCruzalegui

SystemsBiologyInstitut,Tokyo,JapanHiroakiKitanoSamik GhoshYukiko Matsuoka

KEGGteam,KyotoUniversity,Kyoto,JapanMinoruKanehisa

UniversityofCalifornia,Davis,CA,USWolf-DietrichHeyer

AgilentTechnologies,Inc.,SantaClara,CA,USNigelSkinnerNortonKitagawaAntoni WandyczCarolinaLivi

InstitutCurie,Paris,FranceDanielLouvardSylvieRobineCharrion MaiaCelineBaldeyronThierryDuboisFatimaMechta-GrigoriouYannKiefferVassiliSomelisPhilippeChavrierMarc-HenriSternTatyana PopovaChristopheLeTourneauMaudKamal

InstitutCurie,Orsay,FranceSimonSauleLauraDucielMarieDutreixJdey WaelMounira Amor-GuéretJanetHall

InstitutGustave Roussy,Villejuif,FranceMuratSaparbaevPilippo RoselliPatriciaKannouche

EcoleNormaleSupérieureDépartementdeBiologie,Paris,FranceDenisThieffryWassim Abou-Jaoudé

CentredeRecherchedesCordeliers,Paris,FranceGuidoKroemerLorenzoGalluzzi

atlas of cancer signalling network and navicell · 2017-10-26 · “computational systems biology...

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