Precision Medicine in Rare Diseases: Across Con2nents and Disciplines Matthias Kretzler MD Div. Nephrology / Internal Medicine Computational Medicine and Bioinformatics University of Michigan Medical School
PrecisionMedicineinRareDiseases:
AcrossCon2nentsandDisciplines
Matthias Kretzler MDDiv. Nephrology / Internal Medicine
Computational Medicine and Bioinformatics
University of Michigan Medical School
The challenge for target identification in rare disease
• Descriptive disease categorization into syndromes with multiple pathogenetic mechanisms– Problems of ‘mixed bag’ diseases:
• Unpredictable disease course and response to therapy• Medicine as an ‘art of trial and error’
‚DieUrinbeschau‘UnknownmasterLowerRhinevalley,ca.1760
Description of
Renal Failure
anno 1760
• Descriptive disease categorization into syndromes with multiple pathogenetic mechanisms– Problems of ‘mixed bag’ diseases:
• Unpredictable disease course and response to therapy• Medicine as an ‘art of trial and error’
• Shift in our disease paradigms:– Mechanism based personalized patient management
• Define the disease process active in the individual patient
– Define molecular disease causality– Base prognosis on specific disease process– Target therapy to interfere with the mechanism
currently destroying organ function
The challenge for target identification in rare disease
The GOAL:Precision Medicine for Rare Disease
(modifiedfromMarianiandKretzler,NDT,2015)
The advantage of being a Nephrologist:Biopsy centred clinical and molecular phenotyping
Cohenetal.,2002
Research networks for molecular analysis of renal disease
Standardizedprotocolimplemented:>3800biopsiesprocuredandprocess,>13000paSentsincohorts
ERCB
Cohorts for molecular analysis of CKD2560 glomerular disease biopsies for gene expression studies24 European Centers
1165 Nephrotic Syndrome Patients in Registry708 incipient Nephrotic syndrome Cohort23 Centers in North-America
1200 CKD patients in 7 US-Centers in Mid-West
H3 CKD Africa, 8400 patients and controls in Sub-Saharan Africa with biosamples and prospective follow-up
180 Diabetic Nephropathy Cohort in Pima Native American Interventional Trial 98 protocol biopsies available for molecular analysis
760 IgA-Nephropathy Cohort at 1 PKU, Beijing, ChinaProspective biosamples and follow-up
680 incipient Nephrotic Syndrome Cohort
Molecular disease
definition:
Why do I have this disease?
Systems genetics concepts
Genetic variants affect
regulatory and proteomic machinery of the cell,
leading to disruption in a metabolic pathway
resulting in clinical trait / renal
disease phenotype.
B. Keller et al. Kidney Int, 2012
Integration SNPs – transcripts- clinical traits
B. Keller et al. Kidney Int, 2012
GWASCKDGen-candidatemarkers
1.FiltercandidategenesforGFRcorrelatedmRNAexpression
2.DetecSonofco-regulatedtranscriptsforeverycandidategene
3.DetecSonofenrichedpathwaysamongco-regulatedtranscripts
4.DetecSonCKD-associatedpathwayinterdependencies
SystemsGene*csofGlomerularDisease
Pathway network associated with shared CKDgen co-regulated transcripts. Spring embedded algorithm. Node size reflects degree of connectivity, edge thickness increasing with more genes shared among two pathways. Node color reflects number of transcript associated with pathway: (multiple=red, few=green)
Martini et al. J Am Soc Nephrol, 2014
SystemsGene*cs:SharedPathwaysinGlomerularFailure
Metabolism
Xenobiotic Metabolism SignalingAryl Hydrocarbon Receptor SignalingPPARgamma signalingPXR/RXR ActivationLPS/IL-1Mediated Inhibition of RXRFunctionTryptophan Metabolism…
Inflammation – Stress response
NRF2-mediated Oxidative Stress ResponseCdc42 SignalingNF-kappaB SignalingDendritic Cell MaturationJak/Stat SignalingCD28 Signaling in T helper Cells…
Definition of highly interconnected nodes (clusters)
FSGS pathways
Lupus Nephritis pathways
Differentially regulated genes were enriched in pathways indicated by the blue color.
Diabetic Nephropathy pathways
Early DN
Tub
uloi
nter
stiti
um c
ompa
rtm
ent
Progressive DN
Glo
mer
ular
com
part
men
t
Differential regulation Down-regulatation
Up-regulation
Control (LD+MCD): n=12 Early DN: n=24
Control (LD+MCD): n=8 Prog. DN: n=7
Control (LD+MCD): n=7 Prog. DN: n=11
Control (LD+MCD): n=11 Early DN: n=22
Cytoplasm
Nucleus
Cytoplasm
Nucleus
Cytoplasm
Nucleus
Cytoplasm
Nucleus
SharedChronicInflamma*oninCKDJak-Statpathwayac*va*on
Berthier et al., Diabetes. 2009;58:469
Repurposing into Glomerular Disease: Phase II trial of Jak 2 inhibition
BariciSnib,oralJAK2inhibitor:efficacyinRAandPsoriasis =>RepurposedinDiabeScNephropathy
• RCTbyEliLillyinDN• Primaryoutcome:ChangeinurineACRfrombaselineto24week
0 .6
0 .8
1 .0
1 .2
1 .4
T r e a tm e n t
Fo
ld c
ha
ng
e f
rom
ba
se
lin
e
P lo t o f L e a s t S q u a re M e a n s + /- S ta n d a rd E r ro r fo r 2 4 -h o u r U A C R
3 m o n th s 6 m o n th s 4 w k s w a sh o u t
M ix e d m o d e l re p e a te d m e a s u re s a n a ly s is o f lo g - tra n s fo rm e d d a ta w ith re s u lts b a c k tra n s fo rm e d .*p -v a lu e < 0 .0 5 ; * *p -v a lu e < 0 .0 1 b a s e d o n tre a tm e n t d iffe re n c e c o m p a re d to p la c e b o .
**
** **
P la c e b o 0 .7 5 m g Q D 0 .7 5 m g B ID 1 .5 m g Q D 4 m g Q D
* *
26 19 23 22 22 24 17 23 23 19 26 20 24 22 21
n -va lu e s :
Albuminuria Response Urinary IP-10: Target Engagement
From target identification to phase II completion in 42 months
Molecular disease
definition:
What will this
disease do to me?
Predic*onofCKDProgressionSelec*onofpa*entsatrisk
RiskPredicSon: – Use outcome to select predictor from genomic data set
A set of tissue based candidate
markers
Validation markers in independent cohorts
Validated markers for testing in body fluids
Further validation in larger cohort
From*ssuetourine:Non-invasiveBiomarkersforCKDprogression
Enrichment analysis: signaling pathways & Therapeutic targets
Treatmenttargets
Biomarkers
Ju et al, Science Trans Med, 2015.
Table X: Top 10 upstream regulators of eGFR slope correlated genes. Upstream Regulator
Molecule Type
p-value of overlap
Target molecules in dataset
EGF growth factor
2.09E-12 ACTN1,APOA1,B4GALT5,CCL20,CCND2,CD44,CDC42EP1,CLDN3,CLU,CTSD,DUSP6,ELF3,HIF1A,ICAM1,ID2,IER3,ITGB3, LCN2,LTF,MYC,MYCN,NCAN,NOS2,NRP1,PBK,PPIB,RRM2,SERPINA3,SLC37A1,SNAP25,SOX4,SPP1,SYP,TFPI2,THBS1, VCAN,VIM
TP53 transcription regulator
4.83E-11 ACTN1,ALOX5,ANLN,ANTXR1,ANXA1,APAF1,APOA1,ASF1B,ATL3,CCL2,CCND2,CD44,CLU,COL4A1,CTSD,CXCL1,DLGAP1, EIF4G3,ELF4,FHL1,FHL2,GDF15,GLIPR1,HIF1A,HS3ST1,ICAM1,ID2,IER3,ILK,KITLG,KRT18,MCM7,MET,MYC,MYOF,NOS2, NRP1,PBK,PDLIM1,PFN1,PMEPA1,PPFIBP1,PPIC,PTPRE,RRM2,SGPL1,SIVA1,SOD2,SPHK1,SPP1,TFPI2,TGFB2,THBS1, THBS2,TMSB10/TMSB4X,TNFSF9,TOP2A,TPX2,TSC22D3,UBE2C,VCAN,VIM,XRCC5,ZMAT3, ZYX
IL1B cytokine 3.45E-10 ADAMTS1,ANTXR1,ANXA1,CCL2,CCL20,CD44,CD74,CFB,CXCL1,CXCL6,ELF3,FAM129A,G0S2,GDF15,HIF1A,HMGA1, HSD11B1,ICAM1,IER3,ITGB3,ITGB8,LAMC2,LCN2,LHB,MARCKSL1,MMP7,MYC,NFKBIZ,NOS2,NRP1,OSM,PIGR,PSMB10, SERPINA3,SNAP25,SOD2,SOX9,SPP1,TAC1,TFPI2,THBS1,TSC22D3,UGCG,VCAN,VIM,ZYX
TGFB1 growth factor
1.36E-09 ACTN1,ALOX5,CCL2,CCL20,CCND2,CD207,CD44,CFL1,CLU,COL4A1,COL4A2,CTSD,CXCL1,DARC,DYRK2,ELF3,ELF4, FCER1A,FHL1,FNDC3B,FOSL2,GDF15,GGT6,GNG7,HIF1A,HMGA1,ICAM1,ID2,IER3,ILK,ITGB3,ITGB6,KCNG1,KDELR3, KDM5B,KITLG,KLF9,KRT18,LAMC1,LAMC2,LCN2,LOXL1,LOXL2,MET,MMP7,MYC,MYCN,MYOF,NCAN,NNMT,NOS2,NRP1, OSM,PDLIM7,PKIG,PMEPA1,PNMT,SERPINA3,SOD2,SOX4,SOX9,SPARCL1,SPHK1,SPP1,SYP,TGFB2,THBS1,TOP2A, TSC22D3,TUBA1A,VCAN,VIM,ZYX
IL6 cytokine 1.28E-08 ADAMTS1,ANXA1,APOA1,ARL4C,CCL2,CCL20,CD74,CLU,CXCL1,CXCL6,DUSP6,HIF1A,ICAM1,ID2,ITGB3,JAK1,KRT18, LCN2,LTF,MET,MPO,MYC,NOS2,PSMB10,REG1A,RRM2,SENP2,SERPINA3,SOD2,SPP1,STS,SYP,TAC1,THBS1,TOP2A, UBE2C,VIM,XRCC5
TNF cytokine 2.54E-08 ALOX5,ANXA1,APAF1,APOA1,CCL2,CCL20,CCND2,CD44,CFB,CLCF1,CLU,CXCL1,CXCL6,DUSP6,ELF3,FOSL2,G0S2, GDF15,HIF1A,HSD11B1,ICAM1,IDE,IER3,IFNAR2,ITGB3,ITGB6,JAK1,KITLG,LAMC1,LAMC2,LCN2,LGALS12,LIPE,MARCKSL1, MET,MMD,MMP7,MPO,MYC,NCAN,NFKBIZ,NNMT,NOS2,NRP1,OSM,PIGR,PSMB10,RRM2,SERPINA3,SOAT1,SOD2,SOX4, SOX9,SPHK1,SPP1,TAC1,TAPBP,TFPI2,TGFB2,THBS1,THBS2,TNFSF9,TSC22D3,UGCG,VIM,ZYX
CSF2 cytokine 9.96E-08 ALOX5,ANLN,ANXA1,APAF1,CCL2,CD63,CD74,CLCF1,CXCL1,DUSP6,GDF15,ICAM1,ID2,IER3,ITGB3,MET,MMD,MPO,MYC, NOS2,NRP1,OSM,RRM2,SOD2,SPP1,THBS1,TOP2A,TPX2,UBE2C
CEBPA transcription regulator
1.13E-07 ANXA1,ARL4C,CCL20,CCND2,FHL1,G0S2,GLIPR1,HSD11B1,ICAM1,ID2,IER3,LCN2,LTF,MALT1,MPO,MYC,MYF5,NRP1, PTPRE,SOD2,SPP1,TAC1,TGFB2,TRIB1,TSC22D3,VCAN
NFkB complex 1.48E-07 CCL2,CCL20,CCND2,CD44,CD74,CFB,CLCF1,CLU,CXCL1,CXCL6,ELF3,G0S2,GDF15,HGFAC,HIF1A,ICAM1,IER3,IFNAR2, ITGB8,LCN2,MYC,NFKBIZ,NOS2,SENP2,SERPINA3,SOAT1,SOD2,SPP1,TAC1,TFPI2,TRIM38,VIM,XRCC5
ERBB2 kinase 3.46E-07 CCL20,CCND2,CLDN3,COL4A1,DHH,DUSP18,DUSP6,ELF3,ETV6,HIF1A,ID2,ILK,ITGB3,KCNN4,KDM5B,LAMC2,LCN2,MYC, MYCN,NNMT,NOS2,NRP1,PMEPA1,RRM2,SERPINA3,SOX4,SPARCL1,SPINT1,TAPBP,THBS1,TOP2A,TUBA1A,UBE2C, VCAN,VIM
EGF,topup-streamregulatorofrenallosscorrelatedgenes
uEGF: Hazard Ratio for time to event in glomerular disease
PKU-IgAN 0.53 (0.37-0.69)
NEPTUNE 0.29 (0.15-0.58)
C-PROBE 0.27 (0.13-0.54)
Cohort HR (95% CI)
Adjusted HR of uEGF/Cr
0.0 0.5 1.0 1.5
PKU-IgAN 0.53 (0.37-0.69)
NEPTUNE 0.29 (0.15-0.58)
C-PROBE 0.27 (0.13-0.54)
Cohort HR (95% CI)
Adjusted HR of uEGF/Cr
0.0 0.5 1.0 1.5
PKU-IgAN
NEPTUNE
C-PROBE
Cohort
Adjusted HR of uEGF/Cr
0.0 0.5 1.0 1.5
A
B
C
Mul*variable-adjustedhazardra*osurinaryEGF/Crforoutcomes.HRsadjustedbyage,gender,eGFRandACRandobtainedbyindependentCoxregressionmodelsineachstudycohort.
Molecular disease
definition:
What can be done to help
me?
DefiningMolecularSubgroupsinGlomerularFailure
Define functional disease group => associate with outcome => predictors of group
CR*
Cluster1 Cluster2 Cluster3
GFR* 108mL/min/m2 79 42
UPCR 0.9 1.25 1.7
MarianiL,MarSniS,NairV,….
FSGS-MCDRenalBiopsygeneexpressioncluster
Cluster1 Cluster2 Cluster3
GFR 104 94 69
71/202genessign.Upregulated–concordantwithNEPTUNEgenesthatdifferenSatedcluster3there(125/131alsoupregulated)
ValidaSoninindependentERCBcohort
MarSniS,CohenC,NairV
ERCB
The Path Forward:Building a Precision Medicine Research Community
(modifiedfromMarianiandKretzler,NDT,2015)
Sharing Knowledge in the Informational Commons
• Disease community specific low threshold outreach tools
• Nephroseq: Kidney specific web based search engine
Combined data base and systems biology search engine for standardized analysis by the renal research community
DataCommonsforRareDiseaseResearchNetworks:TranSMARTDataIntegra*on&Analysis
The Challenge:• Data Integration & Analysis
Athey and Omenn, 2009
A solution:• tranSMARTOpen-sourcesoluSonforsharing,integraSon,standardizaSonandanalysisofheterogeneousdatafromcollaboraSvetranslaSonalstudiessupportedbyanopen-databiomedicalresearchcommunity
Overviewathip://lanyrd.com/2014/transmart/sdfqkf/
Making Vision Reality :Consortiums and Collaborative Science
in Rare Renal Disease
RPC2
RenalPrecompeSSveConsorSum
MolecularTargetDefiniSoninCKD
Sharedontology+biosamplingprotocolinindependentlygovernedcohortsusingindividualtranSMARTinstances:
De-idenSfieddataelementscaneasilybeshared,compared,andinterpretedamongnetworks
NEPTUNE
NorthAmerican
NephroScSyndrome
(FSGS/MCD/MN/IgAN)
CureGN
North-AmericanGlomerularDiseases
(IgAN,MN,FSGS,MCD)
ERCB
EuropeanCKD(DKD,HTN,LN,IgAN,FSGS,MN,MCD,VasculiSs)
NEPTUNEChina
ChineseNephroSc
Syndrome(MN,FSGS,MCD)
SystemsBiology ClinicalData
ModelSystems
NewTargetsandBiomarkers
BioinformaScsAdvisoryCommiiee
MindtheGap:RenalPre-Compe**veConsor*um(RPC2)for
moleculartargetiden*fica*on
Precision Medicine in Rare Disease
translate such insights into novel therapies (6). The success-ful use of whole-exome sequencing to identify the geneticbasis for response to cytotoxic T-lymphocyte antigen 4blockade in melanoma is one such example (7). In this land-mark study, a discovery cohort of only 25 patients (11 whoresponded to therapy and 14 who did not) was analyzedthrough a bioinformatic pipeline, which included sequenc-ing for mutational load in the tumor tissue, translatingmutations into altered peptides, and simulating MHC class1 binding, leading to the identification of a neoepitope sig-nature that correlated with clinical response. Furthermore,the Cancer Genome Atlas (TCGA) aims to establish a coreresource to facilitate this approach in other cancer diagno-ses. TCGA is funded by the National Cancer Institute andthe National Human Genome Research Institute and hascollected tumor and normal tissue from 11,000 patientswith 33 types of cancer to generate multiple large–scaledatabases of DNA, RNA, and proteomics. Among otheradvances, this database was used to identify biologicallybased subtypes of glioblastoma, setting up improved trialdesign, and enabling novel therapeutic developments (8,9).This article will highlight definitions, important limita-
tions, and approaches to large–scale dataset generation inglomerular disease (excluding genetics, which will be dis-cussed separately in this series). Examples of the successful
application of an integrative approach in glomerular dis-ease will be provided, with the anticipation that more suc-cess stories are expected in the future. The power ofanalyzing the human disease as a unified whole has inde-pendent value, but combining with parallel work in modelsystems, cell cultures, and observational and interven-tional clinical studies allows each approach to inform theother and expedite discovery and clinical translation.
Generation of Large-Scale Data in GlomerularDiseaseGeneration of multilayered large–scale datasets for glo-
merular disease begins to address the goals of precisionmedicine for nephrology (Figure 1). Not unlike oncology,there are unique advantages of applying this approach tokidney disease, where the biospecimens needed to gener-ate the datasets highlighted in Figure 1 are frequently ob-tained during routine clinical care. For example, biopsytissue (morphology) not only provides valuable structuralinformation currently in clinical use, but also offers a win-dow into the molecular mechanisms active in the tissue ofthe affected organ (transcriptome). Another advantage isthe easy access to urine, which can be tested repeatedlyand serves as a potential liquid biopsy to capture changes
Figure 1. | Large–scale dataset integration across the genotype-phenotype continuum. Integrative biology approaches can be initiated withany dataset across the genotype-phenotype continuum and then, evaluated against the other domains to help identify novel risk factors,pathogenic pathways, or biomarkers of progression and therapeutic response. As a result, novel mechanisms can be further investigated inmodel systems to confirmfindings and test therapies.Mechanistic–based patient groups can be identified and targeted for enrollment in proof ofconcept clinical trials.
2 Clinical Journal of the American Society of Nephrology
Mariani,Pendergran,Kretzler,cJASN,2016
• Independently governed, jointly designed cohorts for mechanistic patient stratification
• Mechanistic disease pathways cutting across multiple rare disease mapped (NCATS-RDCRN)
=> Risk based disease stratification=> Molecular target identification
• Repurposing successfully implemented and scalable into adequately targeted patient populations
Team science of translational medicine
ERCBGrantsupportby