New research and drug discovery paradigms for asthma

Ian M Adcock

National Heart and Lung Institute,Imperial College London

Asthma: size of the problem

• High prevalence: 20% children, 10-15% adults

•Inflammatory disease •AHR – ‘twitchy airways’•Chronic and incurable

• Increasing prevalence world-wide

• Mortality stable or increasing

• High cost: 1-2% UK health budget>$12bn/yr in USA – increase with biologics14% prescribing costs in UK

Normal Inflammed• Hyperaemia• Swelling• Narrowing

BRONCHOSCOPY OF AN ASTHMATIC PATIENT

Asthma Pathophysiology

Allergen

eosinophilCD4+ Th2 cell

vasodilation & angiogenesis Airway

Hyperresponsivness

macrophage/ dendritic cell mast cell

epithelial denudation subepithelial fibrosis

Normal

Asthma

(Busse and Lemanske, NEJM, 2001)

4444

Smooth muscleMucous plug

BasementmembraneEpithelium

Mucous glands

Normal 

airway

Asthmatic 

airway

But….. Not all asthmatics are allergic, asthma is a syndrome, severe asthmatics do not respond to corticosteroidslack of mechanistic understanding of disease – access to disease cells/tissues

Good predictive models of Asthma

Explain lack of new drugs for the treatment of asthma – Pt subsets

The move away from animal models towards a systems approach in man and human cell/tissue disease models

• Does U‐BIOPRED contribute to a clearer understanding of disease causes, pathogenesis, disease progression, or suggest new targets for drug discovery?

• What kind of data does your research provide that might be used instead of relying on animal data? 

• What are the current limitations to human‐specific models and advanced techniques in this field? 

• What is needed (techniques, new models, targeted funding, science policy changes) to overcome those limitations?

www.ubiopred.eu

How does U‐BIOPRED fit into new paradigm?

www.ubiopred.eu

Wheelock et el. ERJ 2013;42:802Wheelock et el. ERJ 2013;42:802

UBIOPRED clusters based on clinical features

• Cluster 1: Moderate‐to‐severe asthma; well‐controlled; medium to high dose ICS 

• Cluster 2: Severe asthma; Late onset asthma; smoker or ex‐smoker; airflow obstruction; high dose ICS

• Cluster 3: Severe asthma; Oral corticosteroid‐dependent; airflow obstruction; high dose ICS

• Cluster 4: Severe asthma; Female; obese; frequent exacerbations; high dose ICS

Better clinical definition of disease needed based on mechanistic understanding

www.ubiopred.eu

U‐BIOPRED HandprintsFingerprint• Biomarker signature derived from the           combination of clinical data and                        high‐high‐dimensional biomarker data collected        within a single technical platform

Handprint• Biomarker signature derived from the               combination of clinical data and                              high‐dimensional biomarker data collected         within multiple technical platforms

Heat map of DEGs between healthy and asthmatic participantsin blood

www.ubiopred.eu

Blood‐related omics:Blood transcriptomicsSerum SomalogicSOTON Serum proteomicsUrine Eicosanoids

227 Patients in common

U‐BIOPRED Blood Asthma Handprint

BH1 (N=47) BH2 (N=47) BH3 (N=53) BH4 (N=39) BH5 (N=41) p-value

Cohort SA: 21; SAS: 8, MMA: 18 SA: 27; SAS: 13; MMA: 16 SA: 26; SAS: 8; MMA: 16 SA: 15; SAS: 8; MMA: 16 SA: 31; SAS: 9; MMA: 1 <0.001 3

Gender (% Female) 59% 81% 45% 54% 44% 0.002 3

FEV1 (% predicted) 71.2 ± 22.4 67 ± 22.2 76.6 ± 20.4 82.9 ± 22.1 65.9 ± 19.9 0.001 1FEV1 / FVC 0.64 ± 0.13 0.61 ± 0.13 0.65 ± 0.14 0.68 ± 0.09 0.60 ± 0.12 0.02 1

White blood cell counts (x10^3/ul) 6.41 ± 1.69 8.54 ± 2.96 7.04 ± 1.76 7.19 ± 2.06 9.48 ± 2.23 < 0.001 1

Blood neutrophils (%) 55.7 ± 9.98 61.1 ± 9.57 59.1 ± 10.9 56.8 ± 7.72 68 ± 11.9 < 0.001 1Blood lymphocytes (%) 32.4 ± 8.78 26.2 ± 7.51 28 ± 9.12 30.1 ± 6.47 20.8 ± 8.82 < 0.001 1

Oral corticosteroids (Yes) 21.30% 25.53% 36.42% 20.51% 60.98% < 0.001 2

CCL-18 (ng/ml) 155 ± 73.9 262 ± 154 176 ± 91.3 169 ± 87.3 222 ± 95.5 < 0.001 1CCL-17 (pg/ml) 366 ± 200 469 ± 221 398 ± 208 356 ± 185 517 ± 257 0.006 1

1: ANOVA 2: Kruskall‐Wallis  3: Chi‐squared test

www.ubiopred.eu

Omics Handprint feature selection

2 ‐ 1 3 ‐ 1 3 ‐ 2 4 ‐ 1 4 ‐ 2 4 ‐ 3 5 ‐ 1 5 ‐ 2 5 ‐ 3 5 ‐ 4LTE4 9.45E‐9 0.042 0.022 0.761 0.0001 0.98 0.0001 0.658 0.999 0.3

www.ubiopred.eu

Topological data analysis - A Pioneering Approach

Topology is the study of shape Our Differentiation is TDA

Topology is a branch of mathematics from the 1700s that studies continuity and connectivity of objects and spaces, utilizing the shape of data to derive meaning in data

The combination of Topological Data Analysis (TDA) with machine-learning automatically creates topological networks revealing statistically significant patterns in complex data

www.ubiopred.eu

NeutrophilicGroup 1

NeutrophilicGroup 2

NeutrophilicGroup 3

EosinophilicGroups 1 & 2

EosinophilicGroups 3 Non‐neutrophilic

or eosinophilicsevere asthmatics

Mixed healthy &mild/moderate asthmaticgroups 1 & 2 

TDA analysis: clustering of sputum lipidomics data with colour overlay of U‐BIOPRED cohorts

www.ubiopred.eu

Biomarkers of Handprints

tryptasetryptase

EDN

MPOMPO

PeriostinPeriostin

PGD2 metabolites

PGD2 metabolites

Eos + periostin + 

FeNO

Protein X + lipid X

Protein X + lipid X

First attempts at greater mechanistic understandingIs the dogma that Is all eosinophilia is T2 driven correct?

BBS10ACN9SLC38A9GTF2H2BFLJ44896RPTNKLRC4SYBULOC101928DACH1LOC440346NXPH4LINC00867LINC01448ADAM5SLC7A14MGC15885CLCGPR42SOCS2GAPTFLVCR1F13A1CD1EPTPN7CCR3LGALS12KIF21BVSTM1TPSB2TPSAB1CRLF2P2RY10SMOXZNF395FAM159AC8orf 60UGT2B28SLC7A11-ASLINC01010MMP10CCL17KLF9ADORA3ZNF321PJARID2-AS1KCNH2FCER2LOC101927PDE2ALOC100287FAM193BSUN5FBXL18POU5F1P3SNX32GBP1P1CXCL11FAM160B1TRIM5CLOCKCCNYL1EMC2APOL3HERC6GIMAP1LINC01094MRPS33ZYG11BME1AIG1PYURFTRAPPC12TBC1D2BPDCD2NT5DC1LSM5COA7FUNDC1PEX3NAPEPLDLOC100128TLR7FBXO3SLC35F5C12orf 5FCF1FTCDNL1TMPOCOQ9WRBPARNCCP110BMFPOC1BTANC2CLCN4AKAP11GPR85HCCSRYR1OR2A7ARMCX5ARHGAP22UMPSMTORBPNT1TMEM170BZNF436ZNF570POLR3FMAP3K7CLTRG-AS1LCMT2PUS7MAGOHBPKIBCD1BCD3EC21orf 91-OCCDC146MARS2ZC3H6LOC100288PHACTR3FEVTMEM108-ALINC00589TAAR2LOC100507LOC148696AMELXSPAG5CD96URB2ZNF177EPN2-AS1ITM2AKITLOC100130IL5CNR2IL21EPHA10TRIM51LOC653581THRBHRCT1SFTPDIL2RASTARD4S1PR1HRH4OLIG2CST1CYSLTR2CPA3ALOX15LOC100127PTGER2GZMAKLRC4-KLRNKG7SULT1B1CD1APPP1R14ADNASE1L3LOC642236EXTL2GZMKGLCCI1CLEC4FNEK11CYP1B1-ASDCBLD1CDK1BDNFLRRC7DENND5BLINC01366LAG3FANCBHELLSRNF32OLFM1UGT1A6IL12A-AS1ATP11A-AS1TBL1YNEO1MYL5PCOLCE-ASFZD4APOBEC3DRPAP2HLA-ANEAT1CDK13HLA-ES100A8S100A9FCGR3BCALM2CD163CAP1CSTAVEGFATNFAIP3IL1R2THBS1MNDAIFI16FAM129ACARD16FLOT1MAFFRASEFC9orf 64CXCL10GCH1IFIT3IFIT2HERC5IFIT1OAS1IFI44OAS3SAMD9LPLD3ALDH1A1SNX18UTRNHN1TOR1AIP2PPP2R5CAMD1ACAA1ITGAEACAA2SRP19PSMA3MRPS21CCZ1BMRPS15DCNBLVRBCOPG1BANF1POLR2J4ANKRD10RNA45S5CSF1TGM2PNPLA6GPR183BIRC3SATB1AREGUBALD2ETS2SPRY4PHC2CMASG3BP1ZNF611UGCGPCBP2STX7BTBD1PYCARDRNH1SBDSLAP3PSMB9TRIM22NFAM1SAMD9BAZ1ACASP4NMICPEB4LY96RBM47FCGR1AFCGR1BPLBD1CECR1NDUFA1ATP6V1AMYD88RIPK2ANKRD10-ICCDC152ZC3H7BSLAXRCC5DSETAX1BP1GNAQPTBP3RHOHBTBD19MAP3K8NFKBIELOC100506MCM3APPLCD3FAM101BATP2A3MARCKSL1CCL22DPH1NFKBIDRUNX3SMG1P5MIR142LENG8IL1RL1PRSS33MMP12HMG20BCD1CTARPTRGV9TRGC2CD24IGLV@IGLC1RRN3P2SLC16A10CTNSKMT2APALLDDUSP4IL3RALOC101060KLK4DUSP16TCF7IL18R1CR1LGIMAP4SBF2KLHL15SYNE2INAFM1AP5B1REPS2SYMPKYES1CD6MAN2C1C11orf 49LOC158402RBFAAHCYSPINT1TRIM16R3HCC1GNL3LCUL2ANGEL1FAM118APTGS1WASH5PSTARD10GATA2SPDEFHIPK4GZMBGNLYGIMAP5PRF1GPR171RFTN1CCR7CPEB2SOCS1DIDO1RASAL3CD300LBLINC01016IGHMBP2POU5F1BST3GAL3PIP4K2BTATFZR1FOXO6FRMD4BPLEKHG2RLFCLEC4DTREML2TNFSF10IFIH1GBP4CREB5SPATA13TLR1WDFY3CPPED1UBE2D1HSD17B11FAM126BTLR6LILRA5C5orf 56TRANK1SIGLEC5TLR8PARP9TRAFD1MAPK14VAV1PPP3R1LOC728613HELZCD3DMORC3LY75MRPS10PDK3CAMPPEX10TMEM18SNAPINCTNND1TM7SF3SDCCAG8TULP4ARHGEF3PQLC3ABHD3GTF2A2PPP1R7C2CD5AP3M1GINM1MIOSRNF135COA6METTL5MYO6PRKACBFUCA1LMAN1NAA20TMEM14BZCRB1RAB7BTTC7AKIAA0100LRRC8DMAGED2GORASP2NUCB2PSMB8UBA3SDHBFSTL1MARCH2CCDC58RNF146TRAP1TP53MRPL14CD302TGFBR2NLRC4RBP7DHRS7DDX1PPP1CCMRPL33MEIS3P1RAB22APDCD10

2 6 10

Value

010

0

Color Keyand Histogram

Cou

nt

DDC1

DDC2

DDC3

Eosinophilic

non‐Eosinophilic

Clusters from disease drivers

Phenotype from cellproportion

478 DEGs

n=31 n=31 n=56

Machine learningDDC1 – 28 genesDDC2 – 27 genesDDC3 – 31 genes

www.ubiopred.eu

Distinct macrophage subsets associated with inflammasome activationTNF, PGE2 & TLR2 activated

healthy

LPS activated

healthy

M1 macrophages

healthy

0

10

20

30

40

50

E M N Pconvention.70

Freq

Var2eos.h

mix1

mix2DDC3

DDC1

Disease Driver Cluster

Conventional sputum cell phenotypesNum

ber o

f patient

DDC2

Mixed granulocytic: Neu > 74%; Eos>1.5%

Neutrophil predominant: Neu > 74% ; Eos<1.5%

Eosinophil predominant : Neu < 74% ; Eos>1.5%

Paucigranulocytic: Neu < 74%; Eos <1.5%

Eosinophil predominant asthma:

&                    subtype DDC3DDC1

Mixed granulocytic asthma:

&                    subtype DDC2DDC1

E M N P

Can we use this human mechanistic data instead of relying on animal data? 

Screen or compare with other databasesUse signatures (from TACs for example) to examine disease models

± treatments, responder/non‐responders e.g. ICS/OCS

www.ubiopred.eu

BIOPSIES**$$$

***$$$

Mouse lung chronic HDM signature is enriched in severe asthma and non‐severe asthma: all compartments

BLOOD**$$

***$$$ SPUTUM

***

BRONCHIAL BRUSHING

***$

**$$ ***

$$$

** Vs. a p<0.01*** Vs. a p<0.001$ Vs. b p<0.05$$ Vs. b p<0.01$$$ Vs. b p<0.001

www.ubiopred.eu

Conclusions• Chronic HDM model

• Signature enrichment is enhanced in the severe asthmatics (A) and asthmatic smokers (B) compared to the moderate asthmatics and the healthy non‐smokers.

• Surprising result as the model phenotypically represents moderate to mild asthma

• Enrichment is lost in mice treated with corticosteroids ‐ steroid‐sensitive genes in mouse remain in asthma

• Further analysis wrt steroid response genes required

• CFA/HDM model time course• Have studied common genes at 2, 3 and 4 time points• Differences seen at 2 and 3 time points in common

• Mostly between severe asthma (A) compared with healthy controls (D)

• ?Do steroid‐insensitive signatures map with sputum cell subtypes? ‐ TDA

www.ubiopred.eu

Cluster 1

Neutrophil predominant enriched

Colored by neutrophil predominant subjects

Steroid‐insensitive mouse signatures are associated with subsets of severe asthmatics

Cluster 2

Eos and Mixed granulocytic enriched

Colored by mixed granulocytic subjects

Clustering on 12 mouse signatures from steroid‐insensitive models in human sputum drives 3 clusters

www.ubiopred.eu

Steroid‐insensitive mouse signatures are associated with subsets of severe asthmatics

Cluster 3

Pacigranulocyticenriched

Colored by Paucigranulocyticsubjects

Need to (i) examine additional models of severe asthma and (ii) map human signatures to mouse models

What are the current limitations to human‐specific models and advanced 

techniques in this field

Adequate mapping to human disease subsets – ‘omics may provide better discrimination

Greater understanding of disease pathophysiology – which are key cells?3D complex disease models – do we have the right ones?

Money/Incentives/ disease tissue

PCLS preparation

Confocal microscopy of dsRNA staining post HRV1b infection of human PCLS

Replicating HRV (red) localised in airway epithelial cells (yellow)Not possible to infect biopsies with RV

29

Ingenuity IPA pathway analysis for the 116 probesets that Up‐regulated by HRV –High in 24hrs:

Day 1 Day 3 Day 12 Day 18Mucous Inner 

lumen

Basal Ring

Merging Spheroids

Start of Mucous  secretionSingle basal 

cells

Vacuole 

Start of Vacuole Formation

Basal Cells Spheroids Intermediate Spheroid Bronchosphere

NHBE Bronchosphere Development

63µM

Basal Ring

Inner lumen

Vacuole

85µM

Basal Ring

Start of Vacuole Formation

35µMUndifferentiated Spheroid

NHBEC AHBEC CHBEC

Lumen

x40

Bronchotubules/organoid formation

The future: what is needed.• Better models

• Hard to discern which is best without comparison of those currently available • Organoids• Lung‐on‐a‐chip• 3D printing

• Closer integration of bioinformaticians/computer scientists with clinicians/wet lab scientists

• Mathematical modelling• Post‐NC3Rs‐directed research strategies

• Not just ‘big data’ analysis• Money/Incentive/access to disease tissue

University of Amsterdam, University of Southampton, Imperial College London, University of Manchester, University of Nottingham, Fraunhofer Institute Hannover, CNRS-EISBM Lyon, Université de Méditerranée Montpellier, Karolinska Institutet Stockholm, University Hospital Umea, University Tor Vergata Rome, Università Cattolica del Sacro Cuore Rome, University of Catania, Hvidore Hospital Copenhagen, University Hospital Copenhagen, Haukeland University Bergen, Semmelweis University Budapest, Jagiellonian University Krakow, University Hospital Bern, University of Ghent

EFPIA PartnersNovartisAlmirallAmgenAstraZenecaBoehringer IngelheimChiesiGlaxoSmithKlineJohnson & Johnson / JanssenMerckUCBRoche /Genentech

SME’sAerocrineBioSci ConsultingSynairgenPhilips Research

Patient organisationsAsthma UKEuropean Lung FoundationEFAInt Primary Care Respiratory GroupLega Italiano Anti FumoNetherlands Asthma Foundation

www.ubiopred.eu

Clinically relevant allergen – multiple challenges neededEosinophilia and Th2 cell response into airwaysSteroid sensitiveReproducible in academia and industry – but not predictive

Chronic HDM model protocol

3 week HDM  exposureDay 34

Endpoints• BAL, lung and serum cytokines• BAL cells (neutrophils and eosinophils)• Lung transcriptomics

Re‐challenge with HDM (50µg, i.n.)

Day 33Pre dosei.p. Isotypecontrol

Pre‐treat with Dexamethasone 1mg/kg 

www.ubiopred.eu

Genes that are down‐regulated by steroids in the chronic HDMmodel are still enriched in severe asthma

BiopsiesSputum

Steroid‐responsiveness of the model makes translation difficult

www.ubiopred.eu

CFA/HDMmodel protocol

Day ‐14 Day 0

CFA/HDM Saline or HDM

Day 1 Day 2 Day 3 Day 4 Day 7 Day 14

Endpoints• Force Manoeuvres• Resistance & Compliance• BAL‐ cell numbers• BAL‐ cytokines• Tissue‐ RNA ‐> sent for 

transcriptomics • Tissue‐ Protein ‐> in ‐80C storage

Severe asthma in vivomodel (Dixon et al. 2013, De Alba et al., 2015)House dust mite (HDM) in complete freunds adjuvant (CFA)Eosinophilia, neutrophilia and a mixed T cell response (Th1, Th2 and Th17 cells)into airways

Steroid insensitiveReproducible in academia and industry

Pre‐treat with Dexamethasone 1mg/kg 

www.ubiopred.eu

Genes upregulated at 3 time‐points in CFA/HDMmodel:Signature enriched in severe asthma blood and sputum

*** Vs. d p<0.001

***

Genes up‐regulated at 3 time points: does not change with steroid treatment

SPUTUMBLOOD