Embed Size (px)
Citation preview
Impact of biomarkers on asthma phenotypes
Professor Kian Fan Chung MD DScExperimental Studies, National Heart & Lung Institute,
Imperial College London; Biomedical Research Unit, Royal Brompton Hospital,
London, UK
17.06.16Lausanne
Declaration of interest
• Participation in Advisory Board meetings regarding treatments of asthma and COPD for GSK, AstraZeneca, Novartis and Johnson & Johnson
• Research grant funding from Pfizer, GSK and Merck
• Speaking engagements: AstraZeneca, Merck, Novartis
• Investigator of IMI EU/EFPIA funded UBIOPRED Consortium on Severe Asthma
How many phenotypes of asthma that clinicians can recognise?
• Late onset non-atopic asthma
• Early onset childhood atopic asthma
• Aspirin-induced asthma: Samter’s triad
• Corticosteroid-sensitive asthma associated with eosinophilia
• Asthma associated with chronic airflow obstruction
• Asthma with frequent exacerbations
© Global Initiative for Asthma
GINA 2015
Stepwise management - pharmacotherapy
*For children 6-11 years,
theophylline is not
recommended, and preferred
Step 3 is medium dose ICS
**For patients prescribed
BDP/formoterol or BUD/
formoterol maintenance and
reliever therapy
# Tiotropium by soft-mist
inhaler is indicated as add-on
treatment for adults
(≥18 yrs) with a history of
exacerbations
GINA 2015, Box 3-5 (2/8) (upper part)
Diagnosis
Symptom control & risk factors(including lung function)
Inhaler technique & adherence
Patient preference
Asthma medications
Non-pharmacological strategies
Treat modifiable risk factors
Symptoms
Exacerbations
Side-effects
Patient satisfaction
Lung function
Other
controller
options
RELIEVER
STEP 1 STEP 2STEP 3
STEP 4
STEP 5
Low dose ICS
Consider low
dose ICS
Leukotriene receptor antagonists (LTRA)
Low dose theophylline*
Med/high dose ICS
Low dose ICS+LTRA
(or + theoph*)
As-needed short-acting beta2-agonist (SABA) As-needed SABA or low dose ICS/formoterol**
Low dose
ICS/LABA*
Med/high
ICS/LABA
Refer for
add-on
treatment
e.g.
anti-IgE
PREFERRED CONTROLLER
CHOICE
Add tiotropium#High dose ICS + LTRA (or + theoph*)
Add tiotropium#Add low dose OCS
Severe asthma
Why do we need a systems medicine approach in severe asthma?
• A complex common disease with difficult definition
• Gene-environment interactions
• Variable disease with chronic changes
• Heterogeneous presentation
• Variable response to drugs
• Severe asthma, less responsive to current medications, need for new medications
Characteristics of severe asthma clinical traits
Early onset/childhood asthma vs late onset
Chronic airflow obstruction vs normal lung function
(Increased decline in FEV1)
Recurrent exacerbations vs occasional exacerbations
Atopic/high IgE vs non-atopic
Eosinophilic vs non-eosinophilic
Obese vs non-obese
Steroid-insensitive vs steroid-sensitive
b-adrenergic bronchodilation vs no bronchodilation
Th2-high vs Th2-low in mild-moderate asthma
A: Mild-moderateasthmaB: Healthy
Features of Th2-high asthma● More blood and BAL eosinophils● ↑ serum IgE● ↑ mucin MUC5AC● ↑ IL5 and IL13 in biopsies● ↑ bronchial hyperresponsiveness● FEV1 increase with ICS
Transcriptomic analysis of epithelial brushings: expression of Th2 cytokines
Woodruff et al AJRCCM 2009; 180:388
Molecular phenotyping
(50%)
How common is a Th2 (IL-13) high in severe asthma?
Cohort Non Smoking Severe Asthma
Smoking Severe Asthma
Mild/Moderate Asthma
IL13 Th2 high % 37 % (18/49) 17 % (3/18) 25 % (9/36)
Non-smokingSevereasthma
SmokingSevereasthma
Mild-Moderateasthma
Healthy
Enrichment score for IL-13 IVS signature
Gene Set Variation Analysis
U-BIOPRED Bronchial Brushings
Define “Th2(IL-13) High” as
>95th %ile of Healthy controls
Stelios Pavlidis, Matthew Loza, Fred Baribaud for UBIOPRED
Transcriptome analysis of bronchial brushings for Th2 signature from epithelial cells activated by IL-13 in vitro (IL13 IVS definition)
Potential Severe Asthma Phenotypes
Th2-high inflammation Th2-low inflammation
“Severe Asthma”
FEV1
Symptoms
Exacerbations
Early onset
allergicLate onset
eosinophilic
Obese
Oxidative stress
Neutrophilic
Bacterial infection
Adapted from Wenzel 2013
???
10
Remodeling/repair Eosinophilic inflammation Neutrophilic inflammation
Poor asthma control High treatment requirements
Chronic airflow obstruction Recurrent exacerbations
Poor response to corticosteroids
10
Eosinophil
Th2
Mast cellB-cell
IL-5
IgE
IL-3IL-4
IL-13
Histamine
Leukotrienes
Epithélium
Allergens, Virus, Bacteria
Pollution & oxidants
Non-T2
Th0
MHC II
Peptide
TCR
B7.2
CD28IL-12
Dendritic cells
Growth factors
eg TGFß
Fibroblast
Airway smooth muscle
GM-CSF
Eotaxin
RANTES
Neutrophil
TSLP, IL-33, IL-25
Th1 Th17
ILC-2
TNFa
IL-1ß
IFNγ,TNFα
IL-17A,E,F
IL6TGFβ
IL-8
Mechanisms of severe asthma
Non-T2
New asthma treatments: targettingINTERLEUKINS/CYTOKINES
T2 (ILC2)
IL-17
TSLP
TNFαIL-5
IL-5Rα
IL-13 IL-4Rα
MepolizumabReslizumab
Benralizumab
GSK679586LebrikizumabTralokinumab
AMG317Dupilumab CXCR2
Antagonist(SCH527123)
IL-4IL-13
Non-T2
CXCR8
Brodalumab
EtanerceptGolimumabOmalizumab
QGE031Quilizumab
IgE
AMG157
Mepolizumab, an anti-IL5 antibody, in patients with severe eosinophilic asthma
P<0.001P<0.05
Ortega et al NEJM 2014; 371: 1198
≥ 2 exacerbations ≥ 1,000 µg FP/day Blood eos > 150/µl
Anti-IL5 antibody
Exacerbations FEV1
6/24/2016
Currently-available biomarkers to select
patients for each specific anti-Th2 target?
1.Blood eosinophil count: for anti-IL5, anti-IgE,
Anti-IL4Rα (blocks IL4/IL-13)
2. Serum periostin: for anti-IL13
3. FeNO: for anti-IgE/anti-IL4Rα
4. Other biomarkers?
Biomarkers of response to therapy
www.ubiopred.eu
Severe asthma:
non-smoking
(308)
Severe asthma:
smoking & ex-
smoking (110)
Moderate
Asthma (98)
Non-asthma
(101)P-value
Age (yr) 50.9 54.5 42.4 38.92.9E-
17
Female (%) 65.91 50.91 50.00 38.61 5.16E-06
BMI (kg/m2) 29.08 29.56 25.88 25.31 2.02E-10
Exacerbations in past yr 2.48 2.55 0.37 0 2.51E-26
IgE (IU/ml) 119.5 126 89.4 23.45 5.40E-15
Atopy (%) 69 58 80 38 6.1E-066
Nasal polyps (%) 34.7 33.7 8.3 8.8 1.33E-06
FEV1 (% pred) 67.42 67.25 88.37 101.76 1.81E-44
Oral corticosteroids (%) 50.68 46.08 1.06 0 9.73E-17
Sputum eosinophils (%) 2.75 4.13 1.05 0.00 2.69E-12
Exhaled NO 27 23.5 25.50 19.00 3.00E-04
Total: 617 participants
Demographics of UBIOPRED cohort
Shaw et al: Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort.
European Respiratory Journal Sep 2015.
111 (26%) 154 (37%) 66(16%)
90 (21%)
IgE 30-1300 IU/mlAtopic
EOS ≥150 /ul
Anti-IgE
Anti-IL5
44 (10%) 115 (27%) 150 (36%)
112 (27%)
Periostin≥50 ng/ml
EOS ≥150 /ul
Anti-IL5
Anti-IL-13
Total 418 Total 418
Anti-IL5 vs Anti-IgE Anti-IL-13 vs Anti IL-5
Data from UBIOPRED
Assuming anti-IL5, anti-IgE and anti-IL13 available: application to UBIOPRED cohort
Potential anti-Th2 treatment approaches in severe asthma (1)
Stelios Pavlidis
(11%)
(13%)
(10%)
(13%)
(7%)
(8%)
(5%)
101 patients (32%) were low for all 3 biomarkers
Blood EOS≥300
Distribution of high FeNO, high serum periostin and high blood eosinophil count in 418 severe asthma (UBIOPRED)
Serum Periostin≥55
FeNO≥30 ppb
101 patients (32%) were low for all 3 biomarkers
Blood EOS≥300
High FeNO, high serum periostin and high blood eosinophil count: linked to exacerbations and Th2 signature
Serum Periostin≥50 µg/l
FeNO≥30 ppb
Th2 ES 0.10Exacerbations 2.17
Th2 ES 0.36Exacerbations 2.83
Th2 ES 0.08Exacerbations 2.2
Th2 ES 0.05Exacerbations 2
Th2 ES 0.29Exacerbations 2.2
Th2 ES 0.00Exacerbations 3.25
Th2 ES 0.13Exacerbations 1
Th2 ES -0.11Exacerbations 3.07
Th2 expression score (ES):Using gene set variation analysiswith IL-13/epithelial cell gene expression in epithelial brushings
www.ubiopred.eu
Wheelock et el. ERJ 2013;42:802
2013
2015
UBIOPRED PROCESS OF SYSTEMS MEDICINE
Tissue samples
CONFIDENTIAL
Analysis of sputum inflammatory
cells transcriptomics and
proteome: ‘sputum fingerprint’
Christos Rossios, Stelios Pavlidis, Chihhsi Kuo, Uruj Hoda
AJRCCM
Distribution of neutrophilic and eosinophilic inflammation in sputum
2%0% 5%
93%
NEU &EOSNEUONLYEOSONLY
Neu > 73.6%; Eos>1.49%
Neu > 73.6%; Eos<1.49%
Neu < 73.6%; Eos>1.49%
Neu < 73.6%; Eos<1.49%
12%
15%
50%
23%A - Severe Asthma
11%8%
53%
28%
B - Severe Smokers
2% 14%
40%
44%
C - Mild/Moderate Asthma3% 2%
2%
93%
D - Health Volunteers
Uruj Hoda
(PAUCI-GRANULOCYTIC)
Hierarchical Clustering
Number of differentially expressed genes
478 DEGs
Defining ‘disease drivers’ from sputum inflammatory cell pattern: eosinophilic vs non-
eosinophilic
EOS vs HC non-EOS vs HC
non-EOS vs EOS
• Eosinophilic phenotype:
Sputum EOS ≥ 1.5% (n=67)
• Non-Eosinophilic phenotype:
Sputum EOS < 1.5% (n=51)
• Healthy Control (n=21)
• Differentially expressed gene from 3 sets of comparison
201 genes 145 genes
197 genes
Kuo et al 2016
A_
49
3A
_2
27
A_
22
0A
_2
56
A_
41
2A
_0
65
A_
31
0A
_2
43
A_
00
3A
_5
88
A_
25
1A
_7
24
A_
24
0A
_0
88
A_
63
8A
_4
44
A_
44
9A
_1
93
A_
02
0A
_3
57
A_
28
5A
_3
16
A_
44
6A
_4
86
A_
45
1A
_2
13
A_
53
6A
_0
85
A_
09
1A
_5
49
A_
10
8A
_4
77
A_
55
0A
_0
00
A_
41
8A
_5
62
A_
61
9A
_6
32
A_
38
7A
_3
96
A_
57
4A
_5
28
A_
60
2A
_3
62
A_
56
3A
_4
57
A_
12
7A
_2
08
A_
72
2A
_1
03
A_
33
3A
_6
12
A_
51
3A
_2
47
A_
55
9A
_0
98
A_
59
0A
_6
94
A_
69
1A
_1
90
A_
24
4A
_0
19
A_
20
0A
_0
36
A_
71
0A
_5
39
A_
69
5A
_3
20
A_
66
4A
_3
38
A_
41
0A
_0
33
A_
60
8A
_4
30
A_
50
2A
_0
27
A_
11
9A
_5
29
A_
65
1A
_5
00
A_
15
1A
_6
16
A_
32
3A
_4
19
A_
27
5A
_2
21
A_
30
0A
_4
52
A_
00
9A
_3
97
A_
37
3A
_2
91
A_
28
9A
_3
03
A_
17
4A
_4
15
A_
10
4A
_7
16
A_
22
6A
_5
27
A_
49
4A
_0
35
A_
23
5A
_6
86
A_
67
8A
_4
58
A_
35
3A
_1
02
A_
05
3A
_3
63
A_
55
3A
_6
26
A_
14
6A
_4
74
A_
20
7A
_5
19
A_
72
0A
_1
33
BBS10ACN9SLC38A9GTF2H2BFLJ44896RPTNKLRC4SYBULOC101928020DACH1LOC440346NXPH4LINC00867LINC01448ADAM5SLC7A14MGC15885CLCGPR42SOCS2GAPTFLVCR1F13A1CD1EPTPN7CCR3LGALS12KIF21BVSTM1TPSB2TPSAB1CRLF2P2RY10SMOXZNF395FAM159AC8orf 60UGT2B28SLC7A11-AS1LINC01010MMP10CCL17KLF9ADORA3ZNF321PJARID2-AS1KCNH2FCER2LOC101927770PDE2ALOC100287590FAM193BSUN5FBXL18POU5F1P3SNX32GBP1P1CXCL11FAM160B1TRIM5CLOCKCCNYL1EMC2APOL3HERC6GIMAP1LINC01094MRPS33ZYG11BME1AIG1PYURFTRAPPC12TBC1D2BPDCD2NT5DC1LSM5COA7FUNDC1PEX3NAPEPLDLOC100128108TLR7FBXO3SLC35F5C12orf 5FCF1FTCDNL1TMPOCOQ9WRBPARNCCP110BMFPOC1BTANC2CLCN4AKAP11GPR85HCCSRYR1OR2A7ARMCX5ARHGAP22UMPSMTORBPNT1TMEM170BZNF436ZNF570POLR3FMAP3K7CLTRG-AS1LCMT2PUS7MAGOHBPKIBCD1BCD3EC21orf 91-OT1CCDC146MARS2ZC3H6LOC100288721PHACTR3FEVTMEM108-AS1LINC00589TAAR2LOC100507419LOC148696AMELXSPAG5CD96URB2ZNF177EPN2-AS1ITM2AKITLOC100130264IL5CNR2IL21EPHA10TRIM51LOC653581THRBHRCT1SFTPDIL2RASTARD4S1PR1HRH4OLIG2CST1CYSLTR2CPA3ALOX15LOC100127886PTGER2GZMAKLRC4-KLRK1NKG7SULT1B1CD1APPP1R14ADNASE1L3LOC642236EXTL2GZMKGLCCI1CLEC4FNEK11CYP1B1-AS1DCBLD1CDK1BDNFLRRC7DENND5BLINC01366LAG3FANCBHELLSRNF32OLFM1UGT1A6IL12A-AS1ATP11A-AS1TBL1YNEO1MYL5PCOLCE-AS1FZD4APOBEC3DRPAP2HLA-ANEAT1CDK13HLA-ES100A8S100A9FCGR3BCALM2CD163CAP1CSTAVEGFATNFAIP3IL1R2THBS1MNDAIFI16FAM129ACARD16FLOT1MAFFRASEFC9orf 64CXCL10GCH1IFIT3IFIT2HERC5IFIT1OAS1IFI44OAS3SAMD9LPLD3ALDH1A1SNX18UTRNHN1TOR1AIP2PPP2R5CAMD1ACAA1ITGAEACAA2SRP19PSMA3MRPS21CCZ1BMRPS15DCNBLVRBCOPG1BANF1POLR2J4ANKRD10RNA45S5CSF1TGM2PNPLA6GPR183BIRC3SATB1AREGUBALD2ETS2SPRY4PHC2CMASG3BP1ZNF611UGCGPCBP2STX7BTBD1PYCARDRNH1SBDSLAP3PSMB9TRIM22NFAM1SAMD9BAZ1ACASP4NMICPEB4LY96RBM47FCGR1AFCGR1BPLBD1CECR1NDUFA1ATP6V1AMYD88RIPK2ANKRD10-IT1CCDC152ZC3H7BSLAXRCC5DSETAX1BP1GNAQPTBP3RHOHBTBD19MAP3K8NFKBIELOC100506860MCM3APPLCD3FAM101BATP2A3MARCKSL1CCL22DPH1NFKBIDRUNX3SMG1P5MIR142LENG8IL1RL1PRSS33MMP12HMG20BCD1CTARPTRGV9TRGC2CD24IGLV@IGLC1RRN3P2SLC16A10CTNSKMT2APALLDDUSP4IL3RALOC101060424KLK4DUSP16TCF7IL18R1CR1LGIMAP4SBF2KLHL15SYNE2INAFM1AP5B1REPS2SYMPKYES1CD6MAN2C1C11orf 49LOC158402RBFAAHCYSPINT1TRIM16R3HCC1GNL3LCUL2ANGEL1FAM118APTGS1WASH5PSTARD10GATA2SPDEFHIPK4GZMBGNLYGIMAP5PRF1GPR171RFTN1CCR7CPEB2SOCS1DIDO1RASAL3CD300LBLINC01016IGHMBP2POU5F1BST3GAL3PIP4K2BTATFZR1FOXO6FRMD4BPLEKHG2RLFCLEC4DTREML2TNFSF10IFIH1GBP4CREB5SPATA13TLR1WDFY3CPPED1UBE2D1HSD17B11FAM126BTLR6LILRA5C5orf 56TRANK1SIGLEC5TLR8PARP9TRAFD1MAPK14VAV1PPP3R1LOC728613HELZCD3DMORC3LY75MRPS10PDK3CAMPPEX10TMEM18SNAPINCTNND1TM7SF3SDCCAG8TULP4ARHGEF3PQLC3ABHD3GTF2A2PPP1R7C2CD5AP3M1GINM1MIOSRNF135COA6METTL5MYO6PRKACBFUCA1LMAN1NAA20TMEM14BZCRB1RAB7BTTC7AKIAA0100LRRC8DMAGED2GORASP2NUCB2PSMB8UBA3SDHBFSTL1MARCH2CCDC58RNF146TRAP1TP53MRPL14CD302TGFBR2NLRC4RBP7DHRS7DDX1PPP1CCMRPL33MEIS3P1RAB22APDCD10
2 6 10
Value
01
00
Color Key
and Histogram
Co
un
t
TAC1
TAC2
TAC3
Eosinophilic
non-Eosinophilic
Clusters from disease drivers
Phenotype from cellproportion
478 DEGs
n=31 n=31 n=56
Hierarchical clustering of differentially expressed genes between eosinophilic vs non-eosinophilic asthma in sputum cells
TAC: Transcriptome-associated clusters
Kuo et al 2016
TAC 1 TAC 2 TAC 3Mechanisms ‘T-2 associated’
Epithelial driven
‘Inflammasome’
Macrophage driven
‘Mitochondrial’
Oxidative stress
Sputum inflammation
Eosinophilic/Mixed Neutrophilic/Mixed Eosinophilic/Paucigranulocytic
Microarray IL33R, TSLPR, CCR3, IL3RA
IFN & TNF superfamily, CASP4
Metabolic genes
GSVA Th2/ILC2 NLPR3/DAMP-associated Th17; OXPHOS; ageing
Protein (Somalogics)
IL-16, Periostin, Serpin peptidase inhibitor 1, ADIPOQ
TNFAIP6, MIF, Tyrosine kinase src
Cathepsin B, G
Clinical features Severe asthma; Highest nasal polyps and OCS use; Severe airflow obstruction
Moderate-to-severe asthmaMild airflow obstruction
Moderate-to-severe asthmaMild airflow obstruction
Transcriptome-associated clusters (TAC) of moderate-severe asthma from sputum analysis
Kuo et al 2016
Systems Medicine: finding biomarker(s)
Multi-omicsplatforms
HandprintsCandidate
Biomarkers
Point of CareBiomarkers
Novel/existingtargets
CHAMPSMEDALLSTELARSOMOSAEMBERUBIOPRED
CHAMPS EUROPAIHECMEDALL STELARUBIOPRED
HarmonisationData management
Multi-omics integration
From multi-omics analysis to point-of-care biomarker
TestingValidation
CHAMPSEUROPASOMOSAEMBER
Correlativeanalysis
SOMOSA
CohortsCohorts
Cohorts
Systems medicine for precision medicine of asthma
Treatmentcohort
University of Amsterdam, University of Southampton, Imperial College London,
University of Manchester, University of Nottingham, Fraunhofer Institute Hannover,
Centre Nat Recherche Sc Villejuif Paris, Université de Méditerranee Montpellier,
Karolinska Institute 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 Partners
Novartis
Almirall
Amgen
AstraZeneca
Boehringer Ingelheim
Chiesi
GlaxoSmithKline
Johnson & Johnson / Janssen
Merck
UCB
Roche /Genentech
SME’s
Aerocrine
BioSci Consulting
Synairgen
Philips Research
Patient organisations
Asthma UK
European Lung Foundation
EFA
Int Primary Care Respiratory Group
Lega Italiano Anti Fumo
Netherlands Asthma Foundation
website hosted by the ELF: www.ubiopred.eu
Funded by the European Union
Barcelona 2013
Scientists, biologists, physiologists, statisticians, bioinformaticians, computer scientists, clinicians, clinical
triallists, managers, patients
