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Phenotyping severe asthma
The U-BIOPRED project
p.j.sterk@amc.nl
Department of Respiratory Medicine
Academic Medical Center
University of Amsterdam
The Netherlands
Towards phenotyping severe asthma
1. What is a phenotype?
2. What is the concept behind unbiased
disease-fingerprinting?
3. Is this needed for the management of severe
asthma?
4. The IMI-EU project U-BIOPRED
5. The promiss of ‘systems medicine’
What is a phenotype?
The composite of observable characteristics
of an organism…
resulting from interaction between its
genetic make-up and environmental
influences…
that is relatively stable, but not invariable
with time.
The three determinants of a phenotype
Genetics
Time
Environment
Complex biological systems:
The secret of life
•
Spontaneous self-organisation
•
Open systems, importing energy, exporting waste
•
Not linear: output not simply proportional to input
•
Sudden emergent phenomena, deterministic chaos
•
Adaptation by negative feedback loops
•
Fluctuating: homeokinesis rather than homeostasis
Schrödinger E. Lectures Trinity College Dublin; 1944. New York: MacMillan
Goldberger AL. Proc Am Thoracic Soc 2006;3:467-472.
Macklem PT. J Appl Physiol 2008;104:1844-1846.
Macklem PT & Seely A. Perspectives Biol Med 2010;53:330-343.
Genes, environment and time
Richards K. Life.
Little, Brown & Company, London, 2010
Genes
Cell
differentiation
&
activation
Gene-& posttranscriptional
regulation
Organ
structure
& function
Cell-cell
interaction
Organism
health
& disease
Macro
physiology
Capturing phenotypes
disease
domain
diagnosis
& therapy
Symptoms
√
Functional
√
Cellular
?
Molecular
?
Severe asthma
Facts
– Despite all our attempts, the clinical course of severe
asthma is far from optimal
– Unfortunately, the development of new drugs for severe
asthma has not been successful during the past years
Reasons?
– Severe asthma is not a single disease: individual
patients are clinically very different
– There are multiple and co-existent disease mechanisms
– At present the efficacy of new drugs cannot be predicted
well enough from preclinical models nor from currently
defined patient characteristics
Asthma severity and control
No
treatment
controlled
mildest
Intensive
treatment
mild
mild
severe
severe
uncontrolled
most
severe
Cockcroft & Swystun JACI 1996;98:1016-1018
ATS/ESR Task Force Asthma Control and Exacerbations
Taylor et al. ERJ 2008;32:545-554
Reddel et al. AJRCCM 2009;180;58-99
“Severe asthma”
Problematic asthma
Fixed
obstruction
Uncontrolled asthma
Non-adherent asthma
Exacerbation
prone
Co-morbid asthma
Refractory asthma
Truly
severe asthma
Difficult asthma
no asthma
NAEPP 1997, ERS 1999, GINA 2002, ATS & SARP 2002, ENFUMOSA 2003, BIOAIR 2005
TENOR 2004, Paris 2007, ERS 2008, PSACI 2008, WHO 2009, U-BIOPRED 2011
Consensus
Definition and classification
1.
Problematic asthma
•
2.
All asthma that remains uncontrolled despite prescription of high
intensity treatment
Difficult asthma
•
•
•
•
3.
Mild-moderate asthma that remains uncontrolled
Adherence <50%, VCD, dysfunctional breathing, psychosocial
Persistent exposures
Untreated co-morbidity
Severe asthma
•
•
•
•
Poor control or >2 exacerbations/year, despite high intensity treatment
> 1000 (adults) or 500 (children) μg FP equivivalent or daily oral
steroids, combined with LABA or other add-on medication
Mainted control only achievable by high intensity treatment
Thereby serious risk of adverse effects
Bel et al. U-BIOPRED Study. Thorax 2011 EPub
SARP
Severe Asthma
Cluster Analysis
Early onset
Atopic
Normal pb FEV1
< 2 Controllers
1
Early onset
Atopic
Normal pb FEV1
More medications
2
Late onset
Older
Obese
Female
Lower pb FEV1
Exacerbations
3
Long duration
Very low FEV1
Reversibility
Long duration
More women
Less atopic
Very low FEV1
Not reversible
4
Moore et al. Am J Respir Crit Care Med 2010;181:315-323
5
Asthma: complex biology
Normal
asthma
Central
Peripheral
Mauad, Bel, Sterk. J Allergy Clin Immunol 2007;120:997-1009
Additional phenotypic markers?
When will a disease marker be useful?
● ●●
●
●
●
●
●
●
Reference feature
Marker B
Marker A
●
●
●
●
●
●
●
● ●
●
●
●
Reference feature
Disease markers which provide
complementary information in asthma
Factor analysis
Age
FEV1
FVC
PC20
Reversibility
Sputum
- eosinophils
- ECP
Rosi et al. JACI 1999;103:232
Phenotypic cluster analysis in asthma
Symptoms
Discordant
Symptoms
Obese
Late onset
Controlled
Mixed onset
Discordant
Inflammation
Eosinophilic inflammation
Haldar et al. Am J Respir Crit Care Med 2008;178:218-224
Exhaled nitric oxide + FEV1
predict lung function decline in severe asthmatics
during 5 years prospective follow-up
Baseline FEV1 ≤ 80%
200
100
Change in FEV1 (ml)
Change in FEV1 (ml)
Baseline FEV1 > 80%
50
0
-50
-100
-150
150
100
50
0
-50
-100
-200
1
2
4
8
16
32
64
128
1
Exhaled NO (ppb)
Van Veen et al, ERJ 2008;32:344-349
2
4
8
16
32
64
Exhaled NO (ppb)
128
Heatmap for molecular phenotyping
from cytokines in BAL of severe asthma
Brasier et al. J Allergy Clin Immunol 2008;121:30-37
Transcriptomic phenotypes from sputum in asthma
Baines et al. J Allergy Clin Immunol 2011;127:153-160
Protein expression profiling in serum
in asthma, COPD, cystic fibrosis and controls
(SELDI-TOF-MS signatures)
Asthma vs
COPD
Asthma vs
CF
COPD vs
CF
AUC of
ROC
100%
100%
100%
Gomes-Alves et al. Clin Biochemistry 2010;43:168-177
Electronic nose analysis
Fens et al. Am J Respir Crit Care Med 2009:180:1076-82
Training and validation sets by eNose:
asthma versus COPD
Accuracy: 85%
AUC: 0.93
●Training set COPD
●Training set asthma
▄Validation set COPD ▄Validation set fixed asthma
Fens et al. ATS 2010, submitted
disease
domain
diagnosis
& therapy
Symptoms
√
Functional
√
Cellular
!
Molecular
!
disease
phenotype
Continuous recording and fluctuations
of respiratory resistance in asthma
Rrs at 10 Hz
(cmH2O/l/s)
mild Asthma subject 2
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0
20
40
60
80
100
120
time
Slats et al. Am J Respir Crit Care Med 2007;176:121-128
Respiratory impedance
in asthma and COPD
Asthma
COPD
Muskulus et al. J Appl Physiol 2010;109:1582-1591
Multi-dimensional, non-parametric fluctuation analysis
of the dynamics of respiratory impedance
in asthma and COPD
Muskulus et al. J Appl Physiol 2010;109:1582-1591
Discriminant score
ROC curve using 5-dimensional reconstruction
of respiratory impedance dynamics
in discriminating asthma and COPD
Asthma
Muskulus et al. J Appl Physiol 2010;109:1582-1591
COPD
disease
domain
diagnosis
& therapy
Symptoms
√
Functional
√
Cellular
!
Molecular
!
disease
phenotype
University of Amsterdam
University of Southampton
Imperial College London
University of Manchester
University of Nottingham
Fraunhofer institute Hannover
Centr Nat Recherche Sc Villejuif Paris
Université de Méditerranee Montpellier
Karolinska Institute Stockholm
University Umea
UniversityTor Vergata Rome
Università Cattolica del Sacro Cuore Rome
University of Catania
Hvidore Hospital Copenhagen
University Hospital Copenhagen
Haukeland University Bergen
Semmelweis University Budapest
Jagiellonan University Krakow
University Hospital Bern
University of Ghent
Novartis
GlaxoSmithKline
AstraZeneca
Chiesi
Pfizer
Roche
UCB
Boehringer Ingelheim
Johnson & Johnson
Almirall
Netherlands Asthma Foundation
Asthma UK
European Lung Foundation
EFA
Int Primary Care Respir Group
Lega Italiano Anti Fumo
Biosci
Aerocrine
Synairgen
Philips Research
Hypothesis U-BIOPRED study
Biomarker profiles from multi-scale
molecular, physiological, and clinical data
integrated by an innovative systems
biology approach into distinct handprints
will enable the prediction of clinical
course and therapeutic efficacy
and identification of novel targets in the
treatment of severe asthma
www.ubiopred.eu
1025 subjects
including adults ánd children
Adults
Children
Severe
asthma
525
100
Mild asthma
100
50
Healthy
controls
100
Infants
severe
recurrent
wheeze
100
Infants mild
recurrent
wheeze
50
Study design
1. Severe asthma consensus and diagnostic algorithm
(Bel et al. Thorax 2011 EPub)
2. Cross-sectional comparitive handprint discovery
3. Longitudinal follow-up during 30 months
4. Iterative comparison handprints from preclinical
models (human ex-vivo, animal in vivo)
5. Proof of concept intervention by randomized
controlled trial
www.ubiopred.eu
Study design
exacerbations
tele-monitoring
bronchoscopy
screening
baseline
follow-up 1
-1
0
3-6
Months
Follow-up 2
24-30
U-BIOPRED
Workpackages
1
Coördination and management
Sterk, Higenbottam,
Wagers, Sondervan
2
Consensus generation
Bel, Compton
3
Cross-sectional and longitudinal cohorts
Chung, Gerhardsson
4
Bronchoscopic assessment
Chanez, Sousa
5
Pre-clinical human models
Krug, Lewis
6
Pre-clinical laboratory models
Adcock, Knowles
7
Omics technologies
Djukanovic, Corfield
8
Bioinformatics and systems biology
Auffray, Manta
9
Dissemination
Wagers, Compton
10 Ethics
De Boer, Higenbottam
‘Systems Medicine’
Patient reported
Clinical
Functional
Cellular
Molecular
Auffray, Adcock, Chung, Djukanovic, Pison, Sterk.
Chest 2010;137:1410-1416.
www.ubiopred.eu
Ensure quality
Genomics
Transcriptomics
Proteomics
Metabolomics
Cytology
Histology
biobanking
Unresolved
disease
problem
Quantitative
morphology
(imaging)
Integrate data
Formalize questions
Organ function
and
dynamics
Clinical
expression and
patient
perception
knowledge
repository
Perturb the system
Refine unbiased computational model by iteration
Generate
hypotheses
Add open source
public data
Kaminsky, Irvin, Sterk. J Appl Physiol 2011: EPub.
Kaminsky, Irvin, Sterk. J Appl Physiol 2011, EPub.
Conclusions
• Phenotypes are integral descriptions of biological systems
from the molecular to organism level
• They are not stable, being modulated by genes, time and
environment
• In asthma and COPD there is increasing evidence that
multi-dimensional biomarker signals are complementary to
clinical characteristics
• Unbiased cluster- and time-series analysis by using a
systems medicine approach can make a step-change from
traditional diagnoses to “phenotype-handprints”
• U-BIOPRED is validating this strategy for severe asthma
p.j.sterk@amc.nl