The document discusses the U-BIOPRED study, which aims to better characterize asthma phenotypes through an integrated "omics" approach. It notes that asthma is a complex disease with various clinical phenotypes. The study hypothesizes that biomarker profiles incorporating multidimensional molecular, physiological and clinical data can define distinct phenotypes that enable prediction of disease course and treatment responses. It outlines the study's methods, including large adult and pediatric cohorts, and integration of various omics data like transcriptomics, proteomics and metabolomics with clinical information. The goal is to capture disease complexity and ultimately develop clinically applicable tools to guide precision medicine approaches for severe asthma.

1. U-BIOPRED
Unbiased BIOmarkers for the PREDiction
of respiratory disease outcome
p.j.sterk@amc.nl
Department of Respiratory Medicine
Academic Medical Center
University of Amsterdam
The Netherlands

2. Omics in the phenotyping
of (severe) asthma
1. Asthma as a complex disease
2. Omics as integrative assessment of
(patho)biology
3. Fingerprints and handprints of asthma
4. Towards point-of-care application

3. ERWIN SCHRÖDINGER
First published 1944
What is life?
The Physical Aspect of the Living Cell.
http://whatislife.stanford.edu/LoCo_files/What-is-Life.pdf

4. Life: genes, environment and time
Richards K. Life.
Little, Brown & Company, London, 2010

5. Clinical phenotypes of severe asthma
Difficult asthma
Exacerbation
prone
Truly
severe asthma
Fixed
obstruction
Problematic asthma
Uncontrolled asthma
Refractory asthma
Co-morbid 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
Non-adherent asthma

6. Asthma: complex pathobiology
Central
Peripheral
Normal asthma
Mauad, Bel, Sterk. J Allergy Clin Immunol 2007;120:997-1009

7. Dendogram of clinical phenotypes
Ward clustering and Similarity Profile Analysis

8. Moore et al. J Allergy Clin Immunol 2014;133:1557-1563
Clinical-biological phenotypes of asthma
mild-mod
early onset
young
allergic
normal LF
mild-mod
early onset
older
allergic
reversible LF
higher BMI
later onset
older
high ICS
reverisible LF
severe
obese
oldest
ICS, OCS, etc
impaired LF

9. Hypothesis U-BIOPRED study
n=1028
Biomarker profiles from high-
dimensional molecular, physiological, and
clinical data
integrated by an innovative systems
medicine approach into distinct handprints
will enable the prediction of clinical
course and therapeutic efficacy
and identification of novel targets in the
treatment of severe asthmawww.ubiopred.eu

10. Adult U-BIOPRED cohort
n=729
0
50
100
150
200
250
300
350
400
Severe
asthma, non-
smoking
Severe
asthma,
smoking
Mild to
moderate
asthma
Healthy
controls
Chung et al. and U-BIOPRED Study, ATS 2014:A2424

11. Paediatric U-BIOPRED cohort
n=299
0
20
40
60
80
100
120
Severe
asthma,
school age
Mild to
moderate
asthma,
school age
Severe
wheeze,
preschool
Mild to
moderate
wheeze,
prechool
Chung et al. and U-BIOPRED Study, ATS 2014:A2424

12. Why capturing complexity (phenotypes)
in medicine?
1. Understanding pathogenesis (endotype?)
2. Improving clinical outcome
 prediction of clinical course
 prediction of therapeutic responses
 guiding clinical management

13. disease
domain
Symptoms
Functional
Cellular
Molecular
diagnosis
& therapy
√
√
?
?
Imaging ?

14. Single biomarker Biomarker
Panel
Composite
signature
Clinical Wheeze Asthma Control
Questionnaire
Exacerbation
Functional FEV1 Small airways
obstruction
Exercise
limitation
Histological Reticular layer
thickness
Extracelluar matrix
composition
Inflammation
Remodelling
Cellular Eosinophil counts Th2 high profile Cell differentials
Molecular SNP
FeNO
Periostin
Oxidative stress Transcriptomics
Proteomics
Metabolomics
Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205

15. Single biomarker Biomarker
Panel
Composite
signature
Clinical Wheeze Asthma Control
Questionnaire
Exacerbation
Functional FEV1 Small airways
obstruction
Exercise
limitation
Histological Reticular layer
thickness
Extracelluar matrix
composition
Inflammation
Remodelling
Cellular Eosinophil counts Cell differentials Allergic
inflammation
Molecular SNP
FeNO
Periostin
Oxidative stress
Th2-high profile
Transcriptomics
Proteomics
Metabolomics
Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205

16. Single biomarker Biomarker
Panel
Composite
signature
Clinical Wheeze Asthma Control
Questionnaire
Exacerbation
Functional FEV1 Small airways
obstruction
Exercise
limitation
Histological Reticular layer
thickness
Extracelluar matrix
composition
Inflammation
Remodelling
Cellular Eosinophil counts Cell differentials Allergic
inflammation
Molecular SNP
FeNO
Periostin
Oxidative stress
Th2-high profile
Transcriptomics
Proteomics
Metabolomics
Pathophysiological Probabilistic
understanding pattern recognition
Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205

17. Wheelock et al. and U-BIOPRED Study. Eur Respir J 2013;42:802-825
Genes Gene
expression
Airway
histology
Lung
function
The
patient
GeneticsEpigenetics
Transcriptom
e
Proteom
eMetabolom
e
Im
m
unityInflam
m
ation
Rem
odelingBHR
ObstructionSym
ptom
sCo-m
orbidity
Quality
oflife
respiratory disease phenotyping
M
icrobiom
e

18. Transcriptomics in endobronchial biopsies
from patients with asthma
Yick et al. AJRCCM 2013;187:1076-84
Whole biopsy
Laser dissected
smooth muscle
Yick et al. ERJ 2013;42:662-670

19. Transcriptomics by next generation sequencing
(454 RNA-seq) of bronchial biopsies:
asthma vs controls
Whole biopsy Laser-dissected smooth muscle
Log2
(fold change) Log2
(fold change)
-Log10
(p-value)
-Log10
(p-value)
Yick et al. Allergy 2014;69:1233-1240Yick et al. ERJ 2013;42:662-670

20. Pathway analysis: ASM asthma vs controls
• IPA score 40
Yick et al.
Allergy 2014
69:1233-1240

21. r = -0.740
p < 0.01
r = -0.746
p < 0.01
Relationship between steroid-induced changes
in ASM gene expression and in PC20
Yick et al. Am J Respir Crit Care Med 2013;187:1076-84

22. disease
domain
Symptoms
Functional
Cellular
Molecular
diagnosis
& therapy
disease
phenotype
√
√
Structure ?
√
√ Clinically
applicable?

23. Exhaled VOCs
in asthma, COPD and lung cancer
Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205

24. Human olfactory pattern recognition
A trillion smells by 400 sensors
Bushdid et al. Science 2014;343:1370-1372

25. Exhaled metabolomics:
eNose platform

26. Clustering severe asthma
merely based on eNose data only
Ward clustering and Similarity Profile Analysis
0200400600800100012001400
17
79
92
7
28
78
106
18
27
63
26
48
13
33
83
47
84
101
65
95
34
77
60
61
32
91
55
9
94
104
49
73
68
3
74
66
97
44
72
11
16
75
76
85
89
21
50
107
87
93
58
37
56
30
100
19
70
1
64
90
22
12
105
25
31
36
52
43
6
10
62
5
39
86
45
35
67
8
5
3
Brinkman et al. and U-BIOPRED Study
ATS 2014: A2171

27. Cluster 1 Cluster 2 Cluster 3
Percentage
0.00.20.40.60.81.0
1 2 3
0102030405060
Sputum_eos_percent
eNose clusters of severe asthma
Sputum eosinophils and steroids usage
60
30
0
Sputum eosinophils, p = 0.04
Percentage
NO
YES
100
50
0
Chronic oral steroids usage, p =
0.04
Percentage
Cluster 1 Cluster 2 Cluster 3 Cluster 1 Cluster 2 Cluster 3
Brinkman et al. and U-BIOPRED Study
ATS 2014: A2171

28. Composite molecular breathprints can best predict
responsiveness to oral steroids in asthma
eNose
FeNO
Sput Eos
PD15HS
Van der Schee et al. Clin Exp Allergy 2013;43:1217-1225

29. disease
domain
Symptoms
Functional
Cellular
Molecular
diagnosis
& therapy
disease
phenotype
√
√
Imaging ?
√
√

30. 2013-2014
2014-2015
U-BIOPRED time schedule

31. Conclusions
• Asthma represents a complex group of clinical and
biological phenotypes
• Unbiased ‘omics’ analysis can capture the
complex pathobiology of these phenotypes
• Integration of ‘omics’ with clinical markers of
disease will promote precision medicine
• (External) validation is the absolutely key:
– McShane Nature 2013 and BMC Medicine 2013
• Breathomics can bring this approach towards the
doctor’s office www.ubiopred.eu
p.j.sterk@amc.nl

32. 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
UCB
Boehringer Ingelheim
Almirall
Johnson & Johnson / Janssen
Roche/Genentech
Amgen
Merck
Biosci
Aerocrine
Synairgen
Philips Research
Netherlands Lung Foundation
Asthma UK
European Lung Foundation
EFA
Int Primary Care Respir Group
Lega Italiano Anti Fumo