1. in the translation from lab to bedside
New research methods
in the translation from lab to bedside

2. Disclosure
The Academic Medical Centre of the University of Amsterdam has received a public-private grant for the U-BIOPRED project from the Innovative Medicines Initiative (IMI), covered by the European Union (EU) and the European Federation of Pharmaceutical Industries and Associations (EFPIA)

3. Funded by the European Union
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

4. New research methods towards the bedside
Diagnosis or phenotyping?
Clinical or biological markers?
New research methods
Towards the doctor’s office

5. Phenotypes: pattern recognition Endotypes: understanding
Clinical phenotypes
Clinical physiologic characteristics
Bio-clinical phenotypes
Add (patho)biology at cellular
and molecular level
Endotypes
Identifiable molecular pathways driving a disease state

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

7. Asthma phenotypes Hekking & Bel, JACI in Practice; 2014
He made a distinction between age of onset and the presence of the biomarker eosinophilia. In the green you can see the childhood onset allergic asthma. In bleu you can see the group of patients with adult-onset asthma with high eosinophilic inflammation. In yellow there is the group of patients with non-eosinophilic adult-onset asthma and in red the childhood onset-non eosinophilic asthma.
In this figure you can also see the phenotypes described that were based on triggers (such as occupation, aspirin etc) and based on symptoms (exacerbation, cough). But some of these subgroups were identified by unsupervised cluster analyses, like the one of… nieuwe dia
Hekking & Bel, JACI in Practice; 2014

8. Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205
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

9. Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205
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

10. U-BIOPRED clinical contour plots in children
Fleming et al. and U-BIOPRED Study: Eur Respir J 2015:46:

11. Predicting risk of exacerbation by GINA parameters
AUC
AUC
Hist
Hist
Spir
Hist
Spir
FeNO
Hist
Hist
Spir
Hist
Spir
FeNO
Loijmans et al. for Accurate Study, Thorax 2016, accepted

12. Clinical phenotypes of mixed patient group with airways diseases (asthma and COPD)
Rootmensen et al. J Asthma 2016, accepted

13. Clinical phenotypes of mixed patient group with airways diseases (asthma and COPD)
Reversible
Mild obstruction
Severe obstruction
Atopic
Eosinophilic
High BMI
Low TL,CO
Low BMI
Rootmensen et al. J Asthma 2016, accepted

14. Bio-clinical 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
reversible LF
severe
obese
oldest
ICS, OCS, etc
impaired LF
Moore et al. for SARP: J Allergy Clin Immunol 2014;133:

15. Bio-clinical clustering of COPD
Rennard et al. for ECLIPSE. Ann Am Thor Soc 2015;12:

16. Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205
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

17. Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205
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

18. Systems medicine approach of U-BIOPRED
Wheelock et al. and U-BIOPRED. ERJ 2013;42:

19. Serum proteomics in asthma
Eosinophilic
Neutrophilic
Paucigranulocytic
So here we have a network of proteomic data obtained from our asthma patients so cohorts A, B and C
Here, each node represents a patients or group of patients proteome
Edges, show that there is a relationship between these proteomes
So all these patients shown in this network cluster have a relationship in terms of their proteome
So we can see we have have a number of different clusters defined by different colours
So I need to highlight here, that the software has not defined these coloured clusters and that these have been deduced through extensive amounts of analysis incorporating protein and clinical data and looking for persistence between networks and I hope this will become clearer as we look through some of the data
Node = participant or group of participants proteome
Edge = link between participants proteomes
Skipp et al. and U-BIOPRED Study: ERS Amsterdam 2015
Schofield et al. and U-BIOPRED Study, ERS London 2016

20. Serum proteomics in relation to clinical and molecular biomarkers
Sputum eosinophils
Periostin
High
Low
High
Low
atopic
IL-13
High
Low
High
Low
Skipp et al. and U-BIOPRED Study: ERS Amsterdam 2015
Schofield et al. and U-BIOPRED Study, ERS London 2016

21. Handprints by multi-omics in sputum
Similarity Network Fusion
Transcriptomics (2324)
SomaLogic (258)
Eicosanoids (7)
UPLC MS/MS (17)
Lefaudeux et al. and U-BIOPRED Study: ERS Amsterdam 2015

22. Handprints by multi-omics in sputum
Cluster SH1 (N=22)
Cluster SH2 (N=27)
Cluster SH3 (N=23)
P-value
Cohort
SA: 13
SAS: 7
MMA: 2
SA: 11
SAS: 8
MMA: 8
SAS: 5
MMA: 5
Gender
Female: 55 %
Female: 59 %
Female: 52 %
FEV1 (% predicted)
62.4 ± 21.6
77.9 ± 19.8
66.1 ± 24.6
FEV1/FVC
0.572 ± 0.142
0.669 ± 0.116
0.582 ± 0.1
Sputum eosinophils (%)
2.42 (0.40 – 10.5)
0.39 (0 – 3.64)
29.0 (1.04 – 52.6)
<
Sputum neutrophils (%)
75.7 ± 18.1
47.9 ± 19.4
34.5 ± 21.3
<
Sputum macrophages (%)
9.88 (4.16 – 16.8)
48.8 (29.9 – 63.5)
26 (18.3 – 38.3)
Periostin (ng/ml)
50.1 (45.5 – 61.4)
42.4 (35.3 – 50)
50.2 (41.3 – 70.7)
IL-13 (pg/ml)
0.49 (0.28 – 0.96)
0.47 (0.27 – 0.66)
0.935 (0.58 – 1.18)
Lefaudeux et al. and U-BIOPRED Study: ERS Amsterdam 2015

23. Exhaled volatile organic compounds (VOCs)
Metabolomics
Exhaled volatile organic compounds (VOCs)
Breathomics bestaat uit de woorden breath, adem, en omics, een onderzoeksgebied binnen de biologie waarin een totaliteit bestudeerd wordt. Het gaat dan dus om alle potentiele biomarkers in de adem. Vaak is dit gebaseerd op patroonherkenning, en dit leidt tot een zgn breathprint, oftewel een vingerafdruk van de adem.
Van der Schee et al. Chest 2015;147:
Boots et al. Trends Mol Med 2015;21: 23

24. Training and external validation of eNose in adults:
asthma versus COPD
Accuracy: 85%
AUC: 0.93
Breath profiles of reversible asthma and COPD patients could be very well distinguished in both the training and validation sets. In the picture, the axes represent the principal components, the restructured raw sensor data. Every symbol represents one patient, blue being COPD, red asthma, rounds training set and squares validation set. You can see that the COPD and asthma clouds are separated, and that the group centroids of training and validation sets are virtually overlapping in both asthma and COPD. The accuracy of the test is 85% both in the training and validation sets.
●Training set COPD ●Training set asthma
▄Validation set COPD ▄Validation set fixed asthma
Fens et al. Clin Exp Allergy 2011;41: 24

25. eNose vs BAL eosinophils in asthma
Fens et al. Am J Respir Crit Care Med 2015;191:

26. Topological analysis of eNose-driven clusters of severe asthma
Significantly different in:
blood eos
blood neutros
oral steroid dose
Replicated at 12 months
Brinkman et al. and U-BIOPRED Study. ERS Amsterdam 2015

27. Breath Cloud analysis

28. From diagnoses to precision medicine
Agusti et al. Treatable traits….ERJ 2016;47:

29. Conclusions
Phenotyping of patients with airways disease (asthma or COPD) requires clinical and biological parameters
Current sampling techniques and bioinformatics allows capturing bio-clinical phenotypes and (some) endotypes
Complex biomarkers, rather than singular ones, are most likely to provide such guidance: this is now being tested
It is even feasible to get this towards point-of-care, thereby going to provide the required precision medicine to patients with airways disease