in the translation from lab to bedside New research methods in the translation from lab to bedside p.j.sterk@amc.nl

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)

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 www.ubiopred.eu

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

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

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

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

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

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

U-BIOPRED clinical contour plots in children Fleming et al. and U-BIOPRED Study: Eur Respir J 2015:46:1322-33

Predicting risk of exacerbation by GINA parameters AUC 0.77-0.80 AUC 0.71-0.72 Hist Hist Spir Hist Spir FeNO Hist Hist Spir Hist Spir FeNO Loijmans et al. for Accurate Study, Thorax 2016, accepted

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

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

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;133:1557-1563

Bio-clinical clustering of COPD Rennard et al. for ECLIPSE. Ann Am Thor Soc 2015;12:303-312

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

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

Systems medicine approach of U-BIOPRED Wheelock et al. and U-BIOPRED. ERJ 2013;42:802-825

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

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

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

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 0.420 3 Gender Female: 55 % Female: 59 % Female: 52 % 0.875 3 FEV1 (% predicted) 62.4 ± 21.6 77.9 ± 19.8 66.1 ± 24.6 0.041 1 FEV1/FVC 0.572 ± 0.142 0.669 ± 0.116 0.582 ± 0.1 0.009 1 Sputum eosinophils (%) 2.42 (0.40 – 10.5) 0.39 (0 – 3.64) 29.0 (1.04 – 52.6) < 0.001 2 Sputum neutrophils (%) 75.7 ± 18.1 47.9 ± 19.4 34.5 ± 21.3 < 0.001 1 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) 0.046 2 IL-13 (pg/ml) 0.49 (0.28 – 0.96) 0.47 (0.27 – 0.66) 0.935 (0.58 – 1.18) 0.013 2 Lefaudeux et al. and U-BIOPRED Study: ERS Amsterdam 2015

Exhaled volatile organic compounds (VOCs) Metabolomics Exhaled volatile organic compounds (VOCs) www.breathcloud.org 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;147:224-231 Boots et al. Trends Mol Med 2015;21:633-644 23

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;41:1371-1378 24

eNose vs BAL eosinophils in asthma Fens et al. Am J Respir Crit Care Med 2015;191:1086-1088

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

Breath Cloud analysis www.breathcloud.org

From diagnoses to precision medicine Agusti et al. Treatable traits….ERJ 2016;47:410-419

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 p.j.sterk@amc.nl