1. Grazie per aver scelto di utilizzare a scopo didattico questo materiale delle Guidelines 2011 libra. Le ricordiamo che questo materiale è di proprietà dell’autore e fornito come supporto didattico per uso personale.

2. Phenotyping severe asthma The U-BIOPRED project p.j.sterk@amc.nl Department of Respiratory Medicine Academic Medical Center University of Amsterdam The Netherlands

3. 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’

4. 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.

5. The three determinants of a phenotype Time Genetics Environment

6. 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. Complex biological systems: The secret of life

7. Genes, environment and time Richards K. Life. Little, Brown & Company, London, 2010

8. Genes Cell differentiation & activation Organ structure & function Cell-cell interaction Gene-& post- transcriptional regulation Macro physiology Organism health & disease Capturing phenotypes

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

10. 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

11. Asthma severity and control 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 No treatment Intensive treatment controlledmildestmild severe uncontrolled severe most severe

12. “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

13. Consensus Definition and classification 1.Problematic asthma All asthma that remains uncontrolled despite prescription of high intensity treatment 2.Difficult asthma Mild-moderate asthma that remains uncontrolled Adherence <50%, VCD, dysfunctional breathing, psychosocial Persistent exposures Untreated co-morbidity 3.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

14. 4 3 Moore et al. Am J Respir Crit Care Med 2010;181:315-323 125

15. Asthma: complex biology Central Peripheral Normalasthma Mauad, Bel, Sterk. J Allergy Clin Immunol 2007;120:997-1009

16. Additional phenotypic markers?

17. When will a disease marker be useful? ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Reference feature Marker A Marker B

18. Disease markers which provide complementary information in asthma Factor analysis Rosi et al. JACI 1999;103:232 PC20 Reversibility Age FEV 1 FVC Sputum - eosinophils - ECP

19. Phenotypic cluster analysis in asthma Haldar et al. Am J Respir Crit Care Med 2008;178:218-224 Eosinophilic inflammation Symptoms Discordant Symptoms Obese Late onset Controlled Mixed onset Discordant Inflammation Atopic and early onset Concordant symptoms and inflammation

20. Exhaled nitric oxide + FEV 1 predict lung function decline in severe asthmatics during 5 years prospective follow-up Van Veen et al, ERJ 2008;32:344-349 Baseline FEV 1 ≤ 80% Baseline FEV 1 > 80% Exhaled NO (ppb) Change in FEV 1 (ml)

21. Heatmap for molecular phenotyping from cytokines in BAL of severe asthma Brasier et al. J Allergy Clin Immunol 2008;121:30-37

22. Baines et al. J Allergy Clin Immunol 2011;127:153-160 Transcriptomic phenotypes from sputum in asthma

23. Gomes-Alves et al. Clin Biochemistry 2010;43:168-177 Protein expression profiling in serum in asthma, COPD, cystic fibrosis and controls (SELDI-TOF-MS signatures) AUC of ROC Asthma vs COPD 100% Asthma vs CF 100% COPD vs CF 100%

24. Electronic nose analysis Fens et al. Am J Respir Crit Care Med 2009:180:1076-82

25. Training and validation sets by eNose: asthma versus COPD Fens et al. ATS 2010, submitted ● Training set COPD ● Training set asthma ▄ Validation set COPD ▄Validation set fixed asthma Accuracy: 85% AUC: 0.93

26. disease domain Symptoms Functional Cellular Molecular diagnosis & therapy disease phenotype √ √ ! !

27. Continuous recording and fluctuations of respiratory resistance in asthma Slats et al. Am J Respir Crit Care Med 2007;176:121-128

28. Respiratory impedance in asthma and COPD Asthma COPD Muskulus et al. J Appl Physiol 2010;109:1582-1591

29. 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

30. ROC curve using 5-dimensional reconstruction of respiratory impedance dynamics in discriminating asthma and COPD Asthma COPD Discriminant score Muskulus et al. J Appl Physiol 2010;109:1582-1591

31. disease domain Symptoms Functional Cellular Molecular diagnosis & therapy disease phenotype √ √ ! !

33. 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 Biosci Aerocrine Synairgen Philips Research Netherlands Asthma Foundation Asthma UK European Lung Foundation EFA Int Primary Care Respir Group Lega Italiano Anti Fumo

34. 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

35. 1025 subjects including adults ánd children AdultsChildren Severe asthma 525100 Mild asthma10050 Healthy controls 100 Infants severe recurrent wheeze 100 Infants mild recurrent wheeze 50

36. 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

37. Study design screeningbaselinefollow-up 1Follow-up 2 0 3-6 24-30 Months bronchoscopy exacerbations tele-monitoring

38. U-BIOPRED Workpackages 1 Coördination and managementSterk, Higenbottam, Wagers, Sondervan 2 Consensus generationBel, Compton 3 Cross-sectional and longitudinal cohortsChung, Gerhardsson 4 Bronchoscopic assessmentChanez, Sousa 5 Pre-clinical human modelsKrug, Lewis 6 Pre-clinical laboratory modelsAdcock, Knowles 7 Omics technologiesDjukanovic, Corfield 8 Bioinformatics and systems biologyAuffray, Manta 9 DisseminationWagers, Compton 10 EthicsDe Boer, Higenbottam

39. Patient reported Clinical Functional Cellular Molecular ‘Systems Medicine’ Auffray, Adcock, Chung, Djukanovic, Pison, Sterk. Chest 2010;137:1410-1416. www.ubiopred.eu

40. Unresolved disease problem Genomics Transcriptomics Proteomics Metabolomics Cytology Histology Quantitative morphology (imaging) Organ function and dynamics Clinical expression and patient perception Formalize questions Ensure quality Integrate data Perturb the system Refine unbiased computational model by iteration biobanking knowledge repository Generate hypotheses Add open source public data Kaminsky, Irvin, Sterk. J Appl Physiol 2011: EPub.

41. Kaminsky, Irvin, Sterk. J Appl Physiol 2011, EPub.

42. 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