EDUCATIONAL MATERIAL

Transcription

1 ERS Annual Congress Munich 6 10 September 2014 EDUCATIONAL MATERIAL Meet the Expert - ME9 Asthma: a complex syndrome of many diseases? Tuesday, 9 September :00 14:00 Room 11 (ICM)

2 You can access an electronic copy of these educational materials here: To access the educational materials on your tablet or smartphone please find below a list of apps to access, annotate, store and share pdf documents. iphone / ipad Adobe Reader - FREE With the Adobe Reader app you can highlight, strikethrough, underline, draw (freehand), comment (sticky notes) and add text to pdf documents using the typewriter tool. It can also be used to fill out forms and electronically sign documents. UPAD Lite - FREE UPAD Lite is an advanced note-taking application with annotation features. You can handwrite notes, highlight text, add sticky notes and reference images and export any type of document as a PDF or PNG file by or to cloud services. Noteability - $4.99 Noteability uses CloudServices to import and automatically backup your PDF files and allows you to annotate and organise them (incl. special features such as adding a video file). On ipad, you can bookmark pages of a note, filter a PDF by annotated pages, or search your note for a keyword. Android Adobe Reader - FREE The Android version of Adobe Reader lets you view, annotate, comment, fill out, electronically sign and share documents. It has all of the same features as the ios app like freehand drawing, highlighting, underlining, etc. iannotate PDF - FREE You can open multiple PDFs using tabs, highlight the text and make comments via handwriting or typewriter tools. iannotate PDF also supports Box OneCloud, which allows you to import and export files directly from/to Box. ez PDF Reader - $3.99 With the ez PDF reader you can add text in text boxes and sticky notes; highlight, underline, or strikethrough texts or add freehand drawings. Add memo & append images, change colour / thickness, resize and move them around as you like.

3 ERS monograph Difficult-to-Treat Severe Asthma Edited by K.F. Chung, E.H. Bel and S.E. Wenzel ISBN A comprehensive summary of the reasons for and mechanisms underlying difficult-totreat severe asthma, this book also provides a physician s guide to the management of this condition. If you re an ERS member, you automatically have full online access to the ERS Monographs. To buy printed copies, visit the ERS Bookshop in Hall A1, stand D.01. Find out more about the ERS Monograph and other ERS publications at erspublications.com

4 Meet the Expert 9 Asthma: a complex syndrome of many diseases? Prof. Dr Peter Jan Sterk Department of Respiratory Medicine Academic Medical Center University of Amsterdam P.O. Box DE Amsterdam Netherlands p.j.sterk@amc.nl AIMS: It is now evident that asthma is not a single disease but rather a syndrome of overlapping phenotypes that involve several different mechanisms. The aims of the session are to: a) Provide evidence for the segregation of patients into distinct phenotypes; b) Highlight some of the key asthma phenotypes; and c) Indicate the treatment options that are available for patients with the distinct phenotypes. HERMES LINKS ADULT: B.1 Airway diseases. HERMES LINKS PAEDIATRIC: Bronchial Asthma and other wheezing disorders. TARGET AUDIENCE: Pulmonologists, respiratory physicians, clinical researchers, general practitioners, research fellows, nurses, allergologists, and basic scientists. SUMMARY It has long been recognized that asthma has various clinical presentations. Patients present themselves at various ages, various asthma duration, degrees of atopy, levels of lung function, co-morbid conditions and treatment needs. Based on clinical expertise, clinicians are handling this on a case by case basis, which can lead to some degree of tension between asthma management based on international guidelines [1] and the desired, individualized treatment. During the past 5 years there have been multiple international Task Forces addressing the clinical needs to phenotype asthma patients. This starts with defining clinical measures, such as asthma control and severity [2]. Asthma control refers to the extent to which the manifestations of asthma have been reduced by treatment. Whilst asthma severity is then derived from the intensity of treatment that is required to achieve good control [2]. These concepts have very much helped to structure our thinking about this chronic disease. How should control and severity be measured? This has been dealt with in a separate recommendation by ERS and ATS, providing the definition and degrees of exacerbations, the measurements of control (questionnaires, lung function, composite scores) and the potentially most informative biomarkers in asthma [3]. Severe asthma has now been carefully defined including a stepwise diagnostic flow chart on how to get from difficult asthma to real severe asthma [4]. This has led to a recent consensus statement by ERS and ATS on the existing evidence for using particular measurements in the monitoring severe asthma and particularly for guiding asthma treatment [5]. The latter includes explicit recommendations for therapeutic interventions based on the GRADE weighing system [5]. Will this be enough, or is there an explicit contribution of cellular or molecular biomarkers in the phenotyping of asthma? When exploring this, one could select single cells or molecules, or use integrative, composite biological fingerprints. The latter are likely to better represent the complex 4

5 biology of chronic diseases like asthma [6], which can now be applied by measuring the composite composition of complex biological samples at the RNA (transcriptomics), protein (proteomics) or metabolite (metabolomics level) [7]. For centuries clinicians have been familiar with integrating information in health and disease. This is to say: clinical information. In fact, medicine has been very successful in switching between three levels of disease phenotyping. As a first level, single symptoms (e.g. wheezing) represent the cornerstone of the diagnostic process. Second, multiple disease features in combination (e.g. ACQ, or a Th2-high profile) are considered to be even more informative. And finally, the most valuable medical information relies on pattern recognition of composite information (e.g. exacerbation or inflammation). The latter process is empiric and relies on repeated training and validation. All doctors feel they can establish an exacerbation of asthma, whilst after long efforts the definition of an exacerbation has only recently been distilled [3]. When adding single biomarkers to the cluster analyses, it appears that sputum eosinophilia and neutrophilia are reshaping the asthma phenotypes [8]. Indeed, sputum eosinophils have shown to be highly valuable in predicting clinical course and guiding asthma steroid treatment [9]. This has now been confirmed by meta-analyses, thereby being entirely evidence-based [9], even though for severe asthma the evidence of using sputum [5] or circulating eosinophils [10] is still building up. Furthermore, newly developed therapies with e.g. anti-il-5 [11] or blocking the IL-4 receptor [12] have been very successful in patients purposely selected by featuring elevated sputum eosinophils despite high dose steroid therapy. Given the complexity of the biology of asthma it is surprising that, regardless of its pathophysiological nature, a single biomarker can already provide such valuable information. Nevertheless, the promise of delivering better, composite cellular and molecular markers has yet to be fulfilled. Computational pattern recognition of such biomarker signatures will then be analogous to powerful clinical pattern recognition of complex entities. Therefore, it can be envisaged that high-throughput omics technologies will allow a step change in biomarker discovery and application in respiratory medicine [7,13]. The first evidence that transcriptomics or metabolomics are useful in phenotyping asthma is emerging [14]. This also includes point-of-care technology as provided by metabolomics of exhaled air (breathomics) [15]. The key challenge now is to rigorously standardize the application and analysis of these omics technologies in the discovery of biomarker signatures. Fortunately, there are recent recommendations available on the computational and staged validation of omics biomarker signatures [16]. This also includes stringent strategies to limit false discoveries [17]. And most importantly, stepwise validation criteria for application of omics in clinical medicine have recently been published [18]. It is envisaged that in this way biomarker signatures will gradually enter clinical medicine. Conclusions Asthma exhibits clinical phenotypes that emerge for cluster analysis. Adding biological markers reshapes these phenotypes, which may facilitate the development of targeted therapies. In fact, metaanalysis is showing that guiding asthma therapy based on sputum eosinophilia is improving disease outcome considerably. When considering the complex biology of health and disease it can be envisaged that composite molecular fingerprints have the best prospect as biomarkers in the phenotyping of patients. The EU-IMI project U-BIOPRED is currently delivering the data for this strategy in the phenotyping of severe asthma ( REFERENCES 1. Global Initiative for Asthma. Global strategy for asthma management and prevention. Update May The international standard website on asthma 2. Taylor DR, Bateman ED, Boulet L-P, Boushey HA, Busse WW, Casale TB, Chanez P, Eright PL, Gibson PG, de Jongste JC, Kerstjens HAM, Lazarus SC, Levy ML, O Byrne PM, Partridge MR, Pavord ID, Sears MR, Sterk PJ, Stoloff SW, Szefler SJ, Sullivan SD, Thomas MD, Wenzel SE, Reddel HK. A new perspective on concepts of asthma severity and control. Eur Respir J 2008;32: The best paper on distinguishing asthma control and severity 5

6 3. Reddel HK, Taylor DR, Bateman ED, Boulet L-P, Boushey HA, Busse WW, Casale TB, Chanez P, Enright PL, Gibson PG, de Jongste JC, Kerstjens HAM, Lazarus SC, Levy ML, O Byrne PM, Partridge MR, Pavord ID, Sears MR, Sterk PJ, Stoloff SW, Sullivan SD, Szefler SJ, Thomas MD, Wenzel SE. An official American Thoracic Society / European Respiratory Society statement: asthma control and exacerbations. Standardizing endpoints for clinical asthma trials and clinical practice. Am J Respir Crit Care Med 2009;180: The most up to date and comprehensive document on measures of control, exacerbations and future risk of asthma 4. Bel EH, Sousa A, Fleming L, Bush A, Chung KF, Versnel J, Wagener AH, Wagers SS, Sterk PJ, Compton CH on behalf of U-BIOPRED Study. Diagnosis and definition of severe refractory asthma: an international consensus statement from the Innovative Medicines Initiative (IMI). Thorax 2011;66: A stepwise diagnostic flow chart on how to come from difficult asthma to real severe asthma 5. Chung KF, Wenzel SE, Brozek J, Bush A, Castro M, Sterk PJ, Adcock IM, Bateman ED, Bel EH, Bleecker ER, Boulet L-P, Brightling C, Chanez P, Dahlen S-E, Djukanovic R, Frey U, Gaga M, Gibson PG, Hamid Q, Jajour NN, Mauad T, Sorkness RL, Teague WG. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. Eur Respir J 2014;43: The very recently published consensus report by ERS and ATS on severe asthma and its diagnosis, phenotyping, key measures, and explicit treatment recommendations. 6. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, Chen R, Miriami E. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012;148: Wheelock CE, Goss VM, Balgoma D, Nicholas B, Brandsma J, Skipp PJ, Snowden S, D'Amico A, Horvath I, Chaiboonchoe A, Ahmed H, Ballereau S, Rossios C, Montuschi P, Fowler SJ, Adcock IM, Postle AD, Dahlén SE, Rowe A, Sterk PJ, Auffray C, Djukanović R and the U-BIOPRED Study Group. Application of omics technologies to biomarker discovery in inflammatory lung diseases. Eur Respir J 2013;42: The most comprehensive review of omics technologies 8. Moore WC, Hastie AT, Xingnan L, Huashi L, Busse WW, Jarjour NN, Wenzel SE, Peters SP, Meyers DA, Bleecker ER. Sputum neutrophil counts are associated wore severe asthma phenotypes using cluster analysis. J Allergy Clin Immunol 2014;133: Petsky HL, Cates CJ, Lasserson TJ, Li AM, Turner C, Kynaston JA, Chang AB. A systematic review and meta-analysis: tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils). Thorax 2012;67: Katz LE, Gleich GJ, Hartley BF, Yancey SW, Ortega HG. Blood eosinophil count is a useful biomarker to identify patients with severe eosinophilic asthma. Ann Am Thor Soc 2014;11: Nair P, Pizzichini MM, Kjarsgaard M, Inman MD, Pizzichine E, Hargreave FE, O Byrne PM. Mepoluzimab for prednisone-dependent with sputum eosinophilia. N Engl J Med 2009;360: Wenzel SE, Ford L, Pearlman D, Spector S, Sher L, Skobieranda F, Wang L, Kirkessili S, Rocklin R, Bock B, Hamilton J, Ming JE, Radin A, Stahl N, Yancopoulos GD, Graham N, Pirozzi G. Dupilumab in persistent asthma with elevated eosinophil levels. N Engl J Med 2013;368: Bousquet J, Anto JM, Sterk PJ, Adcock IM, Chung KF, Roca J, et al. Systems medicine and integrated care to combat chronic noncommunicable diseases. Genome Med 2011;3(7): Wagener AH, Yick CY, Brinkman P, van der Schee MP, Fens N, Sterk PJ. Toward composite molecular signatures in the phenotyping of asthma. Ann Am Thorac Soc 2013;10:S197-S Fens N, van der Schee MP, Brinkman P, Sterk PJ. Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions. Clin Exp Allergy 2013;43: Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND. Molecular signatures from omics data: from chaos to consensus. Biotechnol J 2012 Aug;7(8): An extremely useful guide to get grips on complex data. 17. Broadhurst DI, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006 Dec 4;2(4): The standard strategies to prevent overfitting and overinterpretation. 18. McShane LM, Cavanagh MM, Lively TG, Eberhard DA, BIgbee WL, Williams PM, Mesirov JP, Polley MYC, Kim KY, Tricoli JV, Taylor JMG, Shuman DJ, Simon RM, Doroshow JH, Conley 6

7 BA. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BMC Medicine 2013;11:220:1-15. The best review of all steps required to validate omics in clinical research. EVALUATION 1. What is the difference between asthma control and asthma severity? a. In the clinic there is no difference between control and severity. b. Severity is derived from the medication needs to achieve control. c. Control is based on today s severity of the disease. d. Exacerbations are making the difference between control and severity. 2. Phenotyping of asthma by using a biological biomarker has led to better management with improved disease outcome. This is evidence-based (level A). a. This is true. b. This is not true, but evidence level B has been demonstrated. c. This is true in children only. d. This is not true. 3. Anti-IL-5 and blocking the IL-4 receptor have shown large benefit to patients using high doses of steroids. Which biomarker can be used to select the patients with the largest benefit? a. Sputum neutrophils. b. Sputum neutrophils and some evidence for circulating neutrophils. c. Sputum eosinophils. d. Sputum eosinophils and some evidence for circulating eosinophils. 4. Omics approaches aim to measure: a. Fluctuations of particular biomarkers over time. b. The full spectrum of a particular class of molecules in a composite biological mixture. c. The spectrum of a particular class of molecules in a composite biological mixture that is deviant from the normal composition. d. The molecular mechanisms that link genes to disease expression. Please find all answers at the back of your handout materials 7

8 Asthma A complex syndrome of many diseases? p.j.sterk@amc.nl Department of Respiratory Medicine Academic Medical Center University of Amsterdam The Netherlands 8

9 Disclosures Co-ordinator of public-private Innovative Medicine Initiative (IMI) project U-BIOPRED sponsored by the EU and EFPIA 9

10 Aims 1. To discuss the rationale of using phenotypes and/or endotypes of asthma 2. To present the latest phenotypes of asthma as derived from clinical and biological parameters 3. To weigh the evidence as to whether patient phenotyping helps in clinical diagnosis and management 10

11 Asthma severity and control No treatment Intensive treatment controlled mildest mild mild severe uncontrolled severe most severe Cockcroft & Swystun JACI 1996;98: Taylor et al. ERJ 2008;32: Reddel et al. AJRCCM 2009;180;

12 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. 12

13 Clinical phenotypes of severe asthma Problematic asthma Fixed obstruction Truly severe asthma Uncontrolled asthma Non-adherent asthma Exacerbation prone Co-morbid asthma Refractory asthma no asthma Difficult asthma NAEPP 1997, ERS 1999, GINA 2002, ATS & SARP 2002, ENFUMOSA 2003, BIOAIR TENOR 2004, Paris 2007, ERS 2008, PSACI 2008, WHO 2009, U-BIOPRED 2011

14 Dendogram of clinical phenotypes Ward clustering and Similarity Profile Analysis 14

15 Moore et al. Am J Respir Crit Care Med 2010;181:

16 disease domain diagnosis & therapy disease phenotype Symptoms Functional Structure? Cellular Molecular? 16

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

18 What is a endotype? A definable subpopulation of disease with discrete pathogenetic pathways or distinct functional or pathophysiologicl mechanisms Anderson Lancet 2008;372:

19 The targets derived from basic science Polosa & Casale. Drug Discovery Today 2012;17:

20 Asthma (i) PEF (L/min) a b c (ii) PEF (L/min) a b c Weeks from commencement of budesonide Morning pre-bronchodilator PEF Post-bronchodilator PEF Evening pre-bronchodilator PEF 20

21 Asthma 21

22 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 22

23 Guiding asthma therapy by sputum eosinophils. Exacerbations by asthma severity Mild asthma Moderate to Severe asthma control strategy control strategy sputum strategy sputum strategy Jayaram et al. ERJ 2006;27:

24 Subphenotyping asthma Symptoms Discordant Symptoms Obese Late onset Atopic and early onset Concordant symptoms and inflammation Controlled Mixed onset Discordant Inflammation Eosinophilic inflammation Haldar et al. Am J Respir Crit Care Med 2008;178:

25 Dendogram of clinical phenotypes Ward clustering and Similarity Profile Analysis 25

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

27 Targeted therapy with anti-il5 in asthma Patients with sputum eosinophils > 3% despite steroid treatment Cumulative number exacerbations placebo anti-il5 Haldar et al. NEJM 2009;360:

28 Dupilumab: IL-4Rα antibody after 12 wks of subcutaneous therapy Anti-IL4Rα n=52 Placebo n=52 p Exacerbations (n) 3 23 <0.001 Change in FEV 1 (L) 0.05 (0.06) (0.06) <0.001 Change in ACQ (0.16) (0.16) Wenzel et al. NEJM 2013;368:

29 disease domain diagnosis & therapy disease phenotype Symptoms Functional Structure? Cellular Molecular 29

30 Single biomarker Biomarker Panel Composite signature Clinical Wheeze Asthma Control Questionnaire Functional FEV1 Small airways obstruction Exacerbation 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 30

31 Single biomarker Biomarker Panel Composite signature Clinical Wheeze Asthma Control Questionnaire Functional FEV1 Small airways obstruction Exacerbation 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 31

32 Single Pathophysiological biomarker Biomarker Probabilistic Composite understanding Panel pattern recognition signature Clinical Wheeze Asthma Control Questionnaire Functional FEV1 Small airways obstruction Exacerbation 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 32

33 Genes Gene expression Airway histology Lung function The patient respiratory disease phenotyping Genetics Epigenetics Transcriptome Proteome Metabolome Microbiome Immunity Inflammation Remodeling BHR Obstruction Symptoms Co-morbidity Quality of life Wheelock et al. and U-BIOPRED Study. Eur Respir J 2013;42:

34 disease domain diagnosis & therapy disease phenotype Symptoms Functional Structure? Cellular Molecular Clinically applicable? 34

35 Protein expression profiling in serum in asthma, COPD, cystic fibrosis and controls (SELDI-TOF-MS signatures) Gomes-Alves et al. Clin Biochemistry 2010;43:

36 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:

37 Exhaled volatile organic compounds (VOCs) 37

38 Exhaled VOCs e.g. in asthma, COPD and lung cancer Wagener et al. Ann Am Thor Soc, 2013;10:S197-S205 38

39 Breathomics by GC-MS and asthma phenotyping eosinophilia controlled neutrophilia Ibrahim et al. Thorax 2011;66:

40 Exhaled metabolomics: enose platform Breath profile originating from platform of enoses 40

41 Training and external validation of enose in adults: asthma versus COPD Accuracy: 85% AUC: 0.93 Training set COPD Training set asthma Validation set COPD Validation set fixed asthma Fens et al. Clin Exp Allergy 2011;41:

42 Clustering severe asthma merely based on enose data only Brinkman et al. and U-BIOPRED Study ATS 2014: Sunday afternoon, session A93: A

43 enose clusters of severe asthma Topological analysis N=26 N=34 N=17 Brinkman et al. and U-BIOPRED Study ATS 2014: A

44 enose clusters of severe asthma Sputum eosinophils and steroids usage Percentage Sputum eosinophils, p = 0.04 Sputum_eos_percent Percentage Chronic oral steroids usage, p = Cluster 1 1 Cluster 2 2 Cluster 3 3 Cluster 1 1 Cluster 2 2 Cluster 3 3 Percentage NO YES Brinkman et al. and U-BIOPRED Study ATS 2014: Sunday afternoon, session A93: A

45 Prospective monitoring of enose breathprints in asthma during loss of control / exacerbation by ICS withdrawal p= ns enose Breathprint principal component baseline loss of control or exacerbation recovery 4 weeks after Fens et al. submitted to ERS

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

47 Conclusions Asthma is a hetergeneous disease, both clinically and biologically Sputum (and blood) eosinophilia currently represents the best validated biomarker for tailoring existing and new therapies in asthma Pattern recognition of omics signals is becoming a powerful tool for patient phenotyping and personalized medicine Breathomics can bring this towards the doctor s office p.j.sterk@amc.nl 47

48 Faculty disclosures Prof. Dr Peter Jan Sterk is the co-ordinator of public-private Innovative Medicine Initiative (IMI) project U-BIOPRED sponsored by the EU and EFPIA. 48

49 Answers to evaluation questions Please find all correct answers in bold below 1. What is the difference between asthma control and asthma severity? a. In the clinic there is no difference between control and severity. b. Severity is derived from the medication needs to achieve control. c. Control is based on today s severity of the disease. d. Exacerbations are making the difference between control and severity. 2. Phenotyping of asthma by using a biological biomarker has led to better management with improved disease outcome. This is evidence-based (level A). a. This is true. b. This is not true, but evidence level B has been demonstrated. c. This is true in children only. d. This is not true. 3. Anti-IL-5 and blocking the IL-4 receptor have shown large benefit to patients using high doses of steroids. Which biomarker can be used to select the patients with the largest benefit? a. Sputum neutrophils. b. Sputum neutrophils and some evidence for circulating neutrophils. c. Sputum eosinophils. d. Sputum eosinophils and some evidence for circulating eosinophils. 4. Omics approaches aim to measure: a. Fluctuations of particular biomarkers over time. b. The full spectrum of a particular class of molecules in a composite biological mixture. c. The spectrum of a particular class of molecules in a composite biological mixture that is deviant from the normal composition. d. The molecular mechanisms that link genes to disease expression. 49