Postgraduate Course 13 AirPROM: patient specific modelling ...Postgraduate Course 13 AirPROM: patient-specific modelling and systems biology living labs workshop Saturday, 1 September

ERS Annual Congress Vienna

1–5 September 2012

Postgraduate Course 13 AirPROM: patient-specific modelling and systems

biology living labs workshop

Saturday, 1 September 2012 14:00–17:30

Room: Schubert 5

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Systems biology: 21st century science

Dr. Charles Auffray European Institute for Systems Biology and Medicine Functional Genomics and Systems Biology for Health

CNRS Institute of Biological Sciences Claude Bernard University and Ecole Normale Supérieure – Lyon

50, avenue Tony Garnier 69007 Lyon

France [email protected]

Aims Introduce concepts and methods of systems biology applied to medicine Describe U-BIOPRED consortium for understanding severe asthma as a use case Discuss extension to other respiratory diseases such as COPD in AirPROM Share vision on emergence of P4 medicine and future impact on healthcare

Summary From functional genomics to systems biology and systems medicine Systems approaches are being used to tackle the complexity and emerging properties of biological systems through exploratory and targeted investigations in iterative combinations of experiments with computational and mathematical modelling [1–4]. This research strategy, when applied to clearly formulated and formalized biomedical questions, enables understanding the dynamic behaviour of biological systems in normal and perturbed conditions. Evolution, development, physiology and disease are viewed in systems biology as dynamic processes that operate on widely different scales in space and time between biological states that are constrained by interrelationships among pathway and network components. In this context, detecting, understanding and treating disease translates into identifying and manipulating global perturbed networks rather than focusing only on unique failing components. It has been recognized early on that systems biology approaches have the potential to overcome hurdles in drug discovery [5, 6], and they have been applied already successfully in a variety of biomedical fields such as cancer [7–9], metabolic and cardiovascular diseases [10], infectious diseases [11], neurological diseases [12], with recent initial application to respiratory diseases [13]. Application of systems biology approaches to biomedical questions is thus opening the way to the development of systems medicine [14–16]. Understanding severe asthma through systems biology: U-BIOPRED as a use case The pre-competitive U-BIOPRED Consortium supported by the European Union and the European Federation of Pharmaceutical Industry Associations is using a systems approach to develop unbiased phenotype handprint biomarkers for the prediction of respiratory disease outcomes, focusing on understanding severe asthma [17, 18]. The aim of U-BIOPRED is to overcome the hurdles encountered in the development of drugs to efficiently control the clinical course of severe asthma through better definition of clinical phenotypes, the development of predictive fingerprint and phenotype handprint biomarkers validated in preclinical models of the disease. This systems medicine approach is being implemented similarly in the Severe Asthma Research Programme supported by the US National Institutes of Health [19]. It relies on the open-source knowledge management platform tranSMART to store, analyze and model a wide variety of biological, clinical and high-throughput functional genomics data [20, 21]. Initial phenotype handprints that distinguish asthma form COPD patients have been obtained through pattern recognition of volatile organic compounds in exhaled breath using electronic noses [22]. Further studies will be conducted within a cohort of paediatric and adult severe asthmatics followed up for disease progression and exacerbations to refine diagnostic criteria and phenotype definitions, by

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combining molecular, histological and patient-reported data. The aim is to predict efficacy of gold-standard and novel therapeutic interventions. Work by a coalition of biologists, clinicians, engineers and computer scientists at academic and industrial laboratories will be conducted in close interaction with patient organizations and a regulatory agency. Extension to other respiratory diseases such as COPD in AirPROM Prediction of severe asthma outcomes are being enhanced through a close partnership with the AirPROM Consortium, providing additional genetic and imaging data and multi-scale patient-specific computational models, which integrate molecular and disease networks with airway models [23, 24]. The cross-comparison of data and disease models made possible through sharing of common standard operating procedures and data analysis and modelling methodologies and tools is expected to also advance understanding of chronic obstructive pulmonary disease, and by extension serve as a basis for the study of other respiratory and complex chronic diseases [16]. Emergence of P4 medicine and its future impact on healthcare U-BIOPRED and AirPROM are examples of the move from the traditional reactive medicine based on the same treatment for all patients towards a predicitive and preventive medicine that will increasingly be personalized to the needs of the individual patient with their active participation. Systems approaches are catalyzing the emergence of Predictive, Preventive, Personalized, Participatory (P4) Medicine, revolutionizing medical practive and heathcare in the 21st century [25–30]. It is expected that P4 medicine will be able to overcome the current limitations of disease complexity (through stratification of patients and diseases by molecular diagnostics) and drug discovery (through the analysis and targeting of disease-perturbed networks). Systems P4 medicine is developing through an international network of systems biology and medicine centers dedicated to inter-disciplinary training and education, with the goal of reversing the trend in non-sustainable escalating costs in drug and diagnostics development as well as in the healthcare system. It should thus help also reducing the gap in healthcare between developed and developing countries. Acknowledgments I thank Peter Sterk (University of Amsterdam, U-BIOPRED coordinator); Scott Wagers (BioSci Consulting) U-BIOPRED manager; Chris Brightling (University of Leicester), AirPROM coordinator; Christophe Pison (University Hospital of Grenoble & EISBM); Leroy Hood (Institute for Systems Biology, Seattle); Zhu Chen (Center for Systems Biomedicine, Shanghai); Rudi Balling (Center for Systems Biomedicine, Luxembourg); and my U-BIOPRED, AirPROM and EISBM colleagues for their support in preparing this course. Support statement My work is supported by the CNRS, and in part by the EU in the context of the U-BIOPRED consortium (Unbiased Biomarkers for the PREDiction of respiratory disease outcomes, Grant Agreement IMI 115010). The formation of the European Institute for Systems Biology & Medicine (EISBM) hosted at Claude Bernard University is supported by the Lyonbiopole competitive cluster and its academic, industrial and local authority partners, including Grand Lyon, Région Rhône-Alpes, Direction de la Recherche et de la Technologie, and the Finovi Foundation. References

1. A new approach to decoding life: systems biology. Ideker, T., Galitski, T., Hood, L. (2001) Annu Rev Genomics Hum Genet 2:343-372.

2. Systems biology: a brief overview. Kitano, H. (2002) Science 295:1662-1664. 3. Modeling the heart - from genes to cells to the whole organ. Noble, D. (2002) Science

295:1678-1682. 4. From functional genomics to systems biology: concepts and practices. Auffray, C., Imbeaud,

S., Roux-Rouquié, M., Hood, L. (2003) C R Biol 326:879-892. 5. The impact of systems approaches on biological problems in drug discovery. Hood, L.,

Perlmutter, R.M. (2004) Nat Biotechnol, 22:1215-1217.

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6. Data integration: challenges for drug discovery. Searls, D.B. (2005) Nat Rev Drug Discov 4:45-58.

7. Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia. Zheng, P.Z., Wang, K.K., Zhang, Q.Y., Huang, Q.H., Du, Y.Z., Zhang, Q.H., Xiao, D.K., Shen, S.H., Imbeaud, S., Eveno, E., Zhao, C.J., Chen, Y.L., Fan, H.Y., Waxman, S., Auffray, C., Jin, G., Chen, S.J., Chen Z, Zhang J. (2005) Proc Natl Acad Sci USA 102:7653-7658.

8. Deciphering cellular states of innate tumor drug responses. Graudens, E., Boulanger, V., Mollard, C., Mariage-Samson, R., Barlet, X., Grémy, G., Couillault, C., Lajémi, M., Piatier-Tonneau, D., Zaborski, P., Eveno, E., Auffray, C., Imbeaud, S. (2006) Genome Biol 2006, 7:R19.

9. Network-based classification of breast cancer metastasis. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D., Ideker, T. (2007) Mol Syst Biol 3:140.

10. A thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lusis, A.J. (2006) J Lipid Res 47:1887-1890.

11. Systems biology of persistent infection: tuberculosis as a case study. Young, D., Stark, J., Kirschner, D. (2008) Nat Rev Microbiol 6:520-528.

12. A systems approach to prion disease. Hwang, D., Lee, I.Y., Yoo, H., Gehlenborg, N., Cho, J.H,. Petritis, B., Baxter, D., Pitstick, R., Young, R., Spicer, D., Price, N.D., Hohmann, J.G., Dearmond, S.J., Carlson, G.A., Hood, L.E. (2009) Mol Syst Biol 5:252.

13. A systems approach identifies molecular networks defining skeletal muscle abnormalities in chronic obstructive pulmonary disease. Turan, N., Kalko, S., Stincone, A., Clarke, K., Sabah, A., Howlett, K., Curnow, S.J., Rodriguez, D.A., Cascante, M., O’Neill, L., Eggington, S., Roca, J., Falciani, F. (2011) PLoS Comput Biol 7:e1002129.

14. Systems medicine: the future of medical genomics and healthcare. Auffray, C., Chen, Z. and Hood, L. (2009) Genome Medicine 1:2

15. Bridging the gap between systems biology and medicine Clermont, G., Auffray, C., Moreau, Y., Rocke, D.M., Dalevi, D., Dubhashi, D., Marshall, D., Raasch, P., Dehne, F., Provero, P., Tegner, J., Aronow, B.J., Langston, M.A., Benson, M. (2009) Genome Medicine 1:88

16. Systems medicine and integrated care to combat chronic noncommunicable diseases Bousquet, J., Anto, J.M., Sterk, P.J., Adcock, I.M., Chung, K.F., Roca, J., Agusti, A., Brightling, C. Wouters, E., Balling, R., Brookes, A.J., Charron, D., Pison, C. Chen, Z., Hood, L., Auffray, C. (2011) Genome Medicine 3:43

17. U-BIOPRED web site www.ubiopred.eu http://www.ubiopred.european-lung-foundation.org/ 18. Auffray, C., Adcock, I.A., Chung F.K., Djukanovic, R., Pison, C., Sterk, P. (2010) Chest

137:1410-1416. 19. Kaminsky, D.A., Irvin, C.G., Sterk, P.J. Complex systems in pulmonary medicine: a systems

biology approach to lung disease. (2011) J Appl Physiol 110:1716-1722. 20. Szalma, S., Koka, V., Khasanova, T., Perakslis, E.D. Effective knowledge management in

translational medicine. (2010) Translational Med 8:68. 21. How informatics can potentiate precompetitive open-source collaboration to jump-start drug

discovery and development. Szalma, S., Koka, V., Khasanova, T., Perakslis, E.D. (2010) Clin Pharmacol Therapeutics 87:614-616.

22. Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Fens, N., Zwinderman, A.H., van der Schee, M.P., de Nijs, S.B., Dijkers, E., Roldaan, A.C., Cheung, D., Bel, E.H., Sterk, P.J. (2009) Am J Respir Crit Care Med 180:1076-1082.

23. The human disease network. Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabasi, A.L. (2007) Proc Nat Acad Sci USA 104:8685-8690.

24. Computational physiology and the Physiome Project. Crampin, E.J., Halstead, M., Hunter, P., Nielsen, P., Noble, D., Smith, N., Tahwai, M. (2004) Exp Physiol 89:1-26.

25. Systems biology and new technologies enable predictive and preventative medicine. Hood, L., Heath, J.R., Phelps, M.E., Lin, B. (2004) Science 306:640-643.

26. Translational and clinical science - time for a new vision. Zerhouni, E.A. (2005) N Engl J Med 353:1621-1623.

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27. Global systems biology, personalized medicine and molecular epidemiology. Nicholson, J.K. (2006) Mol Syst Biol 2:52.

28. Prospective health care: the second transformation of medicine. Snyderman, R., Langheier, J. (2006) Genome Biol 7:104.

29. P4 medicine: the future around the corner. Sobradillo, P., Pozo, F., Agusti, A. (2011) Arch Bronconeumol 47:35-40.

30. Revolutionizing medicine in the 21st century. through systems approaches Hood, L., Balling, R., Auffray, C. (2012) Biotechnol J, in press.

Evaluation

1. Do Systems Biology approaches require (answer yes or no for each statement) a. Formulation of a scientific or medical question b. High-througput functional genomics data c. Iteration between experiment and modelling d. Experimental or computational perturbations

2. Is Systems Biology (answer yes or no for each statement)

a. A replacement of analytical approaches b. Applicable to any biological system c. A computer science sub-discipline d. An integrative research strategy

3. Are difficulties in treating severe asthma due to (answer yes or no for each statement)

a. The existence of multiple diseases b. Inability to predict the course of disease c. Inefficiency of the drug development pipeline d. Insufficient industrial investment

4. To improve treatment of severe asthma it is necessary (answer yes or no for each statement)

a. To develop public-private partnerships b. To develop complex biomarkers c. To invest more money in the healthcare system d. To do more high-throughput drug screening

Please find all answers at the back of your handout materials

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SYSTEMS BIOLOGY: 21ST CENTURY SCIENCEApplication to Respiratory Diseases

CHARLES AUFFRAY

[email protected]

European Institute for Systems Biology & Medicine

Functional Genomics and Systems Biology for HealthCNRS Institute of Biological Sciences

Claude Bernard University and Ecole Normale Supérieure – Lyon

AirPROM Postgraduate course – ERS – Vienna – September 1, 2012

Faculty disclosure

•Charles Auffray, Research Director, CNRS

•Founding Director European Institute for Systems Biology & Medicine – Lyon – France

•Member of Scientific Advisory Board – Institut Mérieux

•U-BIOPRED WP leader Bioinformatics and Systems Biology

•Member of AirPROM Scientific Advisory Board

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Introduction

AIMS

• Aim 1: Introduce concepts and methods of systems biology applied to medicine

• Aim 2: Describe U-BIOPRED consortium for understanding severe asthma as a use case

• Aim 3: Discuss extension to other respiratory diseases such as COPD in AirPROM

• Aim 4: Share vision on emergence of P4 medicine and future impact on healthcare

Characteristics of Integrative Systems Biology

• Considers the (emerging) properties and the dynamic behaviour of a biological system as different (more or less)

from those of its interacting (elementary or modular) components

• Combines exploratory investigations of global datasets with formalized hypotheses and question driven inquiries

• Aims at identifying the necessary and sufficient characteristics enabling understanding (explaining and predicting) and piloting of the behaviour of biological systems in normal (evolutionary, developmental, physiological) and perturbed (environmental

changes, disease, experimental) conditions

The Iterative Process of Integrative Systems Biology 1- Formulate and formalize a (general or particular) question

2- Define the components of an appropriate biological system and collect targeted and global data sets

3- Integrate them into an initial model of the system

4- Perturb systematically the system components (experimentally and through simulation) and study the results

5- Compare the responses oberved to those predicted by the model

6- Refine the model so that its predictions fit betterwith the experimental observations

•7- Design and test new perturbations allowing arbitration between

multiple competing hypotheses

8- Iterate the process until an answer to the initial question is obtained

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The Iterative Process of Integrative Systems Biology

« Middle-out » scheme for the study of biological systems

Integrative Systems Biology & Medicine

A new approach to decoding life: systems biology

Ideker, T., Galitski, T., Hood L.

(2001) Annu Rev Genomics Hum Genet 2:343-372

Systems biology: a brief overview.

Kitano, H. (2002) Science 295:1662-1664

Modeling the heart - from genes to cells to the whole organ.

Noble, D. (2002) Science 295:1678-1682

From functional genomics to systems biology: concepts and practices

Auffray, C., Imbeaud, S., Roux-Rouquié, M., Hood, L.

(2003) C R Biol 326:879-892


The impact of systems approaches on biologicalproblems

in drug discovery

Hood, L., Perlmutter, R.M. (2004) Nat Biotechnol22:1215-1217

Data integration: challenges for drug discovery

Searls, D.B. (2005) Nat Rev Drug Discov 4:45-58

47


Cancer

Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia

Zheng P.Z. et al. (2005) Proc Natl Acad Sci USA 102:7653-7658

Deciphering cellular states of innate tumor drug responses

Graudens, E. et al. (2006) Genome Biol 7:R19

Network-based classification of breast cancer metastasis

Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D., Ideker, T.

(2007) Mol Syst Biol 3:140


Metabolic and cardiovascular diseases

A thematic review series: systems biology approaches

to metabolic and cardiovascular disorders

Lusis, A.J. (2006) J Lipid Res 47:1887-1890

Infectious diseases

Systems biology of persistent infection:

tuberculosis as a case study

Young, D., Stark, J., Kirschner, D. (2008) Nat Rev Microbiol 6:520-528


Neurological diseases

A systems approach to prion disease

Hwang, D. et al. (2009) Mol Syst Biol 5:252

Respiratory diseases

A systems approach identifies molecular networks definingskeletal muscle abnormalities in chronic obstructive

pulmonary disease

Turan, N. et al. (2011) PLoS Comput Biol 7:e1002129

48


Systems medicine: the future of medical genomics and healthcare

Auffray, C., Chen, Z. and Hood, L.

(2009) Genome Medicine 1:2

Bridging the gap between systems biology and medicine

Clermont, G., Auffray, C. et al.

(2009) Genome Medicine 1:88

Systems medicine and integrated care to combat

chronic noncommunicable diseases

Bousquet, J. et al. (2011) Genome Medicine 3:43

Unbiased Biomarkers for the Predictionof Respiratory Disease Outcomes

Innovative Medicines Initiative:

Understanding Severe Asthma

Coordinator: Peter Sterk

University of Amsterdam

www.ubiopred.eu

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 from preclinical models nor from currently defined patient characteristics

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Bottlenecks in Severe Asthma

pathobiologyclinical

phenotypesclinical

outcome

predictivepreclinical

models

predictive biomarkers

predictive targets for

intervention

disease modification

Hypothesis

Biomarker fingerprints from high-dimensional molecular, physiological, and clinical data integrated

by an innovative systems biology approach into distinct phenotype handprints will enable the

prediction of clinical course and therapeutic efficacy, and identification of novel targets in the treatment of

severe asthma

Capturing Phenotypes

Genes

Cell differentiation

&activation

Organstructure& function

Organismhealth

& disease

Cell-cell interaction

Gene-& post-transcriptional

regulation

Macrophysiology

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Systems Medicine of Severe Asthma

Auffray, C. et al. (2010) Chest 137:1410-1416

Patient reported

Clinical

Functional

Cellular

Molecular


Formalize questions

Sample compartments

Ensure quality

Integrate data

Perturb system

Generate hypotheses

Check model predictions

Refine by iteration

An integrated systems biology approach for understanding pulmonary diseases

Auffray, C., Adcock, I.A., Chung F.K., Djukanovic, R., Pison, C., Sterk, P. Chest 2010;137:1410-1416.


Kaminsky, D.A. Irvin, C.G., Sterk, P.J. (2011) J Appl Physiol 110:1716-1722

51

The Handprint of Severe Asthma

Open Source Knowledge Management Platform

Szalma, S. et al. J Translational Med 2010;8:68

Metabolomics - Breathomics

Fens, N. et al. (2009) Am J Respir Crit Care Med 180:1076-82

52

Training and Validation Sets by eNose:Asthma versus COPD

Fens et al. Clin Exp Allergy 2011:EPub

The U-BIOPRED Consortium

Regulators1

BiopharmaCompanies

9

U-BIOPRED

Patients & Care

Organisations6 SME’s

3

Academia20

MultinationalIndustry

1

1025 subjects including adults and children

Adults Children

Severe asthma

525 100

Mild asthma 100 50

Healthy controls

100

Infantssevererecurrent wheeze

100

Infants mild recurrent wheeze

50

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Study Design

Cross-sectional comparative study

Longitudinal follow-up during 30 months

Iterative preclinical model development

(human ex-vivo, animal in vivo)

Proof of concept intervention by randomized controlled trial

Study Design

screening baseline follow-up 1 follow-up 2

0 3-6 24-30-1

bronchoscopy

tele-monitoring

U-BIOPRED – Main Deliverables

1. Reaching international consensus on diagnostic criteria

2. Creating adult/pediatric cohorts and biobanks

3. Creating novel phenotype ‘handprints’ by combining molecular, histological, clinical and patient-reported data

4. Validating such ‘handprints’ in relation to exacerbations and disease progression

5. Refining the ‘handprints’ by using preclinical and human exacerbation models

6. Predicting efficacy of gold-standard and novel interventions

7. Refining the diagnostic criteria and phenotypes

8. Establishing a platform for exchange, education and dissemination

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Airway Disease PRedicting Outcomes throughPatient Specific Computational Modelling

Airway Disease PRedicting Outcomes through Patient Specific Computational Modelling

Construction of the Diseasome Bipartite Network

OMIM, 1284 diseases, 1777 disease genes

Goh, K.I. et al. (2007) PNAS 104:8685-8690

55

Visualization of the HDN and the DGN

Goh, K.I. et al. (2007) PNAS 104:8685-8690

Airway Models

A, airway model using CT data to fit airways down to branch generations6–9

B and C flow in zones with different alveolar, arterial and venouspressure

Crampin, E.J. et al. (2004) Exp Physiol 89:1-26


Predictive, Preventive, Personalized, Participatory (P4) Medicine

Predictive and preventive vs reactive

Personalized and participatory vs same treatment for all

Multi-parameter blood diagnostic

Drugs targeting network nodes

Escalating cost and attrition rate in drug development

Classification/stratification of diseases and patients

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Systems biology and new technologies enable predictive and preventative medicine

Hood, L., Heath, J.R., Phelps, M.E., Lin, B.

(2004) Science 306:640-643

P4 medicine: the future around the corner

Sobradillo, P., Pozo, F., Agusti, A.

(2011) Arch Bronconeumol 47:35-40

Revolutionizing medicine in the 21st century

through systems approaches

Hood, L., Balling, R., Auffray, C.

(2012) Biotechnol J, in press.

Conclusions

• Systems biology approaches are transforming our understanding of physiology and pathology

• They are currently being implemented to overcome biomedical and pharmaceutical challenges in respiratory diseases

• Initial phenotype handprints enable better respiratory disease identification and patient classification

• Sytems approaches catalyze a move underway from reactive to proactive P4 medicine

Acknowledgments

• Peter Sterk, University of Amsterdam, U-BIOPRED coordinator

• Scott Wagers, BioSci Consulting, U-BIOPRED manager

• Chris Brightling, University of Leicester, AirPROM coordinator

• Christophe Pison, University Hospital of Grenoble & EISBM

• Leroy Hood, Institute for Systems Biology, Seattle

• Zhu Chen, Center for Systems Biomedicine, Shanghai

• Rudi Balling, Center for Systems Biomedicine, Luxembourg

• U-BIOPRED, AirPROM and EISBM colleagues

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WWW.EISBM.ORGInstitut de Recherche

Technologique

www.lyonbiopole.orgwww.biovision.org

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Documents

Postgraduate Course 13 AirPROM: patient specific modelling ...Postgraduate Course 13 AirPROM: patient-specific modelling and systems biology living labs workshop Saturday, 1 September