the casym roadmaphealthcare costs continue to rise rapidly. the increas-ing burden of providing...

THE CASyM ROADMAP

Implementation of Systems Medicine across Europe

Version 2.0

The CASyM Roadmap │ Implementation of Systems Medicine across Europe

IMPRINT

This document was produced by the CASyM consortium Acimovic Jure – University of Ljubljana, Slovenia | Auffray Charles – EISBM, France/EASyM Germany | Ballestrero Alberto – University of Genoa, Italy | Balling Rudi, Becker Regina – University of Luxembourg | Ben-Yehuda Ahmi – Ministry of Health, Israel | Bendtsen Claus – AstraZeneca, UK | Benson Mikael – Linköping University, Sweden | Boeter Anne – ZonMw, The Netherlands | Byrne Helen – Oxford University, UK | Diemel Rob – ZonMw, The Netherlands | Fitzmaurice William – University College Dublin, Ireland | Giugliano Raffaella – University of Genoa, Italy | Glod Frank – Fonds Nationale de la Recherche, Luxembourg | Harrison David – University of St Andrews, Scotland, UK | Hendlich Manfred – Sanofi Aventis, Germany | Israeli Avi – Ministry of Health, Israel | Kirschner Marc – Forschungszentrum Jülich GmbH, Germany | Klabunde Thomas – Sanofi Aventis, Germany | Kolch Walter – University College Dublin, Ireland | Lévi Francis– Warwick University, United Kingdom | Miczka Gisela – For-schungszentrum Jülich GmbH, Germany | Maria M. Nogueira – EISBM, France | Ostrowicz Clemens – University of Luxembourg | Mohr Johannes – Federal Ministry of Education and Research, Germany | Parodi Silvio – Univer-sity of Genoa, Italy | Patrone Franco – University of Genoa, Italy | Rozman Damjana – University of Ljubljana, Slovenia | Schuchhardt Johannes – Microdiscovery, Germany | Schuppert Andreas – Bayer Technology Services, Germany | Tegnér Jesper – Karolinska Institute, Sweden | Thiele Ines – University of Iceland | Vidal Elodie – Lyonbiopole, France | Wolkenhauer Olaf – University of Rostock, Germany Reviewed by The EASyM Executive Board: Auffray Charles, EISBM, France | Cesario Alfredo, Università Cattolica del Sacro Cuore, Italy | Radstake Timothy, University of Utrecht, Netherlands | Rozman Damjana, University of Ljubljana, Slovenia | Schmeck Bernd, University of Marburg, Germany | Supple David, Asthma UK | Villoslada Pablo, Insti-tuto de Salud Carlos III, Barcelona, Spain Date April 2017 Contact information Walter Kolch │ William Fitzmaurice Systems Biology Ireland & Conway Institute, University College Dublin, Ireland [email protected] │ [email protected] Links to external websites The CASyM roadmap document may contain links to external third-party websites. These links to third-party sites do not imply approval of their contents. Project Management Jülich has no influence on the current or future contents of these sites. We therefore accept no liability for the accessibility or contents of such websites and no liability for damages that may arise as a result of the use of such content.

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CONTENTS

1 IMPRINT ........................................................................................................................................................................................................... 2 CONTENTS ...................................................................................................................................................................................................... 4 EXECUTIVE SUMMARY ............................................................................................................................................................................... 5

A TOOLBOX TO DELIVER PERSONALIZED CARE ........................................................................................................................................... 5 INTRODUCTION ........................................................................................................................................................................................... 7

WHY SYTEMS MEDICINE AND WHY NOW? ................................................................................................................................................ 7 A SYSTEMS MEDICINE TOOLBOX FOR CLINICIANS ...................................................................................................................................... 8 HARNESSING COMPUTATIONAL, STATISTICAL AND MATHEMATICAL TOOLS TO ANALYSE AND INTERPRET BIOMEDICAL DATA ........... 9

THE CASYM ROAD MAP FOR THE IMPLEMENTATION OF SYSTEMS MEDICINE ACROSS EUROPE ........................... 10 COORDINATING ACTION SYSTEMS MEDICINE......................................................................................................................................... 10 IMPLEMENTATION ....................................................................................................................................................................................... 11 1. IMPROVING THE DESIGN OF CLINICAL TRIALS ...................................................................................................................................... 12 2. PATIENT STRATIFICATION FOR A MORE PERSONALIZED MEDICINE ..................................................................................................... 12 3. WORKING WITH INDUSTRY ................................................................................................................................................................... 13 4. DATA GENERATION ................................................................................................................................................................................ 14 5. METHODOLOGY AND TECHNOLOGY DEVELOPMENT INCLUDING MODELLING .................................................................................. 15 6. TECHNOLOGICAL INFRASTRUCTURE ...................................................................................................................................................... 16 7. ETHICAL AND REGULATORY ISSUES ....................................................................................................................................................... 17 8. MULTIDISCIPLINARY TRAINING IN SYSTEMS APPROACHES.................................................................................................................. 18 9. COMMUNITY BUILDING, NETWORKING AND DISSEMINATING THE SYSTEMS MEDICINE CONCEPT ................................................. 19 10. INTEGRATION OF EU AND NATIONAL EFFORTS IN SYSTEMS MEDICINE ......................................................................................... 20

PROOF OF CONCEPT ................................................................................................................................................................................ 22 SYSTEMS MEDICINE IN PRACTICE .............................................................................................................................................................. 22 EXAMPLE1: OXYUC — THE IMPACT OF HYPOXIA ON INFLAMMATION AND TUMORIGENESIS IN ULCERATIVE COLITIS ................... 23 EXAMPLE 2: SYSPHARMPEDIA — SYSTEMS PHARMACOLOGY APPROACH TO DIFFICULT-TO-TREAT PEDIATRIC ASTHMA................ 23 EXAMPLE 3: SYS4MS — PERSONALIZING HEALTH CARE IN MULTIPLE SCLEROSIS USING SYSTEMS MEDICINE TOOLS .................... 24

FURTHER READING ................................................................................................................................................................................... 25 CASYM LEGACY ......................................................................................................................................................................................... 25 REPORTS FROM CASYM CONSULTATION AND WORKSHOP ACTIVITIES ................................................................................................ 25 SELECTED COMPLEMENTARY INITIATIVES .................................................................................................................................................. 26 SYSTEMS MEDICINE STATE OF THE ART .................................................................................................................................................... 26

REFERENCES ................................................................................................................................................................................................. 30 ACKNOWLEDGEMENTS .......................................................................................................................................................................... 31

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EXECUTIVE SUMMARY

A toolbox to deliver personalized care

By David Harrison

Healthcare costs continue to rise rapidly. The increas-ing burden of providing appropriate care for the el-derly and the deployment of new expensive targeted drugs and biological therapies, more intensive and prolonged investigation of patients, many of whom have comorbidities, is one of the major societal chal-lenges facing Europe in the 21st century. With the explosion in genomics being applied in medicine the challenge is not so much what we know (the data we collect), but rather how we deploy the knowledge to effect change in a meaningful and cost effective man-ner (the information that we can use). The ability to mine data of all types offers the real hope of making as big a change to medicine and wellbeing as was achieved by the recognition of infection and the need for antibiotics.

This revolution, truly it is nothing less, has evolved organically; this is not a future aspiration. Under the banner of genomic medicine, precision medicine, personalized medicine or any number of other labels, this revolution has already made huge advances in many areas. But in order to harness it efficiently, and to ensure that the new model that develops is resili-ent, guidance and consensus are required for gov-ernments, health funders, patients, researchers, clini-

cians, educators, and scientists alike. Thus our inten-tion is not to constrain ideas and research, nor even to suggest one methodology over another, but rather to portray a conceptual approach to ensure that les-sons learned in one setting, be that different geo-graphical or disease areas, can be rapidly assimilated and used elsewhere, bringing maximum benefits in the shortest period of time.

We present a Road Map for Systems Medicine as part of the toolbox which is already bringing significant benefit to the daily lives of patients. The Road Map showcases the approach that takes big data, from laboratory through imaging to population studies, and synthesises predictive models of disease and wellbeing. As they are refined the models provide guidance not just how to use data, but also what experiments still need to be conducted and, crucially, what investigations and data are no longer required but have been superseded. This last point is important if the new age of data-driven medicine is ever to be affordable in practice.

As with any toolbox, the intention is to provide what is need to solve a particular problem; sometimes this may be very focussed and limited, but on other occa-sions the challenges addressed will be otherwise

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overwhelming in scale and complexity. However, the most effective impact of the Road Map for Systems Medicine is to change our mind set and the workflow of biomedical research and medical care. The vision of the Road Map, developed by a uniquely multidiscipli-nary Europe-wide network of clinicians, scientists, mathematicians, patient representatives, industry and others, is to provide the toolbox of Systems Medicine as a practical framework that assists clinical decision making and the design of precise, personalized, pre-vention and treatment plans. The Road Map for Sys-tems Medicine is not prescriptive how research should be conducted, but it provides tools that ultimately will help to manage a person’s health rather than just a patient’s disease.

Systems Medicine employs a ground-breaking inte-grated approach which holds huge potential for holis-tic understanding of human health and disease. Build-ing on advances in systems biology, this approach fuses the disciplines which currently found medicine (biology, genomics, physiology, imaging, pathology, pharmacology and therapeutics) together with infor-matics, computer science, mathematics, statistics, physics, and engineering. The power of this fusion is exploited through the development of complex bio-logical models which can enable researchers to map the (mal)functioning of the human body, its processes and interactions across multiple levels of structural and functional organisation - from molecular reac-tions, to cell-cell interactions in tissues, to the physi-ology of organs and organ systems. These approach-es have laid the foundation for the practice of preci-sion medicine, described as intervention in health that is predictive, preventive, personalized, and participa-tory in nature. Coordinating these research and im-plementation efforts, by ensuring consistency of ap-proach and quality assurance, will allow more rapid and cost effective dissemination of new advances, and lead to economic benefit and social impacts through

the targeted application of therapeutics and preven-tative measures.

The Road Map, the result of a broad cross-disciplinary stakeholder consultation process, identifies ten key areas necessary to the successful implementation of Systems Medicine in Europe.

These areas, including patient stratification into groups with similar characteristics, novel design of clinical trials, and means to access clinical data, -sharing and –standardization, partnership with indus-try, development of the necessary technical infrastruc-ture and the provision of multidisciplinary training, are outlined along with cross-cutting priority actions (community building, proof of concept/pilot study, cross-disciplinary training and data access, sharing and standardisation).

The Road Map asserts that continued investment in high quality, proof of concept/pilot studies will hasten a paradigm shift in the way personalized medicine is practiced. This shift will be supported by a strong Systems Medicine community, new multidisciplinary training programmes and the development of new European-wide practices in clinical data access, shar-ing and standardisation.

By building on current national and international efforts using the toolbox of Systems Medicine to deliver personalized care, and the coalescing of the many stakeholder groups, this coordinated action will bring even more significant and sustained benefits to the European citizen, both in sickness and in health.

David Harrison Chair of the CASyM Steering Committee and Professor of Pathology University of St Andrews, St Andrews, Scotland [email protected]

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INTRODUCTION

Why Systems Medicine and why now?

To manage the complexity of the human body and the diseases it develops, the biological and medical sci-ences have tended to employ a reductionist approach, breaking down complex problems into smaller, simpler and more tractable units. This approach has brought significant improvements to health in the last 150 years, with life expectancies steadily rising as a result of the development of our understanding of infectious and parasitic diseases [figure 1; (Oeppen and Vaupel, 2002),(Christensen et al., 2009)].

The consequent public health projects of the twentieth century have had a significant impact on child and infant mortality rates, immunizing millions against including smallpox, polio, and major childhood killers like measles.

However, as the global health burden has shifted to non-communicable chronic diseases such as cancer and heart disease that predominantly affect ageing populations, new challenges have arisen. These dis-eases are multifactorial in nature with a complex range of environmental, lifestyle and genetic risk factors that challenge the simplifications of reductionism, while their economic impact has seen global health expendi-ture per capita double in the last twenty years [figure 2; (WHO Press, 2017)].

Figure 1:Female Life Expectancy in Developed Countries: 1840-2009 (Oeppen 2002, Christensen 2009)

Figure 2: Health expenditure per capita (US$; World Health Organization Global Health Expenditure database)

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A new approach is required, in which human disease is viewed as perturbations of complex and integrated

genetic, molecular and cellular networks, an approach which offers precise and personalized interventions. Recent developments in the life sciences have seen the creation of the new field of systems biology in which the broader biological ‘system’ is examined in a more holistic way. By using mathematical modelling and high-throughput tools, more complex aspects of biol-ogy can be studied. This not only includes the inter-pretation and integration of large-scale data (such as genomic, transcriptomic, proteomic and other ‘-omics’ data) but also for instance the study of complex fea-tures in intracellular communication networks, which often exhibit behaviour which cannot be explained with verbal reasoning alone.

Using these approaches, concepts from a wide variety of disciplines including mathematics, physics and engineering have been applied to many biological systems and are having an increasingly translational impact on the medical sciences. As the costs of these novel approaches and technologies continue to fall, with the price of sequencing an average human ge-nome plummeting from US$100 million at the start of the millennium to a thousand dollars in little more than fifteen years [figure 3, (NIH, 2016)], there is now a real opportunity for their wholesale adoption in ad-dressing new challenges in human health.

There is a need for flexible, integrative systems ap-proaches that combine ‘-omics’ data with clinical, societal and environmental factors including sex, type of work, sleep and eating habits. The integration of these data could have a transformative effect on med-ical practice by enabling the management of a per-son's health, rather than management of their disease. By applying new computational and mathematical tools to medical practice, we can move from a largely reactive mode of medicine to one that is more focused on wellness and is more cost-effective (Hood et al., 2012), (Auffray et al., 2016), (Duffy, 2016). This is no small challenge, and there are considerable barriers that must be overcome, from integrating new meth-odologies with established medical working practices t to developing the necessary technical infrastructure.

A Systems Medicine toolbox for clini-cians

Systems Medicine is a natural extension of clinical decision-making which is built on pattern recognition and reasoning about causality. Systems Medicine aims to provide medical doctors with methods to integrate multiple layers of data in formats that allow under-standing of disease mechanisms as well as diagnostic prediction. A hallmark of Systems Medicine is the use of computational and mathematical modelling to incorporate the ever-increasing volume and complexi-ty of data, such as genome sequences, protein and metabolite profiles, as well as histopathological and physiological data into decision-making. Failure to tap this rich seam of information denies medicine access to a wealth of patient and disease-relevant infor-mation.

Systems Medicine offers a systematic and tractable approach to complex clinical challenges that is repro-ducible, scalable and evolvable. Systems Medicine will provide new tools that will support and extend the capabilities of the clinician to diagnose and treat pa-tients faster, better and more effectively. It is a new conceptual approach which has the potential to signif-icantly improve patient outcomes. In the medium and long term, the success of Systems Medicine will be

Figure 3: Genome sequencing costs (US$; National Human Genome Research Institute, 2017)

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measured by new biomarkers, new devices (strongly facilitating doctor/patient interaction), novel drugs and patient management strategies. Thus, Systems Medi-cine has the potential to make a significant impact on clinical practice and patient outcomes.

Systems Medicine is a way of thinking, a conceptual and operational framework for incorporating ground-breaking new technologies into medicine, rather than a theoretical discipline. It aims to affect a measurable improvement in patient health based on a deep quan-titative and mechanistic understanding of the human as a complex biological system.

Harnessing computational, statistical and mathematical tools to analyse and interpret biomedical data

A defining feature of systems medicine is the use of mathematical and computational tools to analyse the mechanisms and properties of a biological system with the aim to understand its past and predict its future behaviour. A variety of mathematical, statistical and computational tools are available. Many were devel-oped in systems biology for the analysis of simpler organisms and continue to be developed as we apply them to more complex and heterogeneous data sets.

Common diseases involve altered interactions be-tween thousands of gene products, metabolites and environmental factors. High-throughput, ‘-omics’ technologies allow simultaneous analyses of genes, transcripts, proteins, and metabolites at increasingly exhaustive levels while new advances in imaging have enabled the collection of structural and anatomical data at various scales. With this deluge of data, inter-pretation becomes a significant challenge. In particu-lar, the integration of different datasets and the cou-pling of molecular changes to (patho)physiological features and clinical phenotypes is now a main bottle-neck on the road to precise and personalized preven-tion, diagnosis and treatment of disease. Thus, in the future close interaction between the scientists who develop the next generation of analytical tools and the

clinicians who apply them must be proactively pro-moted and maintained in order to successfully imple-ment systems medicine and exploit the benefits that it offers.

Theoretical methods are best viewed as analytical tools that help to organize, explain and interpret data. As with any analytical tool, they will only work on data of adequate quality, quantity, density and structure. Biological and clinical data are notoriously plagued by heterogeneity and noise, which hamper the applica-tion of theoretical tools that were often conceived to work on perfect and complete sets of data. Similarly, methods have certain limitations or biases, which need to be carefully considered and tailored to the data and question at hand. There is continuous progress in methods development, and a future emphasis needs to be on optimizing the match between method, data and aim of the analysis. It is important to point out that by their very nature computational models are abstractions that make highly complex relationships intelligible by reducing the complexity to the essential features that can address the aim of the analysis. A corollary of this abstraction is that the generalizability of computational models is highly dependent on the aim and purpose for which the model was developed (Wolkenhauer, 2014; Wolkenhauer et al., 2014).

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THE CASyM ROAD MAP FOR THE IMPLEMENTATION OF SYSTEMS MEDICINE ACROSS EUROPE

Coordinating Action Systems Medicine

The European Union Seventh Framework Programme (FP7) Coordinating Action Systems Medicine (CASyM) consortium was charged by the European Commission to prepare a consensus-building strategic road map to outline the implementation of Systems Medicine across Europe. From 2012 until 2017 the multidiscipli-nary consortium CASyM with representatives from academia, research institutes, clinical centres as well as funding bodies, Industry and SMEs joined forces to build a vision and a practical strategy (roadmap) for the implementation of Systems Medicine across Eu-rope. During this time CASyM functioned as manag-ing, coordination and support platform that brought together a critical mass of relevant stakeholders to develop a strong European Systems Medicine com-munity and to work towards a long-term and sus-tained implementation of Systems Medicine across Europe – with the aim to bring Systems Medicine into everyday clinical research and practice for the benefit of public health in the near future.

A first version of the road map was published in 2014; this 2017 revision and update of the road map aims to build on the 2014 publication by integrating the pro-gress that has been made in the intervening years.

The road map reflects the vision of the CASyM stake-holders summarizes the outputs of the programme’s activities over the past five years and points a way forward for the wider adoption of systems approaches to medicine. The aims of the road map are to:

> explain the rationale for Systems Medicine > identify the challenges and opportunities for Systems

Medicine > identify the stakeholders and initiate a dialogue between

them > provide a vision for the implementation of Systems Med-

icine > provide examples of its application > formulate reasonable and achievable goals > provide practical recommendations for the implementa-

tion of Systems Medicine in Europe

A vital feature of CASyM was a broad and coordinated consultation process of all European stakeholders, to network relevant communities, highlight successful projects and initiatives, disseminate relevant infor-mation and closely bring together those that are es-sential for the development of a sustained and far reaching Systems Medicine approach.

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Implementation

This section provides a summary of the main chal-lenges and opportunities for the implementation of Systems Medicine in Europe. It is based on information collected from multiple stakeholder events, workshops and other activities that have taken place over the duration of the CASyM programme.

Ten key areas of consideration for the successful im-plementation of Systems Medicine in Europe were outlined in the 2014 edition of the CASyM road map; a series of further CASyM events in the intervening peri-od have enabled further refinement of our initial rec-ommendations while a series of outputs from within the project and more broadly in systems science have begun to address several of the key recommendations (see Further Reading: CASyM initiatives & Reports from CASyM workshops and activities for details on project outputs).

Cutting across these ten key areas are the requirement for a paradigm shift in the way healthcare is operated

in Europe and worldwide, the building of a strong community and outreach programmes, the develop-ment of proof of concept (PoC) success stories in Systems Medicine to improve understanding and show the utility of the approach; training and educa-tion programmes to embed systems approaches; and a Europe-wide consensus on issues relating to data access, sharing and standardization.

Figure 4: Key areas for a successful implementation of Systems Medicine across Europe.

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1. Improving the design of clinical trials

Systems Medicine shows substantial potential for disruptive changes in clinical research and practice, with concomitant benefits for both clinicians and pa-tients. The challenges, however, are significant, with the need for increasing adaptability in both the design of clinical trials and clinical practice in order to effec-tively harness these novel tools and approaches.

Recommendations

R1: Shift towards a patient-oriented approach, integrating genetics and lifestyle dimensions.

R2: Target pathways instead of single molecules. R3: Examine potential for innovative clinical trials, including

in silico trials, one patient trials, and adaptive trial de-signs.

R4: Further research on co-morbidities and drug interac-tions.

R5: Development of new disease taxonomies based on molecular and mechanistic features.

R6: Access to high-quality, open access datasets to facilitate refinements in the identification of biomarkers, outcome measures and drug targets.

R7: Refinement of existing modelling approaches, in par-ticular for the integration and analysis of heterogeneous datasets.

R8: Development of practical, interoperable open-source computational tools with user-friendly interfaces facili-tating broad usability by translational researchers and clinicians.

2. Patient stratification for a more per-sonalized medicine

A key medical problem is that an estimated 85 % of therapies fail in early clinical trials because of lack of efficacy or sufficient safety (Ledford, 2011). In the US, this corresponds to an annual cost of 350 billion dol-lars of ineffective drug prescriptions (Nature-Biotechnology-editorial, 2012). There is therefore a clear need for stratification of patients for a variety of diseases. Better patient stratification is considered a tractable area that Systems Medicine could drive. With the complexity of multifactorial diseases such as can-cer, advances in ‘-omics’ and imaging technologies can be key to improving patient stratification, from the preclinical phase to post-market analysis and drug repurposing. In addition, re-stratification strategies should also take environmental as well as lifestyle parameters into consideration.

Systems medicine principles can be used to personal-ize medication by identifying the specific combina-tions of disease-associated genes associated with an individual patient. Successes in recent European Commission (EC) funded projects show that this is an important opportunity for Systems Medicine to make a significant impact on health outcomes for patients.

Successful patient stratification has important implica-tions for future research: new drugs may be developed for patients who do not respond. It is also possible that gene variants or other genomic layers in the modules may be used to predict and prevent diseases.

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The following steps towards patient stratification are seen as essential:

1. Modules of highly interconnected disease-associated genes, proteins and metabolites are identi-fied by -omics methods and computational analysis.

2. Computational modelling in order to understand and predict the molecular processes and pathways (modules) that characterize the disease processes.

3. Validation of the computational models and asso-ciated experimental data in independent materials from clinical settings including responders and non-responders to treatment.

4. Development of clinical studies based on the re-sults of validated computational models. Modules of genes or proteins are tried in prospective clinical stud-ies to individualize medication. It is possible that such studies will include more than one sample from the same patients in order to stratify patients based on dynamic changes in disease processes.

Steps 1 and 2 may at least in part be performed on the very large ‘-omics’ resources available in the public domain, so that investigators can directly proceed to stage 3. Therefore systematic large-scale analyses of public ‘-omics’ data from multiple diseases followed by validation and clinical studies (steps 3-4) will be an important research priority to demonstrate the feasi-bility of Systems Medicine.

Recommendations

R9: Develop a standardized and centralized ‘-omics’ data-base describing functional disease-relevant modules in different diseases, as well as variations in such modules.

R10: Develop image databases relating molecular profiles to histological phenotypes, and images on micro-architecture to images with non-invasive imaging mo-dalities.

R11: Develop a compound database linking compounds to modules, and module variants in order to enable the in-dividualization of medication).

R12: Develop modelling tools to individualize medicine for static and dynamic clinical use, including N=1 ap-proaches.

R13: Implement verification and validation strategies for databases, models and modelling software (for example open-source policy and/or certification).

R14: Systematic analyses to repurpose drugs that have not reached the clinic due to late clinical failure, in stratified subgroup populations based on systematic large-scale analyses of public ‘-omics’ and physiology data from multiple diseases followed by clinical studies.

3. Working with industry

The road map for Systems Medicine aims to acceler-ate innovation in translational activities and to inspire scientists, management and investors with best prac-tice in including rationally based cost-effective drug and technology development in their industry and in creating new industries based on a personalized, pre-ventive, predictive and participatory Systems Medicine approach.

Within the broad category of ‘industry’ are a wide range of experiences and utilizations of Systems Medi-cine. These range from ‘big pharma’ companies at-tempting to yield more insights into the increasing volume of data they are producing, to technology-driven personal diagnostics operations who require modelling and simulation tools, and small and medi-um enterprises positioned in niche markets who are often leading the way in the utilization of Systems Medicine approaches. Interviews with representatives from across this spectrum indicated four major areas of industrial interest regarding Systems Medicine:

1. Elucidating mechanisms from disease data: Many companies in the field of modelling, technology de-velopment and drug development are already apply-ing systems approaches. Systems Medicine is consid-ered a requirement for a better understanding of disease mechanisms, treatment portfolio optimization, and for identifying new drug targets and highly strati-fied patient subsets.

2. Collaboration: Public-private partnerships (PPP) are highly appreciated and considered crucial, since they

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combine different areas and traditions in science and health care. PPP should always concentrate on the need of the patient. In addition, a clear focus on the gaps and technological challenges of the future (not of those of today) should be gained through open discussion.

3. Proving the Concept: Lack of robust and wide-spread PoC studies restrains industry from making large-scale investments in Systems Medicine ap-proaches. This is beginning to be addressed through the initiation of the ERACoSysMed programme (see Proof of Concept). Industry would benefit from an online PoC portfolio and a ‘global standard’ of quality guidelines for in silico approaches.

4. Getting access to data: Improved mechanisms for access to and use of patient data are needed for re-search purposes while ensuring the privacy of the patient. Computational models can only be built and used accurately when a sufficient amount of high quality data is available, and a data management in-frastructure has been implemented.

Attention should focus on specific disease subsets represented by homogenous and well-characterized patient subpopulations.

Recommendations

R15: Develop funding programmes specific for disruptive industrial innovation: national governments and EC facil-itate disruptive innovation by means of specific funding schemes allowing for cooperation of scientists from ac-ademia, industry and hospitals.

R16: Create an international information forum for small and medium enterprises to help non-experts discriminate between the various systems tools and applications, re-ducing the market's opacity and facilitating adoption.

R17: Create a PoC platform based on the application of securitized financings to enable small and medium en-terprises to generate intellectual property. This requires the creation of a legal framework, an ecosystem of certi-fied contract research organizations and private and public investors.

R18: Funding for development and implementation of model and software standards, software testing schemes, and certification standards in systems medicine, as well as schemes for software maintenance for complex commu-nity tools developed in academia for later application in industry.

R19: Establish method to evaluate in silico simulation results and associated quality standards for agencies (regula-tors and payers).

4. Data generation

Systems Medicine is an approach driven by clinical research questions, integrating multiple and diverse data sets in quantitative computational models. Re-searchers utilize both existing and specifically generat-ed data and must overcome challenges in terms of data quality, harmonization and accessibility. We pro-pose a Systems Medicine data methodology based on:

1. A systems method: This methodology aims to connect patient level outcomes and conclusions with an underlying mechanistic area of biology using com-putational systems modelling approaches. The data to be generated are determined by the aim and purpose of the study and the computational methods to be used

2. A clinically-relevant research question: In order to define realistic research hypotheses, secondary use of existing data from Registries, clinical studies and Elec-tronic Health Record sources could and should be used to define specific research questions and isolate populations of interest

3. Suitable data generation: With a specific popula-tion selected, focused experiments could be carried out by using data intensive methods of: Patient Re-ported Outcomes, biopsies, ex-vivo biology, bio-sensing to capture the rich biological differences be-tween subjects in a way that is suitable for the model-ling approaches

4. Enriching existing cohorts: Good quality cohorts, with intense phenotyping based on rigorous SOPs,

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could be enriched with more -omics data, thus build-ing on existing work and investment

5. Model-based predictions: Modelling predictions about both the biological systems and how they trans-late into humans can then be deployed based on the rich data collected

6. Proper validation: The results of the modelling and analysis then need to be validated by using existing independent datasets or by targeted experiments in relevant clinical populations. The field of verification, validation and uncertainty quantification (VVUQ) in engineering and physical sciences is a promising de-velopment for rigorously evaluating the credibility of computational model predictions, with recent efforts to apply this in biomedicine (Pathmanathan 2013).

It is the hope that this type of methodology that com-prises both secondary use of real world evidence data and focused rich biological data from small but tar-geted populations would enable research programmes that are innovative and insightful but also cost effec-tive.

Recommendations

R20: Studies on patient and clinical data handling, with focus on standardization, harmonization and sharing.

R21: Collect and analyse data on: (Non-) responder patients; healthy people; therapy schemes (including drug admin-istration schedules); side effects of drugs/genotypes; failed clinical trials; gaps in data for Systems Medicine use.

R22: Develop SOPs and quality standards for the systematic collection of quantitative data.

R23: Develop standards for identifying good quality cohorts suitable for enriching with further -omics data.

R24: Develop policy on open access to data from patients and healthy people.

R25: Implement new guidelines at European level following on from patient data studies

.

5. Methodology and technology devel-opment including modelling

Over the last decade, we have gained detailed insights into the structure and function of molecular, cellular and organ-level systems, with technologies playing an important role in the generation of data at these dif-ferent scales. A central theme for Systems Medicine is the integration of this knowledge across the relevant levels of organization. Key challenges that emerge in Systems Medicine are:

1. How to integrate experimental data from a wide range of technologies and sources?

2. How to relate and integrate different modelling formalisms?

3. How to reliably compare, verify and validate differ-ent models?

4. How to translate computational implementations into clinically useful outputs?

These challenges can be addressed through multiscale modelling in which multiple spatial and temporal scales are spanned, enabling us to understand and predict processes involving molecules, cells, tissues and organs. This requires the development of compu-tational models that can integrate data and knowledge from the clinics and basic science (in vitro and animal model experiments), that are applicable to individual patients, and that will deliver a mechanistic under-standing of pathologies. The concomitant develop-ment of concepts, methods and tools that support the integration of data across organizational levels and

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that can readily interface between different computa-tional and mathematical approaches is also required.

Data analysis and mathematical modelling: A major hurdle for the development of quantitative and pre-dictive models that span the gap between cell-level biochemical models and organism-level pharmacoki-netic/pharmacodynamic (PK/PD) models is that tech-nical difficulties and often considerable costs are asso-ciated withgenerating sufficiently comprehensive quantitativedatasets for large numbers of system variables, acrossdifferent levels of organization. For the modellers, anexciting challenge is the develop-ment of models that canbe used in clinical practice while accounting for theuncertainty and noise in the data. Rapid advances in measurement technologies (e.g. next generation sequencing) and the ongoingdeluge of data could lead to an overemphasison data management issues. Priority areas, towards which data analysis and modelling can contribute in Systems Medicine are:

1. Predicting drug/treatment responses for patients

2. Selection of most efficacious drug combinations

3. Reduction of side effects

4. Monitoring of treatment efficacies

5. Understanding of multivariate and multilevel dis-ease mechanisms

6. Understanding and early detection of disease risks

7. Preventing disease based on predictive personal-ized models

8. Integrating levels of functional and structural or-ganization

Recommendations

R26: Exploitation of existing data as a starting point for mul-tiscale modelling, leading to the identification of gaps in data and in understanding of underlying mechanisms in order to improve the targeted generation of new data that can be exploited for predictive computational mo-dels.

R27: Define test scenarios and PoC studies. R28: Identify required standards and ontologies (e.g. a mark-

up-language and ontology for individual-based models) for models and data repositories in Systems Medicine.

R29: Develop concepts for dedicated modelling workflows for the integration of data and models.

R30: Develop computational tools and algorithms for effi-cient multiscale simulations including parameter estima-tion, sensitivity analysis, identifiability analysis, data in-tegration, and model comparisons.

R31: Develop and roll out strategic modelling workshops on multiscale modelling’s role in Systems Medicine

R32: Support the development of workflows for modelling, including computational tools that support data man-agement, model construction and analysis.

R33: Create biomarker database(s). R34: Enhance the formation of small-scale networks focused

on specific clinical needs, possibly clustered in a larger integrated project (longer time scale).

R35: Put in place a funding model for small groups of 2-3 partners (1-2 modelling postdocs, 1-2 experimental postdocs + consumables, travel between labs).

R36: Use computational models to develop targeted thera-pies for given phenotypes.

R37: Generating incentives for model and modeling software, ideally source code, sharing.

R38: Funding development and implementation of model and software standards, software testing schemes, and certification standards in systems medicine, as well as schemes for software maintenance for complex com-munity tools developed in academia for later application in industry.

6. Technological infrastructure

Systems Medicine must exploit knowledge from dif-ferent disciplines, collaboratively advancing new in-sights into disease mechanisms, improving diagnostics and advancing therapeutic options through a process of collaborative understanding and effective commu-nication. This requires a technological infrastructure that supports efficient collection, integration and dis-

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tribution of different levels of information, which can be tailored to the needs of the users.

An important basis is the development of an agree-ment on data handling, storage and sharing and means to access the quality of knowledge utilized for the formulation of mathematical and computational models which can help to converge towards modular systems and data sources that reliably interact and form an interoperable, sustainable and usable system for clinical and non-clinical data, computational mod-els and condensed knowledge (Wilkinson et al., 2016);(Stanford et al., 2015). Key priorities are:

1. Establishing standards: The definition of parame-ters that have to be documented for each data point greatly facilitates assessment of the quality of data. Additionally, standardized documentation of compu-tational models is required. The standardization pro-cess must be scalable and permissive for the inclusion of semi-quantitative and qualitative data, such as clinical data, lifestyle data, nutrition, etc.

2. Improved knowledge gathering: A comprehensive collection of available disease models, both in experi-mental and computational terms, provides an im-portant basis for Systems Medicine. To improve the process of knowledge acquisition, one should define the necessary metadata that would complement this collection and define unambiguously the conditions of its acquisition. Furthermore, the collaborative design of evidence grids to evaluate knowledge extracted from the scientific literature greatly facilitates the process to provide a core data services and manage-ment and will help to provide a reliable archive as well as active support.

3. Unified solutions for data storage and exchange: The development towards a normalized solution for data storage and exchange with sufficient flexibility requires pluggable infrastructures and a knowledge-based representation of literature, models, data and standards. It will greatly promote sharing of data and sustainability of public efforts. Structured, collabora-tive knowledge, data and model access will replace traditional literature and will greatly accelerate ad-vances in medical science.

4. Open source models: funding sharing and sharing platforms of models and their software implementa-tion is required, together with schemes for software maintenance will promote sustainability and re-use of established models and tools.

Recommendations

R39: Existing solutions for standards and solutions should be collected and utilized; review providing overview and evaluation of prior work.

R40: Develop interoperable standards and software, together with relevant stakeholders and initiatives such as COR-BEL, EATRIS, ELIXIR, FAIRDOM and ISBE (see Further Reading).

R41: Develop incentives to adhere to newly develop stand-ards to increase compliance.

R42: Develop resources that enable and promote the sharing of data sets and software.

R43: Develop centralized or decentralized schemes and platforms to ensure software maintenance, extensibility, and usability as a high level professional service for de-velopers in research and clinics.

7. Ethical and regulatory issues

A major challenge in bringing Systems Medicine-driven personalized medicine to fruition is dealing with the impact of this approach on society. Here ethical, social, legal, regulatory, and economic issues play a crucial role:

1. Steps towards P4 (prediction, prevention, personal-ization, participation): Systems Medicine and the digi-tal revolution are together transforming healthcare to a proactive personalized medicine

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2. Patient participation: Citizens, both in health and in sickness, will be a major driver of their own healthcare; to realize this, significant efforts in improving health literacy and education will be required to ensure ad-vances benefit all, rather than entrenching inequalities

3. Patient privacy: Researchers rely on extensive data of patients and need to be able to share and process data across borders. A robust pan-European legal framework is required to protect the interests of the public, their data and use thereof; the implementation of the new EU General Data Protection Regulation needs to be conducted with expert advice to allow Systems Medicine approaches to develop; public edu-cation programmes to improve understanding of privacy issues and to foster trust

4. Health insurance: With advances in the ability of medicine to predict illness and disease will come chal-lenges to current paradigms of health insurance; a framework for health insurance and insurers will be required to ensure healthcare for all.

Recommendations

R44: Map European wide relevant ethical/regulatory/patient related issues in order to facilitate a harmonized and Systems Medicine friendly implementation of the new EU General Data Protection Regulation.

R45: Research into the inequality and literacy of patients & operators correlated with highly technological systems approaches.

R46: Health Technology Assessment to be evaluated as a tool for accountability and sustainability matters.

R47: Undertake lobbying to influence regulators who will build the appropriate legal framework to ensure that Systems Medicine approaches are enabled.

8. Multidisciplinary training in systems approaches

Systems Medicine training can become a unifying strand for medical education, creating a coherent link between the preclinical disciplines (chemistry, bio-chemistry, cellular and molecular biology, histology and anatomy) whilst incorporating systems-specific learning in networks, statistics, data-handling and modelling.

Major challenges include the training of current re-search MDs and clinical practitioners involved in the diagnosis and treatment of diseases who will require greater familiarity with genomics, data integration, bioinformatics, and ‘-omics’ technologies if they are to incorporate them into their work. As such, Continuing Medical Education (CME) will be central, with educa-tional information and training programmes for all career stages incorporating the latest modes of web-based learning.

Building on current international exemplar training, a variety of modes of learning should be considered, facilitating i) modular, ii) integrated, iii) study-at-own-pace, and iv) cost-effective training, to be combined with and improve on current training structures (Voit et al., 2012). These should be both, international and inclusive, applicable to a variety of healthcare workers from clinicians to paramedics and nurses as well as other professionals who will work across the spectrum of Systems Medicine-related roles.

In tandem, a focus is required on educating the com-puter scientists, mathematicians, engineers and other scientists from disciplines who contribute to Systems

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Medicine in clinical matters to ensure working practic-es are harmonized.

A range of activities initiated by CASyM have sought to address these aims, with an assessment of the cur-rent provision of postgraduate systems medicine courses in Europe revealing a wide variety of ap-proaches and emphases. Building on the recommen-dation of the 2014 CASyM Road Map, a series of ac-credited systems medicine training workshops have been organized by CASyM partners in six countries across Europe.

Recommendations

R48: Build an outreach programme and education tool kits for all stakeholder groups.

R49: Develop a European training agenda, including Systems Medicine educational workshops and mini symposia in different countries including new member states, high-lighting that Systems Medicine is integrative, not simply repetition of other efforts, for example bioinformatics education.

R50: Intensify collaborations with other organizations and projects relevant for systems medicine education and training including for example medical societies and other stakeholders lobbying initiatives, Innovative Medi-cines Initiative, European Medicines Research Training Network.

R51: Emphasize education for patients so that they under-stand the principles of Systems Medicine and P4 Medi-cine so they can participate in the process along with physicians of the healthcare community, with a tool kit adaptable to diverse cultures and socio-economic groups.

R52: Research on the range of new positions that will need to be created to embed systems practices in clinical and hospital environments; development of relevant training courses.

R53: Establish University working groups to develop and implement new Undergraduate (BA), Graduate (MSc), Doctoral and CME programmes listing also different available tools for different levels and different user groups.

R54: Sustained implementation of novel cross-disciplinary training programmes in Systems Medicine for the next generation of medical doctors and scientists though ini-tiatives including Erasmus+.

9. Community building, networking and disseminating the Systems Medi-cine concept

To ensure a sustainable emphasis on using the Sys-tems Medicine approach to make a real, lasting bene-fit on health, understanding of disease, clinical trial design, funding policy and health economics, a rec-ommendation of the 2014 CASyM road map was to establish a European Society of Systems Medicine, composed of representatives from across the stake-holder groups.

In 2015, the European Association of Systems Medi-cine e.V. (EASyM) was founded by 27 medical doctors, researchers and health care stakeholders from 12 different countries. This charitable association is open to everyone with an interest in personalized medicine and systems medicine. It offers conferences, training opportunities, information and events, with the aim of driving the agenda put forward in the CASyM road map and becoming the voice of Systems Medicine in Europe.

The Association’s inaugural conference in Berlin, 2016, entitled Everything you always wanted to know about Personalized Medicine in Clinical Care drew a wide and varied audience and is a positive first step in integrat-ing a disparate community of researchers and clini-cians.

CASyM exists within an ecosystem of initiatives work-ing to embed personalized approaches to medical practice. Cooperation with other initiatives, patient interest groups as well as regulatory agencies and

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health care organizations are crucial for the successful implementation of Systems Medicine (see Further Reading section for a listing of complementary initia-tives).

A key issue to achieve a successful Europe-wide im-plementation of this approach is the formation of interdisciplinary networks between:

1. EU and national programmes related to the field

2. Clinicians, disease-specific biology experts, systems biologists and bioinformaticians

3. Regulators, patient and healthcare organizations, industry and funders

To date, under the aegis of CASyM and EASyM, col-laborative links have been developed with initiatives on the European Strategy Forum on Research Infra-structures (ESFRI) road map in the field of biomedical science, including Infrastructure for Systems Biology Europe (ISBE), ELIXIR and Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC). Cen-tral to this approach is the question of how exist-ing/new infrastructures projects can engage with the relevant Systems Medicine communities to meet such needs necessary for a short-term implementation process. These collaborations will ensure a coordinat-ed approach to the development of infrastructure that will support the aims of the Systems Medicine road map, as well as promoting the added value that the systems approach can bring to other initiatives.

In addition, CASyM has been proactive in engaging and building a community of scientists, clinicians, patients and industry through a series of tailored workshops (see Further Reading: Reports from CASyM workshops and activities). The CASyM-led Systems Medicine Web Hub (www.systemsmedicine.net) also exemplifies this drive to develop an active community.

Recommendations

R55: Establish links and synergies with other related initia-tives, including projects on the ESFRI road map.

R56: Raise awareness among payers and politicians about economic opportunities that are associated with Sys-tems Medicine and a broad Systems Medicine commu-nity.

R57: Building on the success of the inaugural EASyM confer-ence, develop an ongoing series of conferences and in-

tersectoral, interdisciplinary partnering events for healthcare stakeholders to initiate collaborations.

R58: Building on the work of the ERA-NET ERACoSysMed, provide ongoing facilitation and support for interdisci-plinary, intersectoral project funding applications.

R59: Continue collection of data on successful systems medi-cine projects for dissemination to community and stakeholders.

10. Integration of EU and national efforts in Systems Medicine

Due to its complex and interdisciplinary nature, it is of vital importance to pool efforts and expertise in Sys-tems Medicine both at a national and international level. The Era-Net ERACoSysMed was created with 14 European funding bodies from 13 countries to create a first European core for joint national efforts in systems medicine. The initial focus of activities was on demon-strating usefulness of Systems Medicine for personal-ized approaches in medicine.

Recommendations

R60: Provide information on success cases in Systems Medi-cine to convince politicians.

R61: Use bilateral or joint pan-European calls to instigate collaborations between different countries and different research communities medicine with the aim to:

- Develop and improve mathematical and in silico models.

- Support projects for clinical applications of systems medicine that demonstrate the value for persona-lised medicine.

- Enable training and community building. R62: Engage private foundations in common funding efforts

for Systems Medicine.

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http://www.systemsmedicine.net/


R63: Implement requirements for consequent data harmoni-zation, management, accessibility and sustainability plans into the calls for projects.

R64: Establish new and support existing prospective and retrospective patient cohorts.

R65: Align with the International Consortium Personalized Medicine (IC PerMed) and the respective Era-Net PerMed for synergies and collaboration.

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PROOF OF CONCEPT

Systems Medicine in practice

The CASyM Road Map of 2014 identified as a core priority action European funding for proof of concept projects that demonstrate technological and meth-odological feasibility, tangible clinical benefits, with clear potential, social and/or economic benefits of Systems Medicine. In response to this recommenda-tion, the ERA-NET ERACoSysMed was established in 2015 based on an ERA-Net cofund call by the Europe-an Commission. Integrating 14 European funding bodies from 13 countries in a joint effort to further the development of Systems Medicine in the European Research Area, it is a first promising step towards joint funding initiatives in this field at the intersection of biomedical research and clinical studies. It will help gathering experiences and allow an alignment of strategies between public and EU funding bodies in systems medicine funding.

Projects funded under the first joint transnational call for proposals (JCT-1) have already commenced, co-funded by a 33% EU contribution with a total budget of more than €12M, with a second call launched in 2017. These demonstrator projects in Systems Medi-cine are oriented towards a clearly defined medical

need with the aim that they can be successfully com-pleted within three years and are delivered by pan-European consortia. This is an important step in fur-thering the implementation of Systems Medicine in the EU, following the recommen-dations of the 2014 CASyM road map.

Examples 1-3 describe projects currently funded under the ERACoSysMed programme and illustrate the breadth of applications of systems medicine and the expected clinical outcomes, with development of models in a 2 to 5-year timeframe and anticipated benefits to patients within 5 to 10 years. More projects and further details can be found on the EARCoSysMed homepage under www.eracosysmed.eu.

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Example1: OxyUC — The impact of hy-poxia on inflammation and tumorigen-esis in Ulcerative Colitis

Coordinated by: University College Dublin, Ireland Partners: Ireland, Germany, Belgium

The project OxyUC uses a combination of clinical measurements, transgenic animal studies and mathematical modelling to make prognostic predictions about individual pa-tient's prognosis for inflammatory bowel disease based on intestinal mucosal oxygen measurements and the strength of the individual Hypoxia-inducible factor-1 response. The successful achievement of this work requires the use of experimental models, patient based data and mathematical modelling to provide a personalized approach to the diagnosis, prognosis and potentially therapeutic approach to inflammatory bowel diseases on a patient-by-patient basis.

Anticipated impacts 2 years: outputs in the first two years of the project will be mostly academic in terms of developing the model to make effective prognostic and potentially therapeu-tic decisions for individual patients based on their mucosal PO2 measurements and the strength of an individual’s response to hypoxia.

5 years: the project consortium aims to have a math-ematical model which will be informed by an individu-al patient's mucosal PO2 and the strength of their hypoxic response and will be used to make predictions about patient prognosis and inform the design of optimal therapeutic protocols.

10 years: it is hoped that this project will generate a routine way in which to evaluate the nature of an individual patient’s disease and how best to treat it.

Example 2: SysPharmPedia — Systems pharmacology approach to difficult-to-treat pediatric asthma

Coordinated by: Academisch Medisch Centrum, Netherlands Partners: Netherlands, Slovenia, Spain, Germany

The project

Childhood asthma is a complex multifactorial disease. Inhaled corticosteroids (ICS) are the cornerstone of asthma treatment. However, large variability in treat-ment response to ICS is observed. In this project, bi-omarkers for non-response to ICS are studied in vari-ous biological -omics layers (for example breathomics, microbiomics, metabolomics, epigenomics and [phar-maco]genomics). The integration of these layers can lead to predictive profiles of non-response to standard asthma therapy with ICS and the identification of novel biological pathways underlying this non-response.

The aims of the project could not be achieved without systems medicine approaches, since the focus of this project is the combination and integration of different -omics layers to address the complexity of the disease and treatment response. By using systems approaches, different phenotypes of asthmatic children that do not respond to standard therapy with ICS can be identi-fied. In the future, this can lead to faster assignment of the right drug to the right person (precision medicine), less asthma exacerbations and shorter periods of un-controlled asthma symptoms and a reduction of healthcare costs.

The project will lead to the identification of biomarker profiles for children with asthma responding to treat-ment and children not responding to treatment. Fur-thermore, the project will result in a pan-European database of well phenotyped asthmatic children. This data may provide possible leads for other research questions. Additionally, the international collabora-tions in this project can be used for follow-up studies in the future.

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Impacts

2 years: new computational algorithms that combine different -omics layers to predict phenotype of treat-ment response and novel biological pathways underly-ing treatment response will be available.

5 years: the results of this study can lead to improved treatment strategies for paediatric asthma patients, through the implementation of clinical predictive algo-rithms to guide treatment choices. This will improve the quality of life of paediatric asthma patients.

10 years: this study might result in the development of new drugs for treatment of childhood asthma. Addi-tionally, the project can provide leads for industry, for example diagnostic devices companies who develop equipment to monitor asthma and treatment response at home.

Example 3: Sys4MS — Personalizing health care in Multiple Sclerosis using systems medicine tools

Coordinated by: Consorci Institut D'Investigacions Biomèdiques August Pi i Sunyer, Spain. Partners: Spain, Germany, Italy, Norway

Development of personalized health care for complex diseases like Multiple Sclerosis (MS) is hindered by a poor understanding of the biological processes under-lying the disease and their interactions, as well as by the heterogeneity between patients. These shortcom-ings also represent a significant limitation in terms of monitoring or predicting the disease course, as well as in the prescription of the most efficacious or safer therapies. By integrating clinical information with -omics data and mathematical models of MS, we aim to develop algorithms that can be used in clinical practice to define the prognosis of the disease and that will help in selecting the best therapeutic approach based on the patient’s phenotype.

This project is the evolution of two previous European projects (ComplexDis, CombiMS) in which we have

developed mathematical and network models of auto-immune diseases with special focus in MS. Now, Sys4MS is aimed to apply such modeling to clinical applications for patient’s risk stratification and thera-peutic management.

With systems medicine we can afford now questions before completely untenable and unachievable, such as making understanding of a process evolving along time, with thousands of variables and significant het-erogeneity between individuals.

The aim of Sys4MS is to develop computational mod-els of MS in order to stratify patients based in the severity of the disease. Therefore, outcomes expected are a set of algorithms based in molecular assessment, clinical/imaging evaluation and the application of our computational models for guiding physician and pa-tients in the decision-making process of their health care.

Impacts 2 years: the project consortium plans to deliver a set of algorithms available in the web and sustained for databases generated by Sys4MS, which would be used for identifying patients at higher risk of severe disease course or requiring more efficient therapy.

5 years: models to have matured by this time in order to have a wider acceptance in the clinical community, having improved MS patient management and by this way starting to make an impact in their quality of life

10 years: ten years after the deployment of systems medicine tools for the management and treatment of MS they should pay off a significant improvement in MS management, with less patients achieving higher levels of disability and significant more patients being stable and enjoying a higher quality of life.

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FURTHER READING

CASyM legacy

1. ERA-Net ERACoSysMed: Collaboration on systems medicine funding to promote the implementation of

systems biology approaches in clinical research and medical practice. www.eracosysmed.eu

2. European Association of Systems Medicine e.V. (EASyM). www.easym.eu

3. The Systems Medicine Web Hub. www.systemsmedicine.net

4. Systems Medicine, 2016, Ulf Schmitz and Olaf Wolkenhauer. ISBN 978-1-4939-3283-2.

Reports from CASyM consultation and workshop activities

All reports are freely available for download from the CASyM website [more]:

1. From Systems Biology to Systems Medicine (Brussels, 2010) 2. EU/CASyM joint workshop (Brussels, 2012) 3. 1st open stakeholder consultation conference (Lyon, 2013) 4. 2nd clinically focused stakeholder conference (St Andrews, 2013) 5. Strategic modelling workshop (Heidelberg, 2013) 6. Systems Medicine training tutorial (Ljubljana, 2013) 7. Technological and ethical focused workshop (Lyon, 2013) 8. CASyM/ICSB2013 training workshop (Copenhagen, 2013) 9. Analysis of instruments for integrated research programmes (Luxembourg, 2013) 10. National programmes in Systems Medicine (December 2013) 11. Clinical needs in oncology and cardiovascular diseases as drivers for a Systems Medicine approach (Genoa,

2014) 12. Europe-wide inventory of industry involved in Systems Medicine (March 2014) 13. Academic/Industry interaction workshop report (Lyon, April 2014) 14. CASyM tutorial: Modeling Tools for Pharmacokinetics and Systems Medicine (Stuttgart, May 2014) 15. CASyM Road Map 1st edition (June 2014)

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http://www.eracosysmed.eu/

http://www.easym.eu/

http://www.systemsmedicine.net/

https://www.casym.eu/index.php?index=90


16. Open access e-learning for Systems Medicine: The Road Map to Systems Medicine. (June 2014) 17. Systems Biology and Systems Medicine School - Systems Biology and Systems Medicine: Precision Biotech-

nology and Therapies (Lake Como, September 2014) 18. Systems approaches to biological clocks and diseases (Paris, October 2014) 19. CASyM training concept - Plan for tailored interdisciplinary exchange programmes (November 2014) 20. CASyM workshop: Joining national and European research programmes in systems (bio-)medicine (Brussels,

November 2014) 21. Workshops on genomic disease models of complex diseases, 2013-2015 22. CASyM training concept: CPD plan for clinical and pre-clinical MDs. 23. Academic/Industry interaction workshop report (Lyon, April 2015) 24. CASyM Advanced Summer School in System Medicine: Implementation of System Medicine across Europe -

a FEBS Advanced Lecture Course (Stockholm, June 2015) 25. 10th CFGBC Symposium with ISBE and CASyM workshops (Ljubljana, June/July 2015) 26. Endocrine, Metabolic and Digestive disorders: Challenges and Opportunities of a Systems Medicine ap-

proach (Genoa, July 2015) 27. CASyM strategy paper: Common funding initiatives (August 2015) 28. Plan for university SysMed undergraduate/graduate education including pilot courses (December 2015)

Selected complementary initiatives

1. BBMRI-ERIC Biobanking and Biomolecular Resources Research Infrastructure. www.bbmri-eric.eu 2. CORBEL Coordinated Research Infrastructures Building Enduring Life-science Services. www.corbel-

project.eu 3. EATRIS Research Infrastructure for Translational Medicine. eatris.eu 4. ELIXIR Research Infrastructure for Life Science Data. www.elixir.org 5. EU FP7 Coordination and Support Action PerMed: Personalized Medicine 2020 and beyond.

www.permed2020.eu 6. EU FP7 Infrastructure for Systems Biology Europe. project.isbe.eu 7. EU FP7 Systems medicine of chronic Inflammatory Bowel Disease (SysmedIBD). www.sysmedibd.eu 8. FAIRDOMHub: repository and collaboration environment for sharing systems biology research. fair-dom.org 9. International Consortium on Personalised medicine (IC PerMed). www.icpermed.eu

Systems Medicine state of the art

1. Auffray, C., Chen, Z. & Hood, L. 2009, "Systems medicine: The future of medical genomics and healthcare", Genome Medicine, vol. 1, no. 1.

2. Boissel, J.-., Auffray, C., Noble, D., Hood, L. & Boissel, F.-. 2015, "Bridging systems medicine and patient needs", CPT: Pharmacometrics and Systems Pharmacology, vol. 4, no. 3, pp. 135-145.

3. Bordbar, A., Feist, A.M., Usaite-Black, R., Woodcock, J., Palsson, B.O. & Famili, I. 2011, "A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology", BMC Systems Biolo-gy, vol. 5.

4. Bown, J.L., Shovman, M., Robertson, P., Boiko, A., Goltsov, A., Mullen, P. & Harrison, D.J. 2016, “A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT”, Oncotarget, vol.8, no.18.

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http://www.bbmri-eric.eu/

http://www.corbel-project.eu/

http://www.corbel-project.eu/

http://eatris.eu/

http://www.elixir.org/

http://www.permed2020.eu/

https://www.sysmedibd.eu/

http://fair-dom.org/

http://www.icpermed.eu/


5. Briffa, R., Um, I., Faratian, D., Zhou, Y., Turnbull, A.K., Langdon, S.P. & Harrison, D.J. 2015, “Multi-Scale Ge-nomic, Transcriptomic and Proteomic Analysis of Colorectal Cancer Cell Lines to Identify Novel Biomarkers”, PLoS One, vol.10, no.12.

6. Caie, P.D., Schuur, K., Oniscu, A., Mullen, P., Reynolds, P.A., Harrison, D.J. 2013, “Human tissue in systems medicine”, The FEBS Journal, vol. 280, no.23, pp.5949-56.

7. Caie, P.D., Turnbull, A.K., Farrington, S.M., Oniscu, A. & Harrison, D.J. 2014, “Quantification of tumour bud-ding, lymphatic vessel density and invasion through image analysis in colorectal cancer”, Journal of Transla-tional Medicine, vol. 12, pp.156.

8. Caie, P.D., Zhou, Y., Turnbull, A.K., Oniscu, A. & Harrison, D.J. 2016 “Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting”, Oncotarget, vol. 7, no.28, pp.44381-44394.

9. Caie, P.D. & Harrison, D.J. 2016, “Next-Generation Pathology”, Methods in Molecular Biology, vol. 1386, 61-72.

10. Castagnino, N., Maffei, M., Tortolina, L., Zoppoli, G., Piras, D., Nencioni, A., Moran, E., Ballestrero, A., Patrone, F., Parodi, S, 2016, “Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial”; Wiley Interdiscip Rev Syst Biol Med.vol. 8, no.4, pp. 314-336. doi: 10.1002/wsbm.1342.

11. Cesario, A., Auffray, C., Agusti, A., Apolone, G., Balling, R., Barbanti, P., Bellia, A., Boccia, S., Bousquet, J., Car-daci, V., Cazzola, M., Dall'Armi, V., Daraselia, N., Ros, L.D., Bufalo, A.D., Ducci, G., Ferri, L., Fini, M., Fossati, C., Gensini, G., Granone, P.M., Kinross, J., Lauro, D., Cascio, G.L., Lococo, F., Lococo, A., Maier, D., Marcus, F., Margaritora, S., Marra, C., Minati, G., Neri, M., Pasqua, F., Pison, C., Pristipino, C., Roca, J., Rosano, G., Rossini, P.M., Russo, P., Salinaro, G., Shenhar, S., Soreq, H., Sterk, P.J., Stocchi, F., Torti, M., Volterrani, M., Wouters, E.F., Frustaci, A. & Bonassi, S. 2014, "A systems medicine clinical platform for understanding and managing non- communicable diseases", Current pharmaceutical design, vol. 20, no. 38, pp. 5945-5956.

12. Cesario, A., Auffray, C., Russo, P. & Hood, L. 2014, "P4 Medicine Needs P4 Education", Current pharmaceuti-cal design, vol. 20, no. 38, pp. 6071-6072.

13. Duffy, D.J. 2016, "Problems, challenges and promises: Perspectives on precision medicine", Briefings in Bioin-formatics, vol. 17, no. 3, pp. 494-504.

14. Fey, D., Halasz, M., Dreidax, D., Kennedy, S.P., Hastings, J.F., Rauch, N., Munoz, A.G., Pilkington, R., Fischer, M., Westermann, F., Kolch, W., Kholodenko, B.N. & Croucher, D.R. 2015, "Signaling pathway models as bi-omarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma pa-tients", Science Signaling, vol. 8, no. 408.

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http://www.genome.gov/sequencingcosts/:

http://apps.who.int/nha/database)


ACKNOWLEDGEMENTS

CASyM is funded by the European Union, Seventh Framework Programme under the Health Cooperation Theme and Grant Agreement # 305033.

This document is part of CASyM work package 1: Developing a roadmap towards an integrative European Sys-tems Medicine strategy.

The following individuals, as part of the Scientific Steering Committee, are involved in the scientific coordination of CASyM:

Charles Auffray - European Institute for Systems Biology & Medicine - EISBM, France Mikael Benson (Deputy Chair) - Linköping University Hospital, Sweden Rob Diemel - The Netherlands Organisation for Health Research and Development, The Netherlands David Harrison (Chair) - University of St. Andrews, United Kingdom Walter Kolch - University College Dublin, Ireland Johannes Mohr - Federal Ministry of Education and Research, Germany Francis Lévi - Institut National de la Sante et de la Recherche Medicale, France Damjana Rozman (Deputy Chair) - University of Ljubljana, Faculty of Medicine, Slovenia Johannes Schuchhardt - MicroDiscovery GmbH, Germany Olaf Wolkenhauer - Dept. of Systems Biology & Bioinformatics University of Rostock, Germany

Administrative Office (Coordination): Marc Kirschner - Forschungszentrum Jülich GmbH, Project Management Jülich, Germany

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the casym roadmaphealthcare costs continue to rise rapidly. the increas-ing burden of providing...

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