Systems Medicine Approach Research Articles

Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort.

U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12 months; p<0.001), with worse quality of life, and higher levels of anxiety and depression. They also had a higher incidence of nasal polyps and gastro-oesophageal reflux with lower lung function. Sputum eosinophil count was higher in severe asthma compared to mild/moderate asthma (median count 2.99% versus 1.05%; p=0.004) despite treatment with higher doses of inhaled and/or oral corticosteroids.Consistent with other severe asthma cohorts, U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment. It is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach.

Systems Medicine: The Application of Systems Biology Approaches for Modern Medical Research and Drug Development.

The exponential development of highly advanced scientific and medical research technologies throughout the past 30 years has arrived to the point where the high number of characterized molecular agents related to pathogenesis cannot be readily integrated or processed by conventional analytical approaches. Indeed, the realization that several moieties are signatures of disease has partly led to the increment of complex diseases being characterized. Scientists and clinicians can now investigate and analyse any individual dysregulations occurring within the genomic, transcriptomic, miRnomic, proteomic, and metabolomic levels thanks to currently available advanced technologies. However, there are drawbacks within this scientific brave new age in that only isolated molecular levels are individually investigated for their influence in affecting any particular health condition. Since their conception in 1992, systems biology/medicine focuses mainly on the perturbations of overall pathway kinetics for the consequent onset and/or deterioration of the investigated condition/s. Systems medicine approaches can therefore be employed for shedding light in multiple research scenarios, ultimately leading to the practical result of uncovering novel dynamic interaction networks that are critical for influencing the course of medical conditions. Consequently, systems medicine also serves to identify clinically important molecular targets for diagnostic and therapeutic measures against such a condition.

Networks: Elucidating Experimental Data by Differential Protein-Protein Interactions

Recently, the pathogenesis of coronary atherosclerosis in a swine model has been investigated by considering in particular the effects of a high cholesterol diet lasting 8 or 16 weeks. The proposed systems medicine approach integrating data of plasma adhesion molecules, cytokines, lipoproteins, tissue proteins and histology allowed for an assessment of their relationships with artery-specific proteins.

Personalized cancer immunotherapy using Systems Medicine approaches.

The immune system is by definition multi-scale because it involves biochemical networks that regulate cell fates across cell boundaries, but also because immune cells communicate with each other by direct contact or through the secretion of local or systemic signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the tumor microenvironment. Altogether, the tumor-immunity interaction is a complex multi-scale biological system whose analysis requires a systemic view to succeed in developing efficient immunotherapies for cancer and immune-related diseases. In this review we discuss the necessity and the structure of a systems medicine approach for the design of anticancer immunotherapies. We support the idea that the approach must be a combination of algorithms and methods from bioinformatics and patient-data-driven mathematical models conceived to investigate the role of clinical interventions in the tumor-immunity interaction. For each step of the integrative approach proposed, we review the advancement with respect to the computational tools and methods available, but also successful case studies. We particularized our idea for the case of identifying novel tumor-associated antigens and therapeutic targets by integration of patient's immune and tumor profiling in case of aggressive melanoma.

Sensemaking in Intelligent Health Data Analytics

A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

Application of the Forgotten Effects Model to the Economic Effects for Public European Health Systems by the Early Diagnostics of Emergent and Rare Diseases

The development of new, evidence-based treatments relies on an improved understanding of the often very complex pathophysiology of diseases. The systems (bio) medicine approaches have the potential to tackle this complexity through the integration of a variety of biological and medical research data and computational modelling. A European collaborative approach is required to assemble the necessary multidisciplinary expertise (e.g. biology, medicine, mathematics, computational technologies, and economics) to implement these last systems, which are approached in order to reduce the cost of the European Public Health System and simultaneously improve the wellbeing among citizens.

Systems Medicine: from molecular features and models to the clinic in COPD.

Background and hypothesisChronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.Objective and methodOur overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.ResultsIn the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.ConclusionsThe results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Defining phenotypes in asthma: a step towards personalized medicine.

Asthma is a common disease with a complex pathophysiology. It can present in various clinical forms and with different levels of severity. Unbiased cluster analytic methods have unravelled several phenotypes in cohorts representative of the whole spectrum of severity. Clusters of severe asthma include those on high-dose corticosteroid treatment, often with both inhaled and oral treatment, usually associated with severe airflow obstruction. Phenotypes with concordance between symptoms and sputum eosinophilia have been reported, including an eosinophilic inflammation-predominant group with few symptoms and late-onset disease who have a high prevalence of rhinosinusitis, aspirin sensitivity, and exacerbations. Sputum eosinophilia is also a biomarker that can predict therapeutic responses to antibody-based treatments to block the effects of the T-helper (Th)-2 cytokine, interleukin (IL)-5. Low Th2-expression has been predictive of poor therapeutic response to inhaled corticosteroid therapy. Current asthma schedules emphasise a step-up approach to treating asthma in relation to increasing severity, but, in more severe disease, phenotyping or endotyping of asthma will be necessary to determine new treatment strategies as severe asthma is recognized as being a particularly heterogeneous disease. Much less is known about 'non-eosinophilic' asthma. Phenotypic characterisation of corticosteroid insensitivity and chronic airflow obstruction of severe asthma is also needed. Phenotype-driven treatment of asthma will be further boosted by the advent of transcriptomic and proteomic technologies, with the application of systems biology or medicine approaches to defining phenotypes and biomarkers of disease and therapeutic response. This will pave the way towards personalized medicine and healthcare for asthma.

Systems Medicine Approaches for the Definition of Complex Phenotypes in Chronic Diseases and Ageing. From Concept to Implementation and Policies

Chronic diseases are diseases of long duration and slow progression. Major NCDs (cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, rheumatologic diseases and mental health) represent the predominant health problem of the Century. The prevention and control of NCDs are the priority of the World Health Organization 2008 Action Plan, the United Nations 2010 Resolution and the European Union 2010 Council. The novel trend for the management of NCDs is evolving towards integrative, holistic approaches. NCDs are intertwined with ageing. The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has prioritised NCDs. To tackle them in their totality in order to reduce their burden and societal impact, it is proposed that NCDs should be considered as a single expression of disease with different risk factors and entities. An innovative integrated health system built around systems medicine and strategic partnerships is proposed to combat NCDs. It includes (i) understanding the social, economic, environmental, genetic determinants, as well as the molecular and cellular mechanisms underlying NCDs; (ii) primary care and practice-based interprofessional collaboration; (iii) carefully phenotyped patients; (iv) development of unbiased and accurate biomarkers for comorbidities, severity and follow up of patients; (v) socio-economic science; (vi) development of guidelines; (vii) training; and (viii) policy decisions. The results could be applicable to all countries and adapted to local needs, economy and health systems. This paper reviews the complexity of NCDs intertwined with ageing. It gives an overview of the problem and proposes two practical examples of systems medicine (MeDALL) applied to allergy and to NCD co-morbidities (MACVIA-LR, Reference Site of the European Innovation Partnership on Active and Healthy Ageing).

Nutritional modulation of the metabonome

Metabolic profiling technologies provide a global overview of complex dietary processes. Metabonomic analytical approaches have now been translated into multiple areas of clinical nutritional research based on the widespread adoption of high-throughput mass spectrometry and proton nuclear magnetic resonance spectroscopy. This has generated novel insights into the molecular mechanisms that shape the microbiome-dietary-chronic disease axis. Metabolome-wide association studies have created a new paradigm in nutritional molecular epidemiology and they have highlighted the importance of gut microbial cometabolic processes in the development of cardiovascular disease and diabetes. Targeted analyses are helping to explain the mechanisms by which high-risk diets (such as red meat) modulate disease risk and they are generating novel biomarkers that will serve to re-define how the efficacy of nutritional interventions is assessed. Nutritional metabonome-microbiome interactions have also been defined in extreme dietary states such as obesity and starvation, and they also serve as important models for understanding how the gut microbiome modifies disease risk. Finally, nutritional systems medicine approaches are creating novel insights into the functional components of our diet, and the mechanisms by which they cause disease. Diet is an important modulator of the human metabolic phenotype and the analysis of the nutritional metabolome will drive future development of personalized nutritional interventions.

Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project.

BackgroundChronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering.ObjectivesTo assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients.MethodUnderlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them.Results(1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems.ConclusionsThe project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.

Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia

We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.

Identification Of AML Subtype-Selective Drugs By Functional Ex Vivo Drug Sensitivity and Resistance Testing and Genomic Profiling

Introduction Adult acute myeloid leukemia (AML) exemplifies the challenges of modern cancer drug discovery and development in that molecularly targeted therapies are yet to be translated into clinical use. No effective second-line therapy exists once standard chemotherapy fails. While many genetic events have been linked with the onset and progression of AML, the fundamental disease mechanisms remain poorly understood. There is significant genomic and molecular heterogeneity among patients. Several targeted therapies have been investigated for improved second-line AML therapy but none has been approved for clinical use to date. It would be critically important to identify patient subgroups that would benefit from such therapies and to identify combinations of drugs that are likely to be effective. Methods To identify and optimize novel therapies for AML, we studied 28 samples from 18 AML patients with an individualized systems medicine (ISM) approach. The ISM platform includes functional profiling of AML patient cells ex vivo with drug sensitivity and resistance testing (DSRT), comprehensive molecular profiling as well as clinical background information. Data integration was done to identify disease- and patient-specific molecular vulnerabilities for translation in the clinic. The DSRT platform comprises 306 anti-cancer agents, each tested in a dose response series. We calculated differential drug sensitivity scores by comparing AML responses to those of control cells in order to distinguish cancer-specific drug effects. Next generation RNA- and exome-sequencing was used to identify fusion transcripts and mutations that link to drug sensitivities. Results Individual AML patient samples had a distinct drug sensitivity pattern, but unsupervised hierarchical clustering of the drug sensitivity profiles of the 28 AML patient samples identified 5 functional AML drug response subtypes. Each subtype was characterized by distinct combinations of sensitivities: Bcl-2 inhibitors (e.g. navitoclax; Group 1), JAK inhibitors (e.g. ruxolitinib) (Group 2) and MEK inhibitors (e.g. trametinib) (Groups 2 and 4), PI3K/mTOR inhibitors (e.g. temsirolimus; Groups 4 and 5), broad spectrum receptor tyrosine kinase inhibitors (e.g. dasatinib) (Groups 3, 4 and 5) and FLT3 inhibitors (e.g. quizartinib, sunitinib) (Group 5). Correlation of overall drug responses with genomic profiles revealed that RAS and FLT3 mutations were significantly linked with the drug response subgroups 4 and 5, respectively. Activating FLT3 mutations contributed to sensitivity to FLT3 inhibitors, as expected, but also to tyrosine kinase inhibitors not targeting FLT3, such as dasatinib. Hence, these data point to the potential synergistic combinatorial effects of FLT3 inhibitors with dasatinib for improved therapy outcome (Figure). Early clinical translational results based on compassionate use support this hypothesis. Therefore, by combinations of drugs we expect to see synergistic drug responses that can be translated into efficacious and safe therapies for relapsed AML cases in the clinic. Clinical application of DSRT results in the treatment of eight recurrent chemorefractory patients led to objective responses in three cases according to ELN criteria, whereas four of the remaining five patients had meaningful responses not meeting ELN criteria. After disease progression, AML patient cells showed ex vivo resistance to the drugs administered to the patients, as well as significant changes in clonal architecture during treatment response. Furthermore, we saw genomic alterations potentially explaining drug resistance, such as appearance of novel fusion genes. Summary The ISM approach represents an opportunity for improving therapies for cancer patients, one patient at the time. We show that the platform can be used to identify functional groups of AML linking to vulnerabilities to single targeted drugs and, importantly, unexpected drug combinations. This information can in turn be used for personalized medicine strategies and for creating hypotheses to be explored in systematic clinical trials, both for approved and investigational drugs. Disclosures: Off Label Use: Many of the compounds included in our DSRT platform are not indicated for AML therapy. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Honoraria. Porkka:Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Kallioniemi:Medisapiens: Membership on an entity’s Board of Directors or advisory committees; Roche: Research Funding.

A systems medicine research approach for studying alcohol addiction

According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann et al. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.

Human tissue in systems medicine

Histopathology, the examination of an architecturally artefactual, two-dimensional and static image remains a potent tool allowing diagnosis and empirical expectation of prognosis. Considerable optimism exists that the advent of molecular genetic testing and other biomarker strategies will improve or even replace this ancient technology. A number of biomarkers already add considerable value for prediction of whether a treatment will work. In this short review we argue that a systems medicine approach to pathology will not seek to replace traditional pathology, but rather augment it. Systems approaches need to incorporate quantitative morphological, protein, mRNA and DNA data. A significant challenge for clinical implementation of systems pathology is how to optimize information available from tissue, which is frequently sub-optimal in quality and amount, and yet generate useful predictive models that work. The transition of histopathology to systems pathophysiology and the use of multiscale data sets usher in a new era in diagnosis, prognosis and prediction based on the analysis of human tissue.

Public health genomics and personalized healthcare

Rapid scientific advances and tools in genomics such as in the light of epigenomics, microbiomics, and systems biology supported by new ICT solutions not only contribute to the understanding of disease mechanisms but also provide the option of new promising applications in human health management during the whole life-course. What was a little time ago a vision for a new era of public health, in which advances from the -omic sciences would be integrated into strategies aiming at benefiting population health, is now responding to the very pressing need for the development of effective personalized health care, going even beyond personalized medicine based on a systems medicine approach.

Activation of executioner caspases is a predictor of progression-free survival in glioblastoma patients: a systems medicine approach

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. GBM cells are highly resistant to apoptosis induced by antitumor drugs and radiotherapy resulting in cancer progression. We assessed whether a systems medicine approach, analysing the ability of tumor cells to execute apoptosis could be utilized to predict the response of GBM patients to treatment. Concentrations of the key proapoptotic proteins procaspase-3, procaspase-9, Smac and Apaf-1 and the antiapopotic protein XIAP were determined in a panel of GBM cell lines and GBM patient tumor resections. These values were used as input for APOPTO-CELL, a systems biological based mathematical model built to predict cellular susceptibility to undergo caspase activation. The modeling was capable of accurately distinguishing between GBM cells that die or survive in response to treatment with temozolomide in 10 of the 11 lines analysed. Importantly the results obtained using GBM patient samples show that APOPTO-CELL was capable of stratifying patients according to their progression-free survival times and predicted the ability of tumor cells to support caspase activation in 16 of the 21 GBM patients analysed. Calculating the susceptibility to apoptosis execution may be a potent tool in predicting GBM patient therapy responsiveness and may allow for the use of APOPTO-CELL in a clinical setting.

The SysCLAD- Systems Prediction of Chronic Lung Allograft Dysfunction Study: Aims, Strategy and First Data

Purpose The SysCLAD study is an European Union-funded project under the FP-7 to predict CLAD signature by year-1 post lung transplantation (LT) before any decline in lung function. CLAD and its subtypes Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome represent the actual limitation resulting in a high morbi-mortality and costs. Specific aims of SysCLAD are identification of CLAD biomarkers, a better understanding of CLAD mechanisms and a predictive and personalized signature of CLAD. Methods and Materials Built upon a cohort of LT recipients already transplanted and recruited since September 2009 in 14 LT centres, SysCLAD signature will be based on a mathematical model developed through a systems medicine approach integrating clinical and biological data collected within the first year post LT in 2 steps, using calibration and validation samples within LT cohort. Clinical data include donors and recipients characteristics and post LT clinical events; biological data donors and recipients HLA, whole blood transcriptomics, BAL and blood proteomics at months 6 and 12, together with once miRNA, BAL microbiote and recipient genetic polymorphisms analysis at month 6. Results By November 2012, 820 LT candidates of whom 652 received a LT; indications were by digressive order CF, COPD/emphysema, IPF, PH, bronchiectasis and others; LT recipients were 42.7 ± 15.2 yrs old, 320 received a double LT; 2-years survival was 76%. In SysCLAD biobank, they are by year-1 post LT to predict besides “clinicome”: 1 152 TBB, 7 537 PBMC aliquots, 2362 BAL, 1311 blood samples for transcriptomics Paxgene®, 12 903 plasma and 11 008 sera aliquots. Conclusions CLAD tool is expected by early personalized interventions to improve morbi-mortality and increase the cost-effectiveness of LT. Funds from PHRC 2008 and Vaincre La Mucoviscidose within the 11 French lung Transplantation centres to launch Cohort in Lung Transplantation (COLT)-ClinicalTrials.gov Identifier: NCT00980967, Programme transplantation 2008, PRTP-13, and #HEALTH-F5-2012-305457, a FP-7 call.

Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives

A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future.

Pediatric systems medicine: evaluating needs and opportunities using congenital heart block as a case study

Medicine and pediatrics are changing and health care is moving from being reactive to becoming preventive. Despite rapid developments of new technologies for molecular profiling and systems analysis of diseases, significant hurdles remain. Here, we use the clinical setting of congenital heart block (CHB) to uncover and illustrate key informatics challenges impeding the development of a systems medicine approach emphasizing the prevention and prediction of disease. We find that there is a paucity of useful bioinformatics tools enabling the integrative analysis of different databases of molecular information and clinical sources in a disease context such as CHB, contrasting with the current emphasis on developing bioinformatics tools for the analysis of individual data types. Moreover, informatics solutions for managing data, such as the Integrating Biology and the Bedside (i2b2) or Stanford Translational Research Integrated Database Environment, require serious software engineering support for the maintenance and import of data beyond the capabilities of clinicians working with CHB. Hence, there is an urgent unmet need for user-friendly tools facilitating the integrative analysis and management of omics data and clinical information. Pediatrics represents an untapped potential to execute such a systems medicine program in close collaboration with clinicians and families who are keen to do what is needed for their children to prevent and predict diseases and nurture wellness.