Revolutionizing medicine in the 21(st) century through systems approaches

Personalized medicine is a term for a revolution in medicine that envisions the individual patient as the central focus of healthcare in the future. The term "personalized medicine", however, fails to reflect the enormous dimensionality of this new medicine that will be predictive, preventive, personalized, and participatory-a vision of medicine we have termed P4 medicine. This reflects a paradigm change in how medicine will be practiced that is revolutionary rather than evolutionary. P4 medicine arises from the confluence of a systems approach to medicine and from the digitalization of medicine that creates the large data sets necessary to deal with the complexities of disease. We predict that systems approaches will empower the transition from conventional reactive medical practice to a more proactive P4 medicine focused on wellness, and will reverse the escalating costs of drug development an will have enormous social and economic benefits. Our vision for P4 medicine in 10 years is that each patient will be associated with a virtual data cloud of billions of data points and that we will have the information technology for healthcare to reduce this enormous data dimensionality to simple hypotheses about health and/or disease for each individual. These data will be multi-scale across all levels of biological organization and extremely heterogeneous in type - this enormous amount of data represents a striking signal-to-noise (S/N) challenge. The key to dealing with this S/N challenge is to take a "holistic systems approach" to disease as we will discuss in this article.     

A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory

Systems biology and the digital revolution are together transforming healthcare to a proactive P4 medicine that is predictive, preventive, personalized and participatory. Systems biology - holistic, global and integrative in approach - has given rise to systems medicine, a systems approach to health and disease. Systems medicine promises to (1) provide deep insights into disease mechanisms, (2) make blood a diagnostic window for viewing health and disease for the individual, (3) stratify complex diseases into their distinct subtypes for a impedance match against proper drugs, (4) provide new approaches to drug target discovery and (5) generate metrics for assessing wellness. P4 medicine, the clinical face of systems medicine, has two major objectives: to quantify wellness and to demystify disease. Patients and consumers will be a major driver in the realization of P4 medicine through their participation in medically oriented social networks directed at improving their own healthcare. P4 medicine has striking implications for society - including the ability to turn around the ever-escalating costs of healthcare. The challenge in bringing P4 medicine to patients and consumers is twofold: first, inventing the strategies and technologies that will enable P4 medicine and second, dealing with the impact of P4 medicine on society - including key ethical, social, legal, regulatory, and economic issues. Managing the societal problems will pose the most significant challenges. Strategic partnerships of a variety of types will be necessary to bring P4 medicine to patients.     

An economic perspective on personalized medicine

The concept of personalized medicine not only promises to enhance the life of patients and increase the quality of clinical practice and targeted care pathways, but also to lower overall healthcare costs through early-detection, prevention, accurate risk assessments and efficiencies in care delivery. Current inefficiencies are widely regarded as substantial enough to have a significant impact on the economies of major nations like the US and China, and, therefore the world economy. A recent OECD report estimates healthcare expenditure for some of the developed western and eastern nations to be anywhere from 10% to 18%, and growing (with the US at the highest). Personalized medicine aims to use state-of-the-art genomic technologies, rich medical record data, tissue and blood banks and clinical knowledge that will allow clinicians and payors to tailor treatments to individuals, thereby greatly reducing the costs of ineffective therapies incurred through the current trial and error clinical paradigm. Pivotal to the field are drugs that have been designed to target a specific molecular pathway that has gone wrong and results in a diseased condition and the diagnostic tests that allow clinicians to separate responders from non-responders. However, the truly personalized approach in medicine faces two major problems: complex biology and complex economics; the pathways involved in diseases are quite often not well understood, and most targeted drugs are very expensive. As a result of all current efforts to translate the concepts of personalized healthcare into the clinic, personalized medicine becomes participatory and this implies patient decisions about their own health. Such a new paradigm requires powerful tools to handle significant amounts of personal information with the approach to be known as “P4 medicine”, that is predictive, preventive, personalized and participatory. P4 medicine promises to increase the quality of clinical care and treatments and will ultimately save costs. The greatest challenges are economic, not scientific.     

Personalized health care: from theory to practice

The practice of medicine stands at the threshold of a transformation from its current focus on the treatment of disease events to an emphasis on enhancing health, preventing disease and personalizing care to meet each individual's specific health needs. Personalized health care is a new and strategic approach that is driven by personalized health planning empowered by personalized medicine tools, which are facilitated by advances in science and technology. These tools improve the capability to predict health risks, to determine and quantify the dynamics of disease development, and to target therapeutic approaches to the needs of the individual. Personalized health care can be implemented today using currently available technologies and know-how and thereby provide a market for the rational introduction of new personalized medicine tools. The need for early adoption of personalized health care stems from the necessity to reduce the egregious and wasteful burden of preventable chronic diseases, which is not effectively addressed by our current approach to care.     

Predictive diagnostics and personalized medicine for the prevention of chronic degenerative diseases

Progressive increase of mean age and life expectancy in both industrialized and emerging societies parallels an increment of chronic degenerative diseases (CDD) such as cancer, cardiovascular, autoimmune or neurodegenerative diseases among the elderly. CDD are of complex diagnosis, difficult to treat and absorbing an increasing proportion in the health care budgets worldwide. However, recent development in modern medicine especially in genetics, proteomics, and informatics is leading to the discovery of biomarkers associated with different CDD that can be used as indicator of disease's risk in healthy subjects. Therefore, predictive medicine is merging and medical doctors may for the first time anticipate the deleterious effect of CDD and use markers to identify persons with high risk of developing a given CDD before the clinical manifestation of the diseases. This innovative approach may offer substantial advantages, since the promise of personalized medicine is to preserve individual health in people with high risk by starting early treatment or prevention protocols. The pathway is now open, however the road to an effective personalized medicine is still long, several (diagnostic) predictive instruments for different CDD are under development, some ethical issues have to be solved. Operative proposals for the heath care systems are now needed to verify potential benefits of predictive medicine in the clinical practice. In fact, predictive diagnostics, personalized medicine and personalized therapy have the potential of changing classical approaches of modern medicine to CDD.     

Systems Approaches to Biology and Disease Enable Translational Systems Medicine

The development and application of systems strategies to biology and disease are transforming medical research and clinical practice in an unprecedented rate.In the foreseeable future,clinicians,medical researchers,and ultimately the consumers and patients will be increasingly equipped with a deluge of personal health information,e.g.,whole genome sequences,molecular profiling of diseased tissues,and periodic multi-analyte blood testing of biomarker panels for disease and wellness.The convergence of these practices will enable accurate prediction of disease susceptibility and early diagnosis for actionable preventive schema and personalized treatment regimes tailored to each individual.It will also entail proactive participation from all major stakeholders in the health care system.We are at the dawn of predictive,preventive,personalized,and participatory(P4) medicine,the fully implementation of which requires marrying basic and clinical researches through advanced systems thinking and the employment of high-throughput technologies in genomics,proteomics,nanofluidics,single-cell analysis,and computation strategies in a highly-orchestrated discipline we termed translational systems medicine.     

Personalized medicine: revolutionizing drug discovery and patient care.

Advances in human genome research are opening the door to a new paradigm for practising medicine that promises to transform healthcare. Personalized medicine, the use of marker-assisted diagnosis and targeted therapies derived from an individual's molecular profile, will impact the way drugs are developed and medicine is practiced. Knowledge of the molecular basis of disease will lead to novel target identification, toxicogenomic markers to screen compounds and improved selection of clinical trial patients, which will fundamentally change the pharmaceutical industry. The traditional linear process of drug discovery and development will be replaced by an integrated and heuristic approach. In addition, patient care will be revolutionized through the use of novel molecular predisposition, screening, diagnostic, prognostic, pharmacogenomic and monitoring markers. Although numerous challenges will need to be met to make personalized medicine a reality, with time, this approach will replace the traditional trial-and-error practice of medicine.     

Predictive medicine by cytomics: potential and challenges

Predictive medicine by cytomics represents a new concept which provides disease course predictions for individual patients. The predictive information is derived from the molecular cell phenotypes as they are determined by patient's genotype and exposure to external or internal influences. The predictions are dynamic because they are therapy dependent. They may provide a therapeutic lead time for preventive therapy or for the diminution of disease associated irreversible tissue damage. Multiparametric data from cytometry, multiple clinical chemistry assays, chip or bead arrays serve as input for an algorithmic data sieving procedure (http://www.biochem.mpg.de/valet/classif1.html). Data sieving enriches the discriminatory parameters in form of standardized data masks for predictive or diagnostic disease classification in the individual patient (http://www.biochem.mpg.de/valet/cellclas.html). Besides predictive and diagnostic utility, the data patterns can be used in a bottom-up approach for the development of scientific hypotheses on disease inducing mechanisms in complex inflammatory, infectious, allergic, malignant or degenerative diseases.     

Integrating noninvasive molecular imaging into molecular medicine: an evolving paradigm

Molecular imaging is a rapidly emerging field, providing noninvasive visual quantitative representations of fundamental biological processes in intact living subjects. Fundamental biomedical research stands to benefit considerably from advances in molecular imaging, with improved molecular target selection, probe development and imaging instrumentation. The noninvasiveness of molecular imaging technologies will also provide benefit through improved patient care. Molecular imaging endpoints can be quantified, and therefore are particularly useful for translational research. Integration of the two disciplines of molecular imaging and molecular medicine, combined with systems-biology approaches to understanding disease complexity, promises to provide predictive, preventative and personalized medicine that will transform healthcare.     

Personalized Modeling for Prediction with Decision-Path Models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.     

Genes, diet and public health

Common chronic diseases such as coronary heart disease (CHD), diabetes, cancer, hypertension and obesity are significantly influenced by dietary and other behavioural habits. There is increasing scientific evidence that genetic factors (SNPs), conferring either protection or risk, also contribute importantly to the incidence of these diseases. SNPs are of particular interest because they influence disease in a complex but largely unknown manner by interacting with environmental and lifestyle factors. Because genetic factors also affect a person's response to dietary habits, SNPs likely will be useful in helping to determine and understand why individuals differ in their response to diets. Therefore, the discovery of SNPs will likely revolutionize not only the diagnosis of disease but also the practice of preventative medicine. Other developments, like new biomarkers and noninvasive imaging techniques, might turn out to be highly sensitive and specific in order to identify patients at risk, especially in cases with asymptomatic coronary heart disease. Thus, further knowledge of such new risk factors and their interaction with nutrition, has the potential to provide a more precise and personalized approach to prevent and treat chronic diseases like coronary artery disease, myocardial infarction and stroke.     

DiseaseComps: a metric that discovers similar diseases based upon common toxicogenomic profiles at CTD

The Comparative Toxicogenomics Database (CTD) is a free resource that describes chemical-gene-disease networks to help understand the effects of environmental exposures on human health. The database contains more than 13,500 chemical-disease and 14,200 gene-disease interactions. In CTD, chemicals and genes are associated with a disease via two types of relationships: as a biomarker or molecular mechanism for the disease (M-type) or as a real or putative therapy for the disease (T-type). We leveraged these curated datasets to compute similarity indices that can be used to produce lists of comparable diseases ("DiseaseComps") based upon shared toxicogenomic profiles. This new metric now classifies diseases with common molecular characteristics, instead of the traditional approach of using histology or tissue of origin to define the disorder. In the dawning era of "personalized medicine", this feature provides a new way to view and describe diseases and will help develop testable hypotheses about chemical-gene-disease networks. AVAILABILITY: The database is available for free at http://ctd.mdibl.org/     

Cytomics, the human cytome project and systems biology: top-down resolution of the molecular biocomplexity of organisms by single cell analysis

A large amount of structural and functional information is obtained by molecular cell phenotype analysis of tissues, organs and organisms at the single cell level by image or flow cytometry in combination with bioinformatic knowledge extraction (cytomics) concerning nuclei acids, proteins and metabolites (cellular genomics, proteomics and metabolomics) as well as cell function parameters like intracellular pH, transmembrane potentials or ion gradients. In addition, differential molecular cell phenotypes between diseased and healthy cells provide molecular data patterns for (i) predictive medicine by cytomics or for (ii) drug discovery purposes using reverse engineering of the data patterns by biomedical cell systems biology. Molecular pathways can be explored in this way including the detection of suitable target molecules, without detailed a priori knowledge of specific disease mechanisms. This is useful during the analysis of complex diseases such as infections, allergies, rheumatoid diseases, diabetes or malignancies. The top-down approach reaching from single cell heterogeneity in cell systems and tissues down to the molecular level seems suitable for a human cytome project to systematically explore the molecular biocomplexity of human organisms. The analysis of already existing data from scientific studies or routine diagnostic procedures will be of immediate value in clinical medicine, for example as personalized therapy by cytomics.     

Systems Biology,Systems Medicine,Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.     

Clinical Proteomics: From Biomarker Discovery and Cell Signaling Profiles to Individualized Personal Therapy

The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized “designer” therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient’s disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.     

Methods for the absolute quantification of N-glycan biomarkers

BackgroundMany treatment options especially for cancer show a low efficacy for the majority of patients demanding improved biomarker panels for patient stratification. Changes in glycosylation are a hallmark of many cancers and inflammatory diseases and show great potential as clinical disease markers. The large inter-subject variability in glycosylation due to hereditary and environmental factors can complicate rapid transfer of glycan markers into the clinical practice but also presents an opportunity for personalized medicine.Scope of reviewThis review discusses opportunities of glycan biomarkers in personalized medicine and reviews the methodology for N-glycan analysis with a specific focus on methods for absolute quantification.Major conclusionsThe entry into the clinical practice of glycan markers is delayed in large part due to a lack of adequate methodology for the precise and robust quantification of protein glycosylation. Only absolute glycan quantification can provide a complete picture of the disease related changes and will provide the method robustness required by clinical applications.General significanceGlycan biomarkers have a huge potential as disease markers for personalized medicine. The use of stable isotope labeled glycans as internal standards and heavy-isotope labeling methods will provide the necessary method precision and robustness acceptable for clinical use. This article is part of a Special Issue entitled “Glycans in personalized medicine” Guest Editor: Professor Gordan Lauc.     

Personalized medicine and proteomics: lessons from non-small cell lung cancer

Personalized medicine allows the selection of treatments best suited to an individual patient and disease phenotype. To implement personalized medicine, effective tests predictive of response to treatment or susceptibility to adverse events are needed, and to develop a personalized medicine test, both high quality samples and reliable data are required. We review key features of state-of-the-art proteomic profiling and introduce further analytic developments to build a proteomic toolkit for use in personalized medicine approaches. The combination of novel analytical approaches in proteomic data generation, alignment and comparison permit translation of identified biomarkers into practical assays. We further propose an expanded statistical analysis to understand the sources of variability between individuals in terms of both protein expression and clinical variables and utilize this understanding in a predictive test.     

Ethical models underpinning responses to threats to public health: a comparison of approaches to communicable disease control in Europe

Increases in international travel and migratory flows have enabled infectious diseases to emerge and spread more rapidly than ever before. Hence, it is increasingly easy for local infectious diseases to become global infectious diseases (GIDs). National governments must be able to react quickly and effectively to GIDs, whether naturally occurring or intentionally instigated by bioterrorism. According to the World Health Organisation, global partnerships are necessary to gather the most up-to-date information and to mobilize resources to tackle GIDs when necessary. Communicable disease control also depends upon national public health laws and policies. The containment of an infectious disease typically involves detection, notification, quarantine and isolation of actual or suspected cases; the protection and monitoring of those not infected; and possibly even treatment. Some measures are clearly contentious and raise conflicts between individual and societal interests. In Europe national policies against infectious diseases are very heterogeneous. Some countries have a more communitarian approach to public health ethics, in which the interests of individual and society are more closely intertwined and interdependent, while others take a more liberal approach and give priority to individual freedoms in communicable disease control. This paper provides an overview of the different policies around communicable disease control that exist across a select number of countries across Europe. It then proposes ethical arguments to be considered in the making of public health laws, mostly concerning their effectiveness for public health protection.     

Ethical considerations in the presymptomatic diagnosis of Alzheimer's disease

Research into the human genome has undoubtedly opened up a new perspective in medicine. The ability to identify the cause of specific diseases, especially neurodegenerative diseases, will definitively change the concepts of disease and treatment, while advances such as antibiotic therapy and anesthesia will be relegated to history. However, the arrival of genome medicine poses major bioethical challenges, many of which remain to be resolved. We review the applicability, results and consequences of predictions based on genetic tests for presymptomatic Alzheimer's disease, as well as the dilemmas and contradictions that are already arising as a result of the commercialization of predictive tests for public use with little or no medical supervision. Given that there is currently no effective treatment of Alzheimer?s disease, the greatest challenge and contradiction lies in managing the results of predictive tests. There are no indications for the performance of predictive genetic tests in late or sporadic Alzheimer's disease or for counselling of persons requesting these tests. The PICOGEN program provides a safe, effective, reliable and satisfactory option for persons requesting these tests who meet the inclusion criteria. Currently, caution should be the norm when considering the performance of predictive tests in presymptomatic dementia.