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.     

BeadArray-based solutions for enabling the promise of pharmacogenomics

A "one-size-fits-all" approach continues to characterize today's healthcare paradigm. But emergent rules, information, genomics tools, and economics are driving a fundamental and inevitable shift to a more personalized world of medicine. In this new world, the interests of insurers, regulators, suppliers, healthcare providers, and most important, patients, will have converged. The new goal will be the right treatment for the right individual at the right time. In this world, personalized medicine, through pharmacogenomics (PGx), will be the new healthcare paradigm. We will briefly examine healthcare trends and current opportunities for PGx development. We will then demonstrate how microarray technologies-among them bead-based approaches-have emerged as a key enabler for bringing home the promise of PGx.     

Translational genomics in personalized medicine – scientific challenges en route to clinical practice

Background

In the area of omics and translational bio(medical)sciences, there is an increasing need to integrate, normalize, analyze, store and protect genomics data. Large datasets and scientific knowledge are rationally combined into valuable clinical information that ultimately will benefit human healthcare and are en route to clinical practice. Data from biomarker discovery and Next Generation Sequencing (NGS) are very valuable and will combine in comprehensive analyses to stratify medicine and guide therapy planning and ultimately benefit patients. However, the combination into useful and applicable information and knowledge is not trivial.

NGS in personalized medicine

Personalized medicine generally promises to result in both higher quality in treatment for individual patients and in lower costs in health care since patients will be offered only such therapies that are more effective for them and treatments that will not be safe or effective will be avoided. Recent advancements in biomedical and genomic sciences have paved the way to translate such research into clinical practice and health policies. However, the move towards greater personalization of medicine also comes along with challenges in the development of novel diagnostic and therapeutic tools in a complex framework that assumes that the use of genomic information is part of a translational continuum, which spans from basic to clinical research, preclinical and clinical trials, to policy research and the analysis of health and economic outcomes. The use of next-generation genomic technologies to improve the quality of life and efficiency of healthcare delivered to patients has become a mainstay theme in the field as benefits derived from such approaches include reducing a patient’s need to go through ineffective therapies, lowering side- and off-target effects of drugs, prescribing prophylactic therapies before acute exacerbations, and reducing expenditures.

Economic challenges

As such, personalized medicine promises to increase the quality of clinical care and, in some cases, to decrease health care costs. Besides the scientific challenges, there are several economic hurdles. For instance, healthcare providers need to know, whether the approach of personalized healthcare is affordable and worth the expenses. In addition, the economic rationale of personalized healthcare includes not only the reduction of the high expense of hospitalizations, the predictive diagnostics that will help to reduce cost through prevention or the increased efficacy of personalized therapies needs to offset prices of drugs. There are also several factors that influence payer adoption, coverage and reimbursement; the strength of evidence drives payers‘ decisions about coverage and reimbursement, varies widely depending on the personalized healthcare technology applied and regulation and cost-effectiveness seem to be increasingly associated with reimbursement, which is strongly influenced by professional society guidelines. In general, we see the following main obstacles to the advancement of personalized medicine: (i) the scientific challenges (a poor understanding of molecular mechanisms or a lack of molecular markers associated with some diseases, for example), (ii) the economic challenges (poorly aligned incentives), and (iii) operational issues in public healthcare systems. The operational issues can often be largely resolved within a particular stakeholder group, but correcting the incentive structure and modifying the relationships between stakeholders is more complex.

En route to clinical practice

This article focuses on the scientific difficulties that remain to translate genomics technologies into clinical practice and reviews recent technological advances in genomics and the challenges and potential benefits of translating this knowledge into clinical practice, with a particular focus on their applications in oncology.

Electronic supplementary material

The online version of this article (doi:10.1186/1877-6566-6-2) contains supplementary material, which is available to authorized users.     

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.     

Quantifying your body: A how-to guide from a systems biology perspective

During the coming decade we will see an accelerated digital transformation of healthcare. Leading this change within the institutional medical community are both the move to digital medical records and the use of digital biomedical measurement devices. In addition to this institutional evolution, there is a non-institutional, bottom-up, unorganized, highly idiosyncratic movement by early adopters to "quantify" their own bodies. In this article, I share my decade-long personal experience of tracking many blood and stool biomarkers, which provide insight into the health or disease of major subsystems of my body. These results are interpreted in the context of the genetics of my human DNA and that of the microbes in my gut. Even though I am a computer scientist and not a medical professional, by using commercially available tests and a systems biology integrative approach, I have become an early example of Leroy Hood's vision of the emergence of predictive, preventive, personalized, and participatory (P4) medicine. It is an individual's story illustrating how each of us can contribute to realizing this paradigm shift.     

Infection systems biology: from reactive to proactive (P4) medicine

This short review establishes the conceptual bases and discusses the principal aspects of P4-shorthand for predictive, preventive, personalized and participatory medicine-medicine, in the framework of infectious diseases. P4 medicine is a new way to approach medical care; instead of acting when the patient is sick, physicians will be able to detect early warnings of disease to take early action. Furthermore, people might even be able to adjust their lifestyles to prevent disease. P4 medicine is fuelled by systems approaches to disease, including methods for personalized genome sequencing and new computational techniques for building dynamic disease predictive networks from massive amounts of data from a variety of OMICs. An excellent example of the effectiveness of the P4 medicine approach is the change in cancer treatments. Emphasis is placed on early detection, followed by genotyping of the patient to use the most adequate treatment according to the genetic background. Cardiovascular diseases and perhaps even neurodegenerative disorders will be the next targets for P4 medicine. The application of P4 medicine to infectious diseases is still in its infancy, but is a promising field that will provide much benefit to both the patients and the health-care system.     

Precision medicine in human heart modeling

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

     

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.     

Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

A critique of race-based and genomic medicine

Now that a composite human genome has been sequenced (HGP), research has accelerated to discover precise genetic bases of several chronic health issues, particularly in the realms of cancer and cardiovascular disease. It is anticipated that in the future it will be possible and cost effective to regularly sequence individual genomes, and thereby produce a DNA profile that potentially can be used to assess the health risks for each person with respect to certain genetically predisposed conditions. Coupled with that enormous diagnostic power, it will then depend upon equally rapid research efforts to develop personalized courses of treatment, including that of pharmaceutical therapy. Initial treatment attempts have been made to match drug efficacy and safety to individuals of assigned or self-identified groups according to their genetic ancestry or presumed race. A prime example is that of BiDil, which was the first drug approved by the US FDA for the explicit treatment of heart patients of African American ancestry. This race-based approach to medicine has been met with justifiable criticism, notably on ethical grounds that have long plagued historical applications and misuses of human race classification, and also on questionable science. This paper will assess race-based medical research and practice in light of a more thorough understanding of human genetic variability. Additional concerns will be expressed with regard to the rapidly developing area of pharmacogenomics, promoted to be the future of personalized medicine. Genomic epidemiology will be discussed with several examples of on-going research that hopefully will provide a solid scientific grounding for personalized medicine to build upon.     

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.     

Pathology,proteomics and the pathway to personalised medicine

Introduction: As we move from a discovery to a translational phase in proteomics, with a focus on developing validated clinical assays to assist personalized medicine, there is a growing need for strong bidirectional interactions with the clinical pathology community. Thus, while on one hand the proteomics community will provide candidate biomarkers to assist in diagnosis, prognosis, surveillance, identification of individualized patient medication, and development and validation of new assays for diagnostic use, on the other the pathology community will assist with specific tissue identification and selection (e.g. laser capture microdissection, tissue sections for MS imaging), biobanking, validation of emerging automated histopathology techniques, preparation and classification of relevant patient medical reports, and assisting with the optimization of experimental design for clinical trials.

Areas covered: Here we discuss these topics with a particular emphasis on recent publications and relevant initiatives and outline some of the hurdles that still remain for personalized medicine.

Expert commentary: It is clear that effective crosstalk between the proteomics and pathology communities will greatly accelerate crossover of candidate biomarkers to personalized medicine, which will have significant benefits not only for patient wellbeing, but also the global healthcare budget. However, analysis of the big data generated may become rate-limiting.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer’s and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.     

Editorial: Systems biology and personalized medicine - the future is now

This special issue on "Systems biology and personalized medicine" includes five complementary articles that highlight how functional genomics and computational physiology can contribute to the development of predictive, preventive, personalized and participatory (P4) medicine. Edited by Prof. Leroy Hood and Prof. Charles Auffray.     

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.     

权重基因共表达网络分析在生物医学中的应用

高通量生物监测方法可以同时检测同一样本的上千个参数,其在生物医学中的应用越来越广泛,但如何系统地分析并从高通量数据中挖掘有用信息,仍是一项重要的课题。网络生物学的出现使人们对复杂生物系统有了更深刻的理解,组织/细胞功能执行具有模块化特点。目前,相关网络(Correlation network)被越来越多地应用于生物信息学,权重基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)是描述样品基因表达相关模式的一种系统生物学工具。在此,对WGCNA在疾病分型及预后、发病机制和其他相关领域研究进展作一个较为系统的综述。首先,对WGCNA的原理、分析流程和优势缺点进行总结。其次,介绍如何用WGCNA研究疾病、正常组织、药物、进化和基因组注释。最后,结合新高通量技术展望WGCNA应用新空间。以期科研工作者能够对WGCNA的应用有所了解。