Biobanking and public health: is a human rights approach the tie that binds?

Ethical principles guiding public health and genomic medicine are often at odds: whereas public health practice adopts collectivist principles that emphasize population-based benefits, recent advances in genomic and personalized medicine are grounded in an individualist ethic that privileges informed consent, and the balancing of individual risk and benefit. Indeed, the attraction of personalized medicine is the promise it holds out to help individuals get the “right medicine for the right problem at the right time.” Research biobanks are an effective tool in the genomic medicine toolbox. Biobanking in public health presents a unique case study to unpack some of these issues in more detail. For example, there is a long history of using banked tissue obtained under clinical diagnostic conditions for later public health uses. But despite the collectivist approach of public health, the principles applied to the ethical challenges of biobanking (e.g. informed consent, autonomy, privacy) remain individualist. We demonstrate the value of using human rights as a public health ethics framework to address this tension in biobanking by applying it to two illustrative cases.     

How the Electronic Health Record Will Change the Future of Health Care

Genetic testing is expected to play a critical role in patient care in the near future. Advances in genomic research have the potential to impact medicine in very tangible and direct ways, from carrier screening to disease diagnosis and prognosis to targeted treatments and personalized medicine. However, numerous barriers to widespread adoption of genetic testing continue to exist, and health information technology will be a critical means of addressing these challenges. Electronic health records (EHRs) are a digital replacement for the traditional paper-based patient chart designed to improve the quality of patient care. EHRs have become increasingly essential to managing the wealth of existing clinical information that now includes genetic information extracted from the patient genome. The EHR is capable of changing health care in the future by transforming the way physicians use genomic information in the practice of medicine.     

Personalized medicine: new genomics,old lessons

Personalized medicine uses traditional, as well as emerging concepts of the genetic and environmental basis of disease to individualize prevention, diagnosis and treatment. Personalized genomics plays a vital, but not exclusive role in this evolving model of personalized medicine. The distinctions between genetic and genomic medicine are more quantitative than qualitative. Personalized genomics builds on principles established by the integration of genetics into medical practice. Principles shared by genetic and genomic aspects of medicine, include the use of variants as markers for diagnosis, prognosis, prevention, as well as targets for treatment, the use of clinically validated variants that may not be functionally characterized, the segregation of these variants in non-Mendelian as well as Mendelian patterns, the role of gene–environment interactions, the dependence on evidence for clinical utility, the critical translational role of behavioral science, and common ethical considerations. During the current period of transition from investigation to practice, consumers should be protected from harms of premature translation of research findings, while encouraging the innovative and cost-effective application of those genomic discoveries that improve personalized medical care.     

Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

Realizing personalized medicine requires integrating diverse data types with bioinformatics. The most vital data are genomic information for individuals that are from advanced next-generation sequencing (NGS) technologies at present. The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data. The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike. Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine. We review some key bioinformatics tasks, issues, and challenges in contexts of IT requirements, data quality, analysis tools and pipelines, and validation of biomarkers.     

Dawn of ocular gene therapy: implications for molecular diagnosis in retinal disease

Personalized medicine aims to utilize genomic information about patients to tailor treatment. Gene replacement therapy for rare genetic disorders is perhaps the most extreme form of personalized medicine, in that the patients’ genome wholly determines their treatment regimen. Gene therapy for retinal disorders is poised to become a clinical reality. The eye is an optimal site for gene therapy due to the relative ease of precise vector delivery, immune system isolation, and availability for monitoring of any potential damage or side effects. Due to these advantages, clinical trials for gene therapy of retinal diseases are currently underway. A necessary precursor to such gene therapies is accurate molecular diagnosis of the mutation(s) underlying disease. In this review, we discuss the application of Next Generation Sequencing (NGS) to obtain such a diagnosis and identify disease causing genes, using retinal disorders as a case study. After reviewing ocular gene therapy, we discuss the application of NGS to the identification of novel Mendelian disease genes. We then compare current, array based mutation detection methods against next NGS-based methods in three retinal diseases: Leber’s Congenital Amaurosis, Retinitis Pigmentosa, and Stargardt’s disease. We conclude that next-generation sequencing based diagnosis offers several advantages over array based methods, including a higher rate of successful diagnosis and the ability to more deeply and efficiently assay a broad spectrum of mutations. However, the relative difficulty of interpreting sequence results and the development of standardized, reliable bioinformatic tools remain outstanding concerns. In this review, recent advances NGS based molecular diagnoses are discussed, as well as their implications for the development of personalized medicine.     

Should direct-to-consumer personalized genomic medicine remain unregulated?: a rebuttal of the defenses

Direct-to-consumer personalized genomic medicine has recently grown into a small industry that sells mail-order DNA sample kits and then provides disease risk assessments, typically based upon results from genome-trait association studies. The companies selling these services have been largely exempted from FDA regulation in the United States. Testing kit companies and their supporters have defended the industry's unregulated status using two arguments. First, defenders have argued that mere absence of harm is all that must be proved for mail-order tests to be acceptable. Second, defenders of mail-order testing have argued that there is an individual right to the tests' information. This article rebuts these arguments. The article demonstrates that the direct-to-consumer market has resulted in the sidelining of clinical utility (medical value to patients), leading to the development of certain mail-order tests that do not promote customers' interests and to defenders' downplaying of a potentially damaging empirical study of mail-order genomic testing's effects on consumers. The article also shows that the notion of an individual right to these tests rests on a flawed reading of the key service provided by mail-order companies, which is the provision of medical interpretations, not simply genetic information. Absent these two justifications, there is no reason to exempt direct-to-consumer personalized genomic medicine from stringent federal oversight.     

Genomic medicine and neurological disease

“Genomic medicine” refers to the diagnosis, optimized management, and treatment of disease—as well as screening, counseling, and disease gene identification—in the context of information provided by an individual patient’s personal genome. Genomic medicine, to some extent synonymous with “personalized medicine,” has been made possible by recent advances in genome technologies. Genomic medicine represents a new approach to health care and disease management that attempts to optimize the care of a patient based upon information gleaned from his or her personal genome sequence. In this review, we describe recent progress in genomic medicine as it relates to neurological disease. Many neurological disorders either segregate as Mendelian phenotypes or occur sporadically in association with a new mutation in a single gene. Heritability also contributes to other neurological conditions that appear to exhibit more complex genetics. In addition to discussing current knowledge in this field, we offer suggestions for maximizing the utility of genomic information in clinical practice as the field of genomic medicine unfolds.     

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.     

二代基因测序数据管理和大数据平台在精准医学中的应用

精准医学集合了多种数据,包括组学、临床、环境和行为等,是对疾病进行个性化治疗、预防和管理的科学。随着基因测序费用的大幅下降,人们对肿瘤等疾病的认识从传统病理到分子水平的飞跃等,相关科学的发展和普及推动了精准医学的诞生和发展,将更加深远地影响着人类的健康。本文介绍了精准医学的概念、目的及应用,介绍了二代DNA测序技术在精准医学中的应用,认为基因组学数据、样本管理、数据质量控制标准以及数据管理平台等是实现精准医学的基础,智能化精准医疗将是来的发展方向。进行展望的同时,也认为基因组学海量数据的规模特点、各种健康应用在推动数据管理平台的发展的同时,也对其演进提出了挑战。     

The implications of electronic health record for personalized medicine

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.     

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.     

Pharmacogenomics of systemic antifungal agents

The genomic era offers a multitude of new technologies that may make the promise of personalized medicine a reality for patients in this century. Numerous new antifungal agents have been developed over the past two decades, but use of these agents requires optimization of pharmacokinetics and dosing to achieve efficacy and minimize toxicity. This article reviews the potential application of pharmacogenomics to the use of antifungal agents, highlighting genetic variation that may affect absorption, distribution, metabolism, and elimination of these compounds.     

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.     

Cis-regulatory variation: significance in biomedicine and evolution

Cis-regulatory regions (CRR) control gene expression and chromatin modifications. Genetic variation at CRR in individuals across a population contributes to phenotypic differences of biomedical relevance. This standing variation is important for personalized genomic medicine as well as for adaptive evolution and speciation. This review focuses on genetic variation at CRR, its influence on chromatin, gene expression, and ultimately disease phenotypes. In addition, we summarize our understanding of how this variation may contribute to evolution. Recent technological and computational advances have accelerated research in the direction of personalized medicine, combining strengths of molecular biology and genomics. This will pave new ways to understand how CRR variation affects phenotypes and chart out possible avenues of intervention.     

Reading the runes of my genome: a personal exploration of retail genetics

Since 2007, retail genetic companies have offered personal genome scans: DNA testing based on single nucleotide polymorphisms (SNPs) which are interpreted to provide genetic risks of some common diseases and trait information. There is much discussion of the validity, benefits and risks of this testing, and its ethics and regulation, but little information about the experiences of those who have purchased this testing. This paper offers an autobiographical ethnography, describing the author's use of genome scans purchased from deCODEme and 23andMe. The genetic disease risks provided were generally very modest and there were significant variations in the risks from the two companies. Risks were skeptically interpreted through a frame of knowledge of family disease histories. It is suggested that this personal medicine is likely to disappoint and it does not live up to its claims. Rather than being empowering personalized medicine, these scans are a geneticized medicine of the genomic person.     

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/     

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.     

From evidence-based medicine to genomic medicine

The concept of ‘evidence-based medicine’ dates back to mid-19th century or even earlier. It remains pivotal in planning, funding and in delivering the health care. Clinicians, public health practitioners, health commissioners/purchasers, health planners, politicians and public seek formal ‘evidence’ in approving any form of health care provision. Essentially ‘evidence-based medicine’ aims at the conscientious, explicit and judicious use of the current best evidence in making decisions about the care of individual patients. It is in fact the ‘personalised medicine’ in practice. Since the completion of the human genome project and the rapid accumulation of huge amount of data, scientists and physicians alike are excited on the prospect of ‘personalised health care’ based on individual’s genotype and phenotype. The first decade of the new millennium now witnesses the transition from ‘evidence-based medicine’ to the ‘genomic medicine’. The practice of medicine, including health promotion and prevention of disease, stands now at a wide-open road as the scientific and medical community embraces itself with the rapidly expanding and revolutionising field of genomic medicine. This article reviews the rapid transformation of modern medicine from the ‘evidence-based medicine’ to ‘genomic medicine’.