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.     

Complex genetic diseases: controversy over the Croesus code

The polarization of views on how best to exploit new information from the Human Genome Project for medicine reflects our ignorance of the genetic architecture underlying common diseases: are susceptibility alleles common or rare, neutral or deleterious, few or many? Single-nucleotide polymorphism (SNP) technology is almost in place to dissect such diseases and to create a personalized medicine, but success is critically dependent on the biology and "Nature to be commanded must be obeyed" (Francis Bacon, 1620, Novum Organum).     

Sparse Representation-Based Patient-Specific Diagnosis and Treatment for Esophageal Squamous Cell Carcinoma

Precision medicine and personalized treatment have attracted attention in recent years. However, most genetic medicines mainly target one genetic site, while complex diseases like esophageal squamous cell carcinoma (ESCC) usually present heterogeneity that involves variations of many genetic markers. Here, we seek an approach to leverage genetic data and ESCC knowledge data to forward personalized diagnosis and treatment for ESCC. First, 851 ESCC-related gene markers and their druggability were studied through a comprehensive literature analysis. Then, a sparse representation-based variable selection (SRVS) was employed for patient-specific genetic marker selection using gene expression datasets. Results showed that the SRVS method could identify a unique gene vector for each patient group, leading to significantly higher classification accuracies compared to randomly selected genes (100, 97.17, 100, 100%; permutation p values: 0.0032, 0.0008, 0.0004, and 0.0008). The SRVS also outperformed an ANOVA-based gene selection method in terms of the classification ratio. The patient-specific gene markers are targets of ESCC effective drugs, providing specific guidance for medicine selection. Our results suggest the effectiveness of integrating previous database utilizing SRVS in assisting personalized medicine selection and treatment for ESCC.     

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.     

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.     

Emerging genomic technologies and the concept of personalized medicine: An overview of ethical,legal and social implications

With the completion of the Human Genome Project in May 2006, genetic testing for every American is rapidly becoming a reality. As the advanced technology fuels the path towards personalized medicine, genetic nondiscrimination legislation follows closely behind. It seems that the 2007 Genetic Information Nondiscrimination Act (GINA) will finally pass through both chambers of Congress and will be signed by the President, but questions remain. On May 1, 2008, the House passed GINA by a vote of 414 to 1. Why is this the year that genetic nondiscrimination legislation could finally become the reality? Is this the beginning of a new relationship between science and policy, where policy is finally catching up? We examine the answers to these questions through a look at the history of genetic nondiscrimination legislation and where it stands today, including arguments for and against the bill. We conclude by discussing how we can achieve a future of safe personalized medicine for the populous, which would require continuous productive interactions between policymakers and scientists.     

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.     

Focus: Personalized Medicine: Value of Organoids from Comparative Epithelia Models

Organoids have tremendous therapeutic potential. They were recently defined as a collection of organ-specific cell types, which self-organize through cell-sorting, develop from stem cells, and perform an organ specific function. The ability to study organoid development and growth in culture and manipulate their genetic makeup makes them particularly suitable for studying development, disease, and drug efficacy. Organoids show great promise in personalized medicine. From a single patient biopsy, investigators can make hundreds of organoids with the genetic landscape of the patient of origin. This genetic similarity makes organoids an ideal system in which to test drug efficacy. While many investigators assume human organoids are the ultimate model system, we believe that the generation of epithelial organoids of comparative model organisms has great potential. Many key transport discoveries were made using marine organisms. In this paper, we describe how deriving organoids from the spiny dogfish shark, zebrafish, and killifish can contribute to the fields of comparative biology and disease modeling with future prospects for personalized medicine.     

BeadArray technology: enabling an accurate,cost-effective approach to high-throughput genotyping

The Human Genome Project has opened the door to personalized medicine, provided that human genetic diversity can be analyzed in a high-throughput and cost-effective way Illumina has developed a genotyping system that combines very high throughput and accuracy with low cost per SNP analysis. The system uses our BeadArray platform, a high level of multiplexing, and modular, scalable automation to meet the requirements for cost-effective, genome-wide linkage disequilibrium studies. As implemented in a high-throughput genotyping service facility at Illumina, the system has a current capacity of one million SNP assays per day and is easily expandable. Each SNP call is associated with a quality score that correlates with accuracy     

Sensing a revolution

New fully integrated biosensors that monitor molecular and physiological parameters throughout our bodies are set to revolutionize medicine and personalized healthcare.     

基因测序技术的发展以及对个体化用药水平提高的影响

自基因测序技术发明之时起,就已开始运用在生命科学的研究中,对揭示生命本质的研究起到了关键作用。基因测序技术的运用推动了生命科学的发展,并由此引申了更多的科学问题;人们对未知领域的渴求又推动了基因测序技术的进步,发展出更高速、更低价的新技术。随着测序技术的逐步应用,临床个体化用药的水平有了极大的提高。基因测序技术目前已经成功应用于遗传基因多态性标志物的筛选中,使基因导向的合理用药成为可能;还成功应用于疾病组织突变位点标志物的筛查中,使肿瘤靶向用药成为可能;在病原体耐药基因突变检测中的应用,使基于细菌或病毒耐药突变的个体化用药成为可能。随着测序技术向更高通量、更高精度、更低成本的方向发展,基于基因检测的个体化健康时代将会到来。     

Pharmacogenomics and personalized medicine in neuropsychiatry

Despite the need for more effective treatments for psychiatric disorders, development of new medications has stalled. Here we discuss the promise of personalized medicine in developing more efficacious and individualized pharmacotherapies that take into account genetic variation and target groups of patients who share biology, not just symptoms.     

肿瘤异质性：精准医学需破解的难题

肿瘤异质性是恶性肿瘤的重要特征,表现为同一种恶性肿瘤不同患者个体之间或者同一患者体内不同部位肿瘤细胞间从基因型到表型上存在的差异.这种差异可表现为不同的遗传背景、不同的病理类型、不同的分化状态、不同的基因突变谱和转录组、蛋白质组表达谱等,体现了恶性肿瘤在演进过程中的高度复杂性和多样性.肿瘤异质性给肿瘤的治疗带来极大的困难,一直是肿瘤发生发展机制研究领域重要的科学问题.本文综述了肿瘤异质性的生物学特征及其可能的形成机制,并对"精准医学"时代如何针对肿瘤异质性设计更为有效的个性化治疗方案进行了思考.     

How accurate can genetic predictions be?

ABSTRACT: BACKGROUND: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. RESULTS: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. CONCLUSIONS: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.     

Ex vivo gene transfer and correction for cell-based therapies

Cell-based therapies are fast-growing forms of personalized medicine that make use of the steady advances in stem cell manipulation and gene transfer technologies. In this Review, I highlight the latest developments and the crucial challenges for this field, with an emphasis on haematopoietic stem cell gene therapy, which is taken as a representative example given its advanced clinical translation. New technologies for gene correction and targeted integration promise to overcome some of the main hurdles that have long prevented progress in this field. As these approaches marry with our growing capacity for genetic reprogramming of mammalian cells, they may fulfil the promise of safe and effective therapies for currently untreatable diseases.     

药物基因组学相关P450和ABCB1多态性及SNP检测技术

药物基因组学（phamacogenomics）是临床检测遗传差异引起药物应答个体性差异的学科,它涉及药物代谢和有害的药物反应的预测等方面的内容。个性化药物和个性化治疗发展的关键条件是能够快速简便的检测出病人的遗传多态性。文章综述了药物基因相关问题,细胞色素酶1）450和ABCB1转运蛋白的遗传多态性以及检测遗传多态性的相关技术。