The path to personalized medicine

Advances in personalized medicine, or the use of an individual's molecular profile to direct the practice of medicine, have been greatly enabled through human genome research. This research is leading to the identification of a range of molecular markers for predisposition testing, disease screening and prognostic assessment, as well as markers used to predict and monitor drug response. Successful personalized medicine research programs will not only require strategies for developing and validating biomarkers, but also coordinating these efforts with drug discovery and clinical development.     

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.     

基因组编辑技术在干细胞疾病模型建立和精准医疗中的应用

精准医疗强调针对不同个体定制个性化治疗方案,其推行需要精准疾病模型的建立。人类干细胞因其具有多能性而成为体外不同类型的成体细胞和器官小体的潜在来源,其强增殖能力保证了充足原材料用于科研分析和大规模药物筛选。基因组编辑技术(尤其是CRISPR/Cas9技术)的快速发展使得在人多能干细胞和成体干细胞中进行高效基因组编辑成为可能。两者的有效结合能建立起针对不同遗传致病背景的“个性化”疾病模型,有利于深入解析不同遗传突变的致病机制和开发高针对性的精准医疗方案。本文对基因组编辑技术在人类干细胞中的应用以及利用干细胞疾病模型模拟罕见病和肿瘤发生的研究进行了综述。     

Proteomics and personalized medicine: a focus on kidney disease

ABSTRACT

Introduction: Inter-individual variability in response to drug treatment has induced an increased demand for decisions via personalize medicine. Also, the contribution of proteomics to the era of personalized medicine would seem to be vital in improving therapeutic outcomes.

Areas covered: We review validated biomarkers discovered by proteomics techniques and their use in personalized medicine with the focus on kidney diseases. We discuss this topic with a special emphasis on recent publications and relevant initiatives and depict some limitations that remain for personalized medicine.

Expert opinion: The development of highly accurate biomarkers is essential for optimizing the management of kidney diseases. Various biomarkers of kidney diseases have been identified using proteomic techniques. However, only a few of these biomarkers showed the potential to be used in clinical practice concerning personalized medicine. Therefore, it becomes evident that the combination of multiple biomarkers confers higher accuracy and the ability to depict complex pathophysiological conditions, a prerequisite for personalized treatment. CKD273, a multimarker panel for early CKD detection may serve as a first example for personalized medicine in nephrology. Based on this successful example, proteomics is expected to develop into the key technology to guide personalized intervention.     

Clinical laboratory testing in human medicine based on the detection of glycoconjugates

The purpose of this review is to provide a concise overview of developments over the last 15 years in the field of laboratory tests in human medicine that are based on the detection of alterations in the glycan part of glycoconjugates. We show how glycosylation-based diagnostic testing is widespread in the current clinical practice, in different formats. To provide the necessary focus in this extremely broad field, we have only included assays that are either in actual clinical use or that are under active development towards clinical use, with some bias towards assays that were recently developed. The fields included are: cancer, infectious disease, genetic defects of glycoconjugate biosynthesis and catabolism, auto-immunity, drug abuse and liver disease. To conclude this review, we provide a viewpoint on the future of the glyco-diagnostics field in terms of novel technologies, especially with regard to the discovery and clinical implementation of biomarkers that are based on pathologically altered endogenous glycotopes.     

基因组编辑技术在精准医学中的应用

基因组编辑技术的飞速发展,尤其是近年来CRISPR/Cas9基因组编辑体系的出现,使得研究人员能高效地在细胞系和动物模型中对基因组进行精确编辑。基于基因组编辑技术的各种实验研究平台被相继开发,包括通过在细胞系中引入疾病相关突变位点建立疾病模型,通过高通量筛选寻找导致肿瘤耐药性的突变基因,通过体内原位靶向致病基因并修改突变进行基因治疗等。这些基因组编辑技术研究平台极大推动了精准医学研究领域的发展。本文对基因组编辑技术在精准医学领域的基础研究、转化应用、目前存在的问题以及未来发展的方向进行了讨论。     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Personalized diagnostics and biosensors: a review of the biology and technology needed for personalized medicine

Abstract

Exploiting the burgeoning fields of genomics, proteomics and metabolomics improves understanding of human physiology and, critically, the mutations that signal disease susceptibility. Through these emerging fields, rational design approaches to diagnosis, drug development and ultimately personalized medicine are possible. Personalized medicine and point-of-care testing techniques must fulfill a host of constraints for real-world applicability. Point-of-care devices (POCDs) must ultimately provide a cost-effective alternative to expensive and time-consuming laboratory tests in order to assist health care personnel with disease diagnosis and treatment decisions. Sensor technologies are also expanding beyond the more traditional classes of biomarkers – nucleic acids and proteins – to metabolites and direct detection of pathogens, ultimately increasing the palette of available techniques for the use of personalized medicine. The technologies needed to perform such diagnostics have also been rapidly evolving, with each generation being increasingly sensitive and selective while being more resource conscious. Ultimately, the final hurdle for all such technologies is to be able to drive consumer adoption and achieve a meaningful medical outcome for the patient.     

Proteomics in clinical interventions: achievements and limitations in biomarker development

Development of toxicological and clinical biomarkers for disease diagnosis, quantification of toxicant/drug responses and rapid patient care are major concerns in modern biology. Even after human genome sequencing, identification of specific molecular signatures for unambiguous correlation with toxicity and clinical interventions is a challenging task. Differential protein expression patterns and protein-protein interaction studies have started unraveling rigorous molecular explanation of multi-factorial and toxicant borne diseases. Proteome profiling is extensively used to investigate etiology of diseases, develop predictive biomarkers for toxicity and therapeutic interventions and potential strategies for treatment of complex and toxicant mediated diseases. In this review, achievements and limitations of proteomics in developing predictive biomarkers for toxicological and clinical interventions have been discussed.     

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.     

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.

     

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.     

First principles of Hamiltonian medicine

We introduce the field of Hamiltonian medicine, which centres on the roles of genetic relatedness in human health and disease. Hamiltonian medicine represents the application of basic social-evolution theory, for interactions involving kinship, to core issues in medicine such as pathogens, cancer, optimal growth and mental illness. It encompasses three domains, which involve conflict and cooperation between: (i) microbes or cancer cells, within humans, (ii) genes expressed in humans, (iii) human individuals. A set of six core principles, based on these domains and their interfaces, serves to conceptually organize the field, and contextualize illustrative examples. The primary usefulness of Hamiltonian medicine is that, like Darwinian medicine more generally, it provides novel insights into what data will be productive to collect, to address important clinical and public health problems. Our synthesis of this nascent field is intended predominantly for evolutionary and behavioural biologists who aspire to address questions directly relevant to human health and disease.     

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.     

Decoding microRNA drivers in atherosclerosis

An estimated 97% of the human genome consists of non-protein-coding sequences. As our understanding of genome regulation improves, this has led to the characterization of a diverse array of non-coding RNAs (ncRNA). Among these, micro-RNAs (miRNAs) belong to the short ncRNA class (22–25 nucleotides in length), with approximately 2500 miRNA genes encoded within the human genome. From a therapeutic perspective, there is interest in exploiting miRNA as biomarkers of disease progression and response to treatments, as well as miRNA mimics/repressors as novel medicines. miRNA have emerged as an important class of RNA master regulators with important roles identified in the pathogenesis of atherosclerotic cardiovascular disease. Atherosclerosis is characterized by a chronic inflammatory build-up, driven largely by low-density lipoprotein cholesterol accumulation within the artery wall and vascular injury, including endothelial dysfunction, leukocyte recruitment and vascular remodelling. Conventional therapy focuses on lifestyle interventions, blood pressure-lowering medications, high-intensity statin therapy and antiplatelet agents. However, a significant proportion of patients remain at increased risk of cardiovascular disease. This continued cardiovascular risk is referred to as residual risk. Hence, a new drug class targeting atherosclerosis could synergise with existing therapies to optimise outcomes. Here, we review our current understanding of the role of ncRNA, with a focus on miRNA, in the development and progression of atherosclerosis, highlighting novel biological mechanisms and therapeutic avenues.     

Molecular diagnostics and personalized medicine in oncology: Challenges and opportunities

Increasing evidence demonstrates that target‐based agents are active only in molecularly selected populations of patients. Therefore, the identification of predictive biomarkers has become mandatory to improve the clinical development of these novel drugs. Mutations of the epidermal growth factor receptor (EGFR) or rearrangements of the ALK gene in non‐small‐cell lung cancer, and BRAF mutations in melanoma are clear examples of driver mutations and predictive biomarkers of response to treatment with specific inhibitors. Predictive biomarkers might also identify subgroups of patients that are not likely to respond to specific drugs, as shown for KRAS mutations and anti‐EGFR monoclonal antibodies in colorectal carcinoma. The discovery of novel driver molecular alterations and the availability of drugs capable to selectively block such oncogenic mechanisms are leading to a rapid increase in the number of putative biomarkers that need to be assessed in each single patient. In this respect, two different approaches are being developed to introduce a comprehensive molecular characterization in clinical practice: high throughput genotyping platforms, which allow the detection of recognized genetic aberrations in clinical samples, and next generation sequencing that can provide information on all the different types of cancer‐causing alterations. The introduction of these techniques in clinical practice will increase the possibility to identify molecular targets in each individual patient, and will also allow to follow the molecular evolution of the disease during the treatment. By using these approaches, the development of personalized medicine for patients with cancer will finally become possible. J. Cell. Biochem. 114: 514–524, 2013. © 2012 Wiley Periodicals, Inc.     

Closing the ‘phenotype gap’ in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has “co-clinical” modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal ‘models’ and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of ‘phenotype gap.’ In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.