Prediction of personalized drugs based on genetic variations provided by DNA sequencing technologies

Recent reports of death and illness caused by adverse drug reactions have boosted rational drug design research. It has been shown through sequencing of the entire human genome that human genetic variations play a key role in adverse reactions to drugs as well as in differences in the effectiveness of drug treatments. The advent of high-throughput DNA sequencing technologies with bioinformatics of system biology have allowed the easy identification of genetic variations and all other pharmacogenetic variants in a single assay, thus permitting truly personalized drug treatment. This would be particularly valuable for many patients with chronic diseases who must take many medications concurrently. In this review, we have focused on pharmacogenomics for the prediction of variable drug responses between individuals with relevant genetic variations through new DNA sequencing technologies and provided directions for personalized drug therapy in the future.     

Pharmacogenomics and systems biology of membrane transporters

Membrane transporters are essential for fundamental cellular functions and normal physiological processes. These molecules influence drug absorption and distribution, and play key roles in drug therapeutic effects. A primary goal of current research in drug discovery and development is to fully understand the interaction between transporters and drugs at both system level and individual level for personalized therapy. Pharmacogenomics studies the genetic basis of the individual variations in response to drug therapy, whereas systems biology provides the understanding of biological processes at the system level. The integration of pharmacogenomics with systems biology in membrane transporter study is necessary to solve complex problems in diseases and drug effects. Such integration provides insight to key issues of pharmacogenomics and systems biology of membrane transporters. These key issues include the correlations between structure and function, genotype and phenotype, and systematic interactions between different transporters, between transporters and other proteins, and between transporters and drugs. The exploration in these key issues may ultimately contribute to the personalized medicine with high efficacy but less toxicity, which is the overall goal of pharmacogenomics and systems biology.     

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.     

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.     

Genetic polymorphism of pharmacogenomic VIP variants in the Deng people from the Himalayas in Southeast Tibet

Abstract

Little is known about polymorphic distribution of pharmacogenes among ethnicities, including the Deng people. In this study, we recruited 100 unrelated, healthy Deng people and genotyped them with respect to 76 different single-nucleotide polymorphisms by the PharmGKB database. Our results first indicated that the polymorphic distribution of pharmacogenes of the Deng people is most similar to CHD, suggesting that Deng people have a closest genetic relationship with CHD. Our data will enrich the database of pharmacogenomics and provide a theoretical basis for safer drug administration and individualized treatment plans, promoting the development of personalized medicine.     

An Exponential Combination Procedure for Set-Based Association Tests in Sequencing Studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives—that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk—a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.     

Role and implications of nanodiagnostics in the changing trends of clinical diagnosis

Nanodiagnostics is the term used for the application of nanobiotechnology in molecular diagnosis, which is important for developing personalized cancer therapy. It is usually based on pharmacogenetics, pharmacogenomics, and pharmacoproteomic information but also takes into consideration environmental factors that influence response to therapy. Nanotechnology in medicine involves applications of nanoparticles currently under development, as well as longer range research that involves the use of manufactured nano-robots to make repairs at the cellular level. Nanodiagnostic technologies are also being used to refine the discovery of biomarkers, as nanoparticles offer advantages of high volume/surface ratio and multifunctionality. Biomarkers are important basic components of personalized medicine and are applicable to the management of cancer as well. The field of nano diagnostics raises certain ethical concerns related with the testing of blood. With advances in diagnostic technologies, doctors will be able to give patients complete health checks quickly and routinely. If any medication is required this will be tailored specifically to the individual based on their genetic makeup, thus preventing unwanted side-effects.     

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.     

NutriTRAILomics in prostate cancer: time to have two strings to one’s bow

The astonishing development of broad genomics and proteomics tools have catalyzed a new era in both therapeutic interventions and nutrition in prostate cancer. The terms pharmacogenomics and nutrigenomics have been derived out of their genetic forbears as large-scale genomics technologies have been established in the last decade. It is unquestionable that rationale of both disciplines is to individualize or personalize medicine and food and nutrition, and eventually health, by tailoring the drug or the food to the individual genotype. The purpose of this review is to significantly inspect results from current research concerning the mechanisms of action of phytonutrients and potential effects on prostate cancer. Substantial emerging data supports the synergistic adiministration of nutraceuticals with TRAIL in prostate cancer progression to circumvent TRAIL refractoriness. Nonetheless, developing novel scientific methods for discovery, validation, characterization and standardization of these multicomponent phyto-therapeutics is vital to their recognition into mainstream medicine. The key to interpret a personalized response is a greater comprehension of nutrigenomics, proteomics and metabolomics.     

Pharmacogenetics and pharmacogenomics: role of mutational analysis in anti-cancer targeted therapy

The goal of cancer pharmacogenomics is to obtain benefit from personalized approaches of cancer treatment and prevention. Recent advances in genomic research have shed light on the crucial role of genetic variants, mainly involving genes encoding drug-metabolizing enzymes, drug transporters and targets, in driving different treatment responses among individuals, in terms of therapeutic efficacy and safety. Although a considerable amount of new targeted agents have been designed based on a finely understanding of molecular alterations in cancer, a wide gap between pharmacogenomic knowledge and clinical application still persists. This review focuses on the relevance of mutational analyses in predicting individual response to antitumor therapy, in order to improve the translational impact of genetic information on clinical practice.     

Functional Analysis of Variance for Association Studies

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.     

Biotechnology,Big Data and Artificial Intelligence

Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high‐throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area depend, critically, on the ability of biotechnology researchers to master the skills required to effectively integrate their own contributions with the large amounts of information available in these databases. This article offers a perspective of the relations that exist between the fields of big data and biotechnology, including the related technologies of artificial intelligence and machine learning and describes how data integration, data exploitation, and process optimization correspond to three essential steps in any future biotechnology project. The article also lists a number of application areas where the ability to use big data will become a key factor, including drug discovery, drug recycling, drug safety, functional and structural genomics, proteomics, pharmacogenetics, and pharmacogenomics, among others.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

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.     

Systematic Pharmacogenomics Analysis of a Malay Whole Genome: Proof of Concept for Personalized Medicine

Background

With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.

Methods

Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject''s mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.

Principal Findings

Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.

Conclusions

The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

Assessing gene-gene interactions in pharmacogenomics

In pharmacogenomics studies, gene-gene interactions play an important role in characterizing a trait that involves complex pharmacokinetic and pharmacodynamic mechanisms, particularly when each involved feature only demonstrates a minor effect. In addition to the candidate gene approach, genome-wide association studies (GWAS) are widely utilized to identify common variants that are associated with treatment response. In the wake of recent advances in scientific research, a paradigm shift from GWAS to whole-genome sequencing is expected, because of the reduced cost and the increased throughput of next-generation sequencing technologies. This review first outlines several promising methods for addressing gene-gene interactions in pharmacogenomics studies. We then summarize some candidate gene studies for various treatments with consideration of gene-gene interactions. Furthermore, we give a brief overview for the pharmacogenomics studies with the GWAS approach and describe the limitations of these GWAS in terms of gene-gene interactions. Future research in translational medicine promises to lead to mechanistic findings related to drug responsiveness in light of complex gene-gene interactions and will probably make major contributions to individualized medicine and therapeutic decision-making.     

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。     

Human genome project: pharmacogenomics and drug development

Now that all 30,000 or so genes that make up the human genome have been deciphered, pharmaceutical industries are emerging to capitalize the custom based drug treatment. Understanding human genetic variation promises to have a great impact on our ability to uncover the cause of individual variation in response to therapeutics. The study of association between genetics and drug response is called pharmacogenomics. The potential implication of genomics and pharmacogenomics in clinical research and clinical medicine is that disease could be treated according to the interindividual differences in drug disposition and effects, thereby enhancing the drug discovery and providing a stronger scientific basis of each patient's genetic constitution. Sequence information derived from the genomes of many individuals is leading to the rapid discovery of single nucleotide polymorphisms or SNPs. Detection of these human polymorphisms will fuel the discipline of pharmacogenomics by developing more personalized drug therapies. A greater understanding of the way in which individuals with a particular genotype respond to a drug allows manufacturers to identify population subgroups that will benefit most from a particular drug. The increasing emphasis on pharmacogenomics is likely to raise ethical and legal questions regarding, among other things, the design of research studies, the construction of clinical trials and the pricing of drugs.