Top Three Pharmacogenomics and Personalized Medicine Applications at the Nexus of Renal Pathophysiology and Cardiovascular Medicine.

Pharmacogenomics is a field with origins in the study of monogenic variations in drug metabolism in the 1950s. Perhaps because of these historical underpinnings, there has been an intensive investigation of 'hepatic pharmacogenes' such as CYP450s and liver drug metabolism using pharmacogenomics approaches over the past five decades. Surprisingly, kidney pathophysiology, attendant diseases and treatment outcomes have been vastly under-studied and under-theorized despite their central importance in maintenance of health, susceptibility to disease and rational personalized therapeutics. Indeed, chronic kidney disease (CKD) represents an increasing public health burden worldwide, both in developed and developing countries. Patients with CKD suffer from high cardiovascular morbidity and mortality, which is mainly attributable to cardiovascular events before reaching end-stage renal disease. In this paper, we focus our analyses on renal function before end-stage renal disease, as seen through the lens of pharmacogenomics and human genomic variation. We herein synthesize the recent evidence linking selected Very Important Pharmacogenes (VIP) to renal function, blood pressure and salt-sensitivity in humans, and ways in which these insights might inform rational personalized therapeutics. Notably, we highlight and present the rationale for three applications that we consider as important and actionable therapeutic and preventive focus areas in renal pharmacogenomics: 1) ACE inhibitors, as a confirmed application, 2) VDR agonists, as a promising application, and 3) moderate dietary salt intake, as a suggested novel application. Additionally, we emphasize the putative contributions of gene-environment interactions, discuss the implications of these findings to treat and prevent hypertension and CKD. Finally, we conclude with a strategic agenda and vision required to accelerate advances in this under-studied field of renal pharmacogenomics with vast significance for global public health.

Genomik und personalisierte Medizin [Genomics and personalized medicine].

Personalized medicine has a substantial potential to transform the way diseases will be predicted, prevented and treated. The field will greatly benefit from novel DNA sequencing technologies, in particular commoditization of individual whole genome sequencing. This evolution cannot be stopped, and the medical and scientific community, as well as the society at large, have the responsibility to anticipate the expected benefits from this revolution, but also the potential risks associated with it. Massive investments will be needed for the potential of personalized medicine to be realized, and for the field to come to maturity. In particular, a paradigm change in the way clinical research is done is needed. Switzerland and its Western part pro-actively anticipate these changes.

MGMT promoter methylation in malignant gliomas: ready for personalized medicine?

The DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT) antagonizes the genotoxic effects of alkylating agents. MGMT promoter methylation is the key mechanism of MGMT gene silencing and predicts a favorable outcome in patients with glioblastoma who are exposed to alkylating agent chemotherapy. This biomarker is on the verge of entering clinical decision-making and is currently used to stratify or even select glioblastoma patients for clinical trials. In other subtypes of glioma, such as anaplastic gliomas, the relevance of MGMT promoter methylation might extend beyond the prediction of chemosensitivity, and could reflect a distinct molecular profile. Here, we review the most commonly used assays for evaluation of MGMT status, outline the prerequisites for standardized tests, and evaluate reasons for difficulties in reproducibility. We critically discuss the prognostic and predictive value of MGMT silencing, reviewing trials in which patients with different types of glioma were treated with various chemotherapy schedules, either up-front or at recurrence. Standardization of MGMT testing requires comparison of different technologies across laboratories and prospectively validated cut-off values for prognostic or predictive effects. Moreover, future clinical trials will need to determine, for each subtype of glioma, the degree to which MGMT promoter methylation is predictive or prognostic, and whether testing should become routine clinical practice.

Should biomarkers be used to design personalized medicine for the treatment of glioblastoma?

Significant progress has been made in understanding the molecular pathogenesis of gliomas and in predicting general outcome depending on a limited set of clinical parameters and molecular markers. However, methylation of the O⁶-methylguanine DNA methyltransferase (MGMT) gene promoter is the only molecular marker linked to sensitivity of a specific treatment, that is, alkylating agent chemotherapy, and this predictive value may be limited to glioblastoma. Moreover, in the absence of potent alternative drugs, temozolomide chemotherapy should not be withheld from patients with newly diagnosed glioblastoma without MGMT promoter methylation in general practice. In the context of clinical trials, however, irrespective of whether classical cytotoxic drugs, tyrosine kinase inhibitors or antiangiogenic agents are used, tissue should be centrally collected. Appropriate research programs should seek to define enriched patient populations for future trials and ultimately facilitate individualized cancer treatments.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Médecine génomique et Maladies infectieuses [Genomic medicine and infectious diseases].

Relentless progress in our knowledge of the nature and functional consequences of human genetic variation allows for a better understanding of the protracted battle between pathogens and their human hosts. Multiple polymorphisms have been identified that impact our response to infections or to anti-infective drugs, and some of them are already used in the clinic. However, to make personalized medicine a reality in infectious diseases, a sustained effort is needed not only in research but also in genomic education.

Pathology as the Cornerstone of Human Tissue Banking: European Consensus Expert Group Report

Aside from ethical considerations, the primary requirement for usage of human tissues in basic or translational research is the thorough characterization of tissues. The second, but equally essential, requirement is that tissues be collected, processed, annotated, and preserved in optimal conditions. These requirements put the pathologist at the center of tissue banking activities and of research aimed at discovering new biomarkers. Pathologists not only provide information identifying the specimen but also make decisions on what materials should be biobanked, on the preservation conditions, and on the timeline of events that precede preservation and storage. This central position calls for increased recognition of the role of the pathologist by the biomolecular community and places new demands on the pathologist's workload and scope of scientific activities. These questions were addressed by an Expert Group Meeting of the European Biological and Biomolecular Research Infrastructure (BBMRI). While detailed recommendations are published elsewhere (Bevilacqua et al., Virchows Archivs, 2010, in press), this article outlines the strategic and technological issues identified by the Expert Group and identifies ways forward for better integration of pathology in the current thrust for development of biomarker-based "personalized medicine.

New frontiers in therapeutic resistance in cancer.

Despite advances in personalized medicine and targeted therapies, therapeutic resistance remains a persistent dilemma encountered by clinicians, scientists and patients. In this article we summarize the highlights of the third Quebec Conference on Therapeutic Resistance in Cancer. This unique meeting provided researchers and clinicians with insights into: intrinsic and acquired resistance; tumor heterogeneity; complexities of biomarker-driven trials; challenges of 'omics data analysis; and models of clinical applications of personalized medicine. Emphasized throughout the conference was the importance of collaborations - between industry and academia, and between basic researchers and clinicians - so that therapeutic resistance can be studied where it matters most, in patients.

The future key role of LC-high-resolution-MS analyses in clinical laboratories: a focus on quantification.

For the last decade, high-resolution (HR)-MS has been associated with qualitative analyses while triple quadrupole MS has been associated with routine quantitative analyses. However, a shift of this paradigm is taking place: quantitative and qualitative analyses will be increasingly performed by HR-MS, and it will become the common 'language' for most mass spectrometrists. Most analyses will be performed by full-scan acquisitions recording 'all' ions entering the HR-MS with subsequent construction of narrow-width extracted-ion chromatograms. Ions will be available for absolute quantification, profiling and data mining. In parallel to quantification, metabotyping will be the next step in clinical LC-MS analyses because it should help in personalized medicine. This article is aimed to help analytical chemists who perform targeted quantitative acquisitions with triple quadrupole MS make the transition to quantitative and qualitative analyses using HR-MS. Guidelines for the acceptance criteria of mass accuracy and for the determination of mass extraction windows in quantitative analyses are proposed.

Les tests génétiques peuvent-ils être utiles pour la prévention cardiovasculaire? [Are genetic tests useful for cardiovascular prevention?].

Recent progresses in genetics have opened new avenues to further our understanding of the pathophysiological mechanisms underlying cardiovascular disease, raising, new expectations in the field of personalized medicine. Genetic tests may have a high predictive value for rare monogenic diseases. The situation is very different for common polygenic diseases, such as myocardial infarction, type 2 diabetes or stroke. The results from recent genome-wide association studies have provided useful information for research, but have not yet been proven to be clinically useful. It is therefore currently not recommended to conducted genetic testing to guide cardiovascular prevention neither in clinical nor in public health settings.

Gene-environment interactions of selected pharmacogenes in arterial hypertension.

Hypertension affects approximately 1 billion people worldwide. Owing to population aging, hypertension-related cardiovascular burden is expected to rise in the near future. In addition to genetic variants influencing the blood pressure response to antihypertensive drugs, several genes encoding for drug-metabolizing or -transporting enzymes have been associated with blood pressure and/or hypertension in humans (e.g., ACE, CYP1A2, CYP3A5, ABCB1 and MTHFR) regardless of drug treatment. These genes are also involved in the metabolism and transport of endogenous substances and their effects may be modified by selected environmental factors, such as diet or lifestyle. However, little is currently known on the complex interplay between environmental factors, endogenous factors, genetic variants and drugs on blood pressure control. This review will discuss the respective role of population-based primary prevention and personalized medicine for arterial hypertension, taking a pharmacogenomics' perspective focusing on selected pharmacogenes.

Conceptos generales y métodos de estudio en farmacogenética [General concepts and study methods in pharmacogenetics.]

Pharmacogenetics, the study of how individual genetic profiles influence the response to drugs, is an important topic. Results from pharmacogenetics studies in various clinical settings may lead to personalized medicine. Herein, we present the most important concepts of this discipline, as well as currently-used study methods.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.