Personalized medicine using DNA biomarkers: a review

Behavior change is not one size fits all: psychosocial phenotypes of childhood obesity prevention intervention participants

Variability in individuals' responses to interventions may contribute to small average treatment effects of childhood obesity prevention interventions. But, neither the causes of this individual variability nor the mechanism by which it influences behavior are clear. We used qualitative methods to characterize variability in students' responses to participating in a childhood obesity prevention intervention and psychosocial characteristics related to the behavior change process. We interviewed 18 students participating in a school-based curriculum and policy behavior change intervention. Descriptive coding, summary, and case-ordered descriptive meta-matrices were used to group participants by their psychosocial responses to the intervention and associated behavior changes. Four psychosocial phenotypes of responses emerged: (a) Activated-successful behavior-changers with strong internal supports; (b) Inspired-motivated, but not fully successful behavior-changers with some internal supports, whose taste preferences and food environment overwhelmed their motivation; (c) Reinforced-already practiced target behaviors, were motivated, and had strong family support; and (d) Indifferent-uninterested in behavior change and only did target behaviors if family insisted. Our findings contribute to the field of behavioral medicine by suggesting the presence of specific subgroups of participants who respond differently to behavior change interventions and salient psychosocial characteristics that differentiate among these phenotypes. Future research should examine the utility of prospectively identifying psychosocial phenotypes for improving the tailoring of nutrition behavior change interventions.

Development and evaluation of a multimodal marker of major depressive disorder

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R² values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

NiO Nanoparticles for Exceptionally Stable DNA Adsorption and Its Extraction from Biological Fluids

Selective extraction of a small amount of nucleic acids from complex biological samples containing a high concentration of proteins is critical for bioanalytical chemistry. A number of previously published studies have focused on long, double-stranded DNA such as plasmid DNA. On the other hand, we are interested in short oligonucleotides. Nucleic acids have a negatively charged phosphate backbone that interacts with metal oxides strongly, and this may be used to distinguish them from proteins. In this work, a few metal oxide nanoparticles were screened, including NiO, CoO, ZnO, TiO₂, CeO₂, and Fe₃O₄ for DNA recovery. NiO had the highest DNA adsorption efficiency from mixtures containing bovine serum albumin or human blood serum. The adsorption of DNA by NiO was further characterized as a function of the pH, salt concentration, DNA length, and DNA sequence. The adsorption mechanism was studied by adding competing chemicals or denaturing agents. A striking observation was the extremely high adsorption affinity of NiO, much higher than that of the other tested oxides. Polyphosphate was the most effective agent for displacing adsorbed DNA, whereas simple inorganic phosphate was less effective. NiO was able to concentrate DNA from a serum mixture by 33- to 55-fold, depending on the serum concentration. NiO is thus a promising candidate for extracting DNA from biological samples.

Centrifugation-free extraction of circulating nucleic acids using immiscible liquid under vacuum pressure

Extraction of cell-free DNA (cfDNA), which exists at an extremely low concentration in plasma, is a critical process for either targeted-sensing or massive sequencing of DNAs. However, such small amount of DNA cannot be fully obtained without high-speed centrifugation (<20,000 g). Here, we developed a centrifugation-free cfDNA extraction method and system that utilizes an immiscible solvent under single low vacuum pressure throughout the entire process. It has been named Pressure and Immiscibility-Based EXtraction (PIBEX). The amounts of extracted cfDNA by PIBEX were compared with those extracted by the conventional gold standards such as QIAGEN using quantitative PCR (qPCR). The PIBEX system showed equal performance regarding extraction amount and efficiency compared to the existing method. Because the PIBEX eliminates the troublous and repetitive centrifugation processes in DNA extraction, it can be further utilized in microfluidic-sample preparation systems for circulating nucleic acids, which would lead to an integrated sample-to-answer system in liquid biopsies.

The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

Advances in high-throughput molecular biology and electronic health records (EHR), coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Big data require collection and analysis of data at an unprecedented scale and represents a paradigm shift in health care, offering (1) the capacity to generate new knowledge more quickly than traditional scientific approaches; (2) unbiased collection and analysis of data; and (3) a holistic understanding of biology and pathophysiology. Big data promises more personalized and precision medicine for patients with improved accuracy and earlier diagnosis, and therapy tailored to an individual’s unique combination of genes, environmental risk, and precise disease phenotype. This promise comes from data collected from numerous sources, ranging from molecules to cells, to tissues, to individuals and populations—and the integration of these data into networks that improve understanding of heath and disease. Big data-driven science should play a role in propelling comparative medicine and “one medicine” (i.e., the shared physiology, pathophysiology, and disease risk factors across species) forward. Merging of data from EHR across institutions will give access to patient data on a scale previously unimaginable, allowing for precise phenotype definition and objective evaluation of risk factors and response to therapy. High-throughput molecular data will give insight into previously unexplored molecular pathophysiology and disease etiology. Investigation and integration of big data from a variety of sources will result in stronger parallels drawn at the molecular level between human and animal disease, allow for predictive modeling of infectious disease and identification of key areas of intervention, and facilitate step-changes in our understanding of disease that can make a substantial impact on animal and human health. However, the use of big data comes with significant challenges. Here we explore the scope of “big data,” including its opportunities, its limitations, and what is needed capitalize on big data in one medicine.

What is precision medicine?

The term “precision medicine” has become very popular over recent years, fuelled by scientific as well as political perspectives. Despite its popularity, its exact meaning, and how it is different from other popular terms such as “stratified medicine”, “targeted therapy” or “deep phenotyping” remains unclear. Commonly applied definitions focus on the stratification of patients, sometimes referred to as a novel taxonomy, and this is derived using large-scale data including clinical, lifestyle, genetic and further biomarker information, thus going beyond the classical “signs-and-symptoms” approach.

While these aspects are relevant, this description leaves open a number of questions. For example, when does precision medicine begin? In which way does the stratification of patients translate into better healthcare? And can precision medicine be viewed as the end-point of a novel stratification of patients, as implied, or is it rather a greater whole?

To clarify this, the aim of this paper is to provide a more comprehensive definition that focuses on precision medicine as a process. It will be shown that this proposed framework incorporates the derivation of novel taxonomies and their role in healthcare as part of the cycle, but also covers related terms.

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Cancer therapy agents have been used extensively as cytotoxic drugs against tissue or organ of a specific type of cancer. With the better understanding of molecular mechanisms underlying carcinogenesis and cellular events during cancer progression and metastasis, it is now possible to use targeted therapy for these molecular events. Targeted therapy is able to identify cancer patients with dissimilar genetic defects at cellular level for the same cancer type and consequently requires individualized approach for treatment. Cancer therapy begins to shift steadily from the traditional approach of “one regimen for all patients” to a more individualized approach, through which each patient will be treated specifically according to their specific genetic defects. Personalized medicine accordingly requires identification of indicators or markers that guide in the decision making of such therapy to the chosen patients for more effective therapy. Cancer biomarkers are frequently used in clinical practice for diagnosis and prognosis, as well as identification of responsive patients and prediction of treatment response of cancer patient. The rapid breakthrough and development of microarray and sequencing technologies is probably the main tool for paving the way toward “individualized biomarker-driven cancer therapy” or “personalized medicine”. In this review, we aim to provide an updated knowledge and overview of the current landscape of cancer biomarkers and their role in personalized medicine, emphasizing the impact of genomics on the implementation of new potential targeted therapies and development of novel cancer biomarkers in improving the outcome of cancer therapy.

Early Diagnosis of Breast Cancer

Early-stage cancer detection could reduce breast cancer death rates significantly in the long-term. The most critical point for best prognosis is to identify early-stage cancer cells. Investigators have studied many breast diagnostic approaches, including mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have some limitations such as being expensive, time consuming and not suitable for young women. Developing a high-sensitive and rapid early-stage breast cancer diagnostic method is urgent. In recent years, investigators have paid their attention in the development of biosensors to detect breast cancer using different biomarkers. Apart from biosensors and biomarkers, microwave imaging techniques have also been intensely studied as a promising diagnostic tool for rapid and cost-effective early-stage breast cancer detection. This paper aims to provide an overview on recent important achievements in breast screening methods (particularly on microwave imaging) and breast biomarkers along with biosensors for rapidly diagnosing breast cancer.

Genetics and Genomics in Oncology Nursing: What Does Every Nurse Need to Know?

In addition to the need for basic education about genetics/genomics, other approaches are suggested to include awareness campaigns, continuing education courses, policy review, and onsite clinical development. These alternative learning strategies encourage oncology nurses across the continuum of care, from the bedside/seatside to oncology nurse research, to integrate genomics into all levels of practice and research in the specialty of oncology nursing. All nurses are warriors in the fight against cancer. The goal of this article is to identify genomic information that oncology nurses, at all levels of care, need to know and use as tools in the war against cancer.

Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor

Reliable determination of binding kinetics and affinity of DNA hybridization and single-base mismatches plays an essential role in systems biology, personalized and precision medicine. The standard tools are optical-based sensors that are difficult to operate in low cost and to miniaturize for high-throughput measurement. Biosensors based on nanowire field-effect transistors have been developed, but reliable and cost-effective fabrication remains a challenge. Here, we demonstrate that a graphene single-crystal domain patterned into multiple channels can measure time- and concentration-dependent DNA hybridization kinetics and affinity reliably and sensitively, with a detection limit of 10 pM for DNA. It can distinguish single-base mutations quantitatively in real time. An analytical model is developed to estimate probe density, efficiency of hybridization and the maximum sensor response. The results suggest a promising future for cost-effective, high-throughput screening of drug candidates, genetic variations and disease biomarkers by using an integrated, miniaturized, all-electrical multiplexed, graphene-based DNA array.

Monitoring DNA binding and single-base mismatches accurately in real time is difficult, especially for miniaturized devices. Here the authors report a graphene field-effect transistor array capable of reliably measuring DNA hybridization kinetics and affinity at the picomolar level.

Ayurvedic concept of Shatkriyakala: a traditional knowledge of cancer pathogenesis and therapy

The term Kriyakala refers to the recognition of the stage of a disease's progress, which helps to determine appropriate measure to correct the imbalance in Doshas (biological factors). It is a compound expression, comprised of Kriya and Kala, where Kriya means the choice to treatment (medicine, food and daily-routine) used to improve the disturbance in Doshas, and Kala refers to the stage of progress of a disease. Sushruta, an ancient Indian surgeon, has described the concept of Kriyakala in Varnaprashnadhyaya, an ancient Vedic Sanskrit text, which seeks to explain the incidence of Varnas in terms of Doshic disturbances. Varna, in modern parlance, may be described as an inflammatory process that may lead ulceration and chronic inflammation, promoting all stages of carcinogenesis. Abnormal interactions between Prakriti (genotype) and environmental factors vitiate the Doshas and impair immunity, which can lead to aberrant cell growth and cancer. Moreover, the interaction between vitiated Doshas and weak Dhatus (body tissues) manifests as cancers of a specific organ. Shatkriyakala (six stages of progress of a disease), on the other hand, provides a framework to assess the cancer and its pathogenesis in different stages. According to Ayurvedic concepts, all cancer therapies treat the affected tissues indirectly by eliminating vitiated Doshas, rejuvenating Dhatus and restoring immunity in cancer patients. The present review describes the six stages of Shatkriyakala in detail, with an emphasis on research areas to validate the concept of Shatkriyakala. This traditional knowledge can be utilized with modern technologies to detect predisposition for cancer or diagnose cancer in its early stages.

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Value-based and benefit-based strategies in deciding to bring a test into use should be distinguished

BACKGROUND

Regulatory and health technology assessment agencies have commented differently on the question whether results from enrichment studies can be used to justify to bring a test into use. We try to provide a framework to discuss this issue.

RESULTS

Mathematical definitions for the value and the benefit of a new diagnostic test are given. The possible conclusions about value and benefit from enrichment studies and interaction studies are explored. The terms benefit-based strategy and value-based strategy are introduced. Several potential consequences of using one of the two strategies in deciding to bring a test into use are identified and quantified. Interaction designs allow to assess benefit and value. Enrichment designs allow only to assess benefit. However, it is often probable that interaction studies allow no firm conclusions about the value. The advantage of a benefit-based strategy stems mainly from allowing test-positive patients earlier or even ever to benefit. The main disadvantage is a potential delay in detecting tests of no value.

CONCLUSIONS

Benefit-based strategies are preferable if the risk of off-label use and of delayed decisions on the value of a test can be limited. Otherwise, the superiority depends highly on research practice.

Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell

In this paper, we describe a novel method for analyte quantitation that does not rely on calibrants, internal standards, or calibration curves but, rather, leverages the relationship between disparate and predictable surface-directed analyte flux to an array of sensing addresses and a measured resultant signal. To reduce this concept to practice, we fabricated two flow cells such that the mean linear fluid velocity, U, was varied systematically over an array of electrodes positioned along the flow axis. This resulted in a predictable variation of the address-directed flux of a redox analyte, ferrocenedimethanol (FDM). The resultant limiting currents measured at a series of these electrodes, and accurately described by a convective-diffusive transport model, provided a means to calculate an "unknown" concentration without the use of calibrants, internal standards, or a calibration curve. Furthermore, the experiment and concentration calculation only takes minutes to perform. Deviation in calculated FDM concentrations from true values was minimized to less than 0.5% when empirically derived values of U were employed.

Comparing a stratified treatment strategy with the standard treatment in randomized clinical trials

The increasing emergence of predictive markers for different treatments in the same patient population allows us to define stratified treatment strategies. We consider randomized clinical trials that compare a standard treatment with a new stratified treatment strategy that divides the study population into subgroups receiving different treatments. Because the new strategy may not be beneficial in all subgroups, we consider in this paper an intermediate approach that establishes a treatment effect in a subset of patients built by joining several subgroups. The approach is based on the simple idea of selecting the subset with minimal p-value when testing the subset-specific treatment effects. We present a framework to compare this approach with other approaches to select subsets by introducing three performance measures. The results of a comprehensive simulation study are presented, and the relative merits of the various approaches are discussed. Copyright © 2016 John Wiley & Sons, Ltd.

Optimizing Trial Designs for Targeted Therapies

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor's as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

The impact of immunohistochemistry on the classification of lung tumors

INTRODUCTION

To highlight the role of immunohistochemistry to lung cancer classification on the basis of existing guidelines and future perspectives.

AREAS COVERED

Four orienting key-issues were structured according to an extensive review on the English literature: a) cancer subtyping; b) best biomarkers and rules to follow; c) negative and positive profiling; d) suggestions towards an evidence-based proposal for lung cancer subtyping. A sparing material approach based on a limited number of specific markers is highly desirable. It includes p40 for squamous cell carcinoma ('no p40, no squamous'), TTF1 for adenocarcinoma, synaptophysin for neuroendocrine tumors and vimentin for sarcomatoid carcinoma. A close relationship between genotype and phenotype also supports a diagnostic role for negative profiles. Expert commentary: Highly specific and sensitive IHC markers according to positive and negative diagnostic algorithms seem appropriate for individual patients' lung cancer subtyping.

The characterization of decellularized human skeletal muscle as a blueprint for mimetic scaffolds

The use of decellularized skeletal muscle (DSM) as a cell substrate and scaffold for the repair of volumetric muscle loss injuries has shown therapeutic promise. The performance of DSM materials motivated our interest in exploring the chemical and physical properties of this promising material. We suggest that these properties could serve as a blueprint for the development of next generation engineered materials with DSM mimetic properties. In this study, whole human lower limb rectus femoris (n = 10) and upper limb supraspinatus muscle samples (n = 10) were collected from both male and female tissue donors. Skeletal muscle samples were decellularized and nine property values, capturing key compositional, architectural, and mechanical properties, were measured and statistically analyzed. Mean values for each property were determined across muscle types and sexes. Additionally, the influence of muscle type (upper vs lower limb) and donor sex (male vs female) on each of the DSM material properties was examined. The data suggests that DSM materials prepared from lower limb rectus femoris samples have an increased modulus and contain a higher collagen content then upper limb supraspinatus muscles. Specifically, lower limb rectus femoris DSM material modulus and collagen content was approximately twice that of lower limb supraspinatus DSM samples. While muscle type did show some influence on material properties, we did not find significant trends related to sex. The material properties reported herein may be used as a blueprint for the data-driven design of next generation engineered scaffolds with muscle mimetic properties, as well as inputs for computational and physical models of skeletal muscle.

Association of CYP8A1 (Prostacyclin I2 synthase) polymorphism rs5602 with breast cancer in Mexican woman

Breast cancer (BCa) is the most common cancer in Mexican women. Certain risk factors, such as environmental and lifestyle factors have been implicated in BCa initiation and progression. Moreover, genetic factors, such as single nucleotide polymorphisms (SNPs) of the P450 system, have been reported in BCa. In this report, and for the first time in the literature, we analyzed the rs5602 (67730 T > C) polymorphism in the CYP8A1 in patients with BCa and in healthy Mexican women to identify a potential risk between this polymorphism and BCa. Leukocyte cells from 38 control patients and tissue from radical mastectomy surgeries in 64 BCa patients were used for polymorphism analysis using an allelic discrimination assay with TaqMan probes. Links with clinic-pathological characteristics were also analyzed. Statistical analysis was performed using the standard χ(2) or Fisher exact test statistic. All CYP8A1 genotypes were detected in patients with BCa and the controls. Significant differences were observed in the distribution of CYP8A1 genotypes between the patients and controls (P=0.0008) and allele C was significantly associated with BCa risk (OR 2.08, 95% CI 1.166-3.72, P=0.0178). All polymorphism frequencies were in Hardy-Weinberg Equilibrium (HWE) in the controls (P > 0.05). We found that variant 67730 T > C was significantly associated with an increased risk of BCa (P < 0.05). We not observed an association of the TT and TC + CC genotypes with the clinical stage, BIRADS, estrogen receptor (ER) status, progesterone receptor (PR) status, HER2 status, p53 status, CD34 status, metastasis or therapy use. These results indicate that the CYP8A1 rs5602 SNP is a possible risk factor for BCa in Mexican women. This study showed an association between the CYP8A1 polymorphism and BCa risk in a Mexican population.

Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

Genetic Mapping of Novel Loci Affecting Canine Blood Phenotypes

Since the publication of the dog genome and the construction of high-quality genome-wide SNP arrays, thousands of dogs have been genotyped for disease studies. For many of these dogs, additional clinical phenotypes are available, such as hematological and clinical chemistry results collected during routine veterinary care. Little is known about the genetic basis of variation in blood phenotypes, but this variation may play an important role in the etiology and progression of many diseases. From a cohort of dogs that had been previously genotyped on a semi-custom Illumina CanineHD array for various genome-wide association studies (GWAS) at Cornell University Hospital for Animals, we chose 353 clinically healthy, adult dogs for our analysis of clinical pathologic test results (14 hematological tests and 25 clinical chemistry tests). After correcting for age, body weight and sex, genetic associations were identified for amylase, segmented neutrophils, urea nitrogen, glucose, and mean corpuscular hemoglobin. Additionally, a strong genetic association (P = 8.1×10⁻¹³) was evident between a region of canine chromosome 13 (CFA13) and alanine aminotransferase (ALT), explaining 23% of the variation in ALT levels. This region of CFA13 encompasses the GPT gene that encodes the transferase. Dogs homozygous for the derived allele exhibit lower ALT activity, making increased ALT activity a less useful marker of hepatic injury in these individuals. Overall, these associations provide a roadmap for identifying causal variants that could improve interpretation of clinical blood tests and understanding of genetic risk factors associated with diseases such as canine diabetes and anemia, and demonstrate the utility of holistic phenotyping of dogs genotyped for disease mapping studies.

Theoretical assessment of feasibility to sequence DNA through interlayer electronic tunneling transport at aligned nanopores in bilayer graphene

Fast, cost effective, single-shot DNA sequencing could be the prelude of a new era in genetics. As DNA encodes the information for the production of proteins in all known living beings on Earth, determining the nucleobase sequences is the first and necessary step in that direction. Graphene-based nanopore devices hold great promise for next-generation DNA sequencing. In this work, we develop a novel approach for sequencing DNA using bilayer graphene to read the interlayer conductance through the layers in the presence of target nucleobases. Classical molecular dynamics simulations of DNA translocation through the pore were performed to trace the nucleobase trajectories and evaluate the interaction between the nucleobases and the nanopore. This interaction stabilizes the bases in different orientations, resulting in smaller fluctuations of the nucleobases inside the pore. We assessed the performance of a bilayer graphene nanopore setup for the purpose of DNA sequencing by employing density functional theory and non-equilibrium Green’s function method to investigate the interlayer conductance of nucleobases coupling simultaneously to the top and bottom graphene layers. The obtained conductance is significantly affected by the presence of DNA in the bilayer graphene nanopore, allowing us to analyze DNA sequences.

Validation of Self-Management Screening (SeMaS), a tool to facilitate personalised counselling and support of patients with chronic diseases

Background

A rising number of people with chronic conditions is offered interventions to enhance self-management. The responsiveness of individuals to these interventions depends on patient characteristics. We aimed to develop and validate a tool to facilitate personalised counselling and support for self-management in patients with chronic diseases in primary care.

Methods

We drafted a prototype of the tool for Self-Management Screening (SeMaS), comprising 27 questions that were mainly derived from validated questionnaires. To reach high content validity, we performed a literature review and held focus groups with patients and healthcare professionals as input for the tool. The characteristics self-efficacy, locus of control, depression, anxiety, coping, social support, and perceived burden of disease were incorporated into the tool. Three items were added to guide the type of support or intervention, being computer skills, functioning in groups, and willingness to perform self-monitoring. Subsequently, the construct and criterion validity of the tool were investigated in a sample of 204 chronic patients from two primary care practices. Patients filled in the SeMaS and a set of validated questionnaires for evaluation of SeMaS. The Patient Activation Measure (PAM-13), a generic instrument to measure patient health activation, was used to test the convergent construct validity.

Results

Patients had a mean age of 66.8 years and 46.6 % was female. 5.9 % did not experience any barrier to self-management, 28.9 % experienced one minor or major barrier, and 30.4 % two minor or major barriers. Compared to the criterion measures, the positive predictive value of the SeMaS characteristics ranged from 41.5 to 77.8 % and the negative predictive value ranged from 53.3 to 99.4 %. Crohnbach’s alpha for internal consistency ranged from 0.56 to 0.87, except for locus of control (α = 0.02). The regression model with PAM-13 as a dependent variable showed that the SeMaS explained 31.7 % (r² = 0.317) of the variance in the PAM-13 score.

Conclusions

SeMaS is a short validated tool that can signal potential barriers for self-management that need to be addressed in the dialogue with the patient. As such it can be used to facilitate personalised counselling and support to enhance self-management in patients with chronic conditions in primary care.

Electronic supplementary material

The online version of this article (doi:10.1186/s12875-015-0381-z) contains supplementary material, which is available to authorized users.

High-Performance Affinity Chromatography: Applications in Drug-Protein Binding Studies and Personalized Medicine

The binding of drugs with proteins and other agents in serum is of interest in personalized medicine because this process can affect the dosage and action of drugs. The extent of this binding may also vary with a given disease state. These interactions may involve serum proteins, such as human serum albumin or α1-acid glycoprotein, or other agents, such as lipoproteins. High-performance affinity chromatography (HPAC) is a tool that has received increasing interest as a means for studying these interactions. This review discusses the general principles of HPAC and the various approaches that have been used in this technique to examine drug-protein binding and in work related to personalized medicine. These approaches include frontal analysis and zonal elution, as well as peak decay analysis, ultrafast affinity extraction, and chromatographic immunoassays. The operation of each method is described and examples of applications for these techniques are provided. The type of information that can be obtained by these methods is also discussed, as related to the analysis of drug-protein binding and the study of clinical or pharmaceutical samples.

Health care sensor--based systems for point of care monitoring and diagnostic applications: a brief survey

Continuous, real-time remote monitoring through medical point--of--care (POC) systems appears to draw the interest of the scientific community for healthcare monitoring and diagnostic applications the last decades. Towards this direction a significant merit has been due to the advancements in several scientific fields. Portable, wearable and implantable apparatus may contribute to the betterment of today's healthcare system which suffers from fundamental hindrances. The number and heterogeneity of such devices and systems regarding both software and hardware components, i.e sensors, antennas, acquisition circuits, as well as the medical applications that are designed for, is impressive. Objective of the current study is to present the major technological advancements that are considered to be the driving forces in the design of such systems, to briefly state the new aspects they can deliver in healthcare and finally, the identification, categorization and a first level evaluation of them.

Dimethyl adipimidate/Thin film Sample processing (DTS); A simple, low-cost, and versatile nucleic acid extraction assay for downstream analysis

Sample processing, especially that involving nucleic acid extraction, is a prerequisite step for the isolation of high quantities of relatively pure DNA for downstream analyses in many life science and biomedical engineering studies. However, existing methods still have major problems, including labor-intensive time-consuming methods and high costs, as well as requirements for a centrifuge and the complex fabrication of filters and membranes. Here, we first report a versatile Dimethyl adipimidate/Thin film based Sample processing (DTS) procedure without the limitations of existing methods. This procedure is useful for the extraction of DNA from a variety of sources, including 6 eukaryotic cells, 6 bacteria cells, and 2 body fluids in a single step. Specifically, the DTS procedure does not require a centrifuge and has improved time efficiency (30 min), affordability, and sensitivity in downstream analysis. We validated the DTS procedure for the extraction of DNA from human body fluids, as well as confirmed that the quality and quantity of the extracted DNA were sufficient to allow robust detection of genetic and epigenetic biomarkers in downstream analysis.

The promise of biomarkers in diagnosing major depression in primary care: the present and future

Major depressive disorder (MDD) is the most prevalent psychiatric disorder, but it can be underdiagnosed or misdiagnosed. Most people with depression are seen in primary care settings, where there are limited resources to diagnose and treat the patient. There is a lack of clinically validated objective laboratory-based diagnostic tests to diagnose MDD; however, it is clear that these tests could greatly improve the correct and timely diagnosis. This review aims to give a cross-sectional view of current efforts of DNA methylomic, transcriptomic, and proteomic approaches to identify biomarkers. We outline our view of the biomarker developmental steps from discovery to clinical application. We then propose that better cooperation will lead us closer to the common goal of identifying biological biomarkers for major depression. "The important thing is not to stop questioning. Curiosity has its own reason for existing." Albert Einstein.

Comparative study of seven commercial kits for human DNA extraction from urine samples suitable for DNA biomarker-based public health studies

Human genomic DNA extracted from urine could be an interesting tool for large-scale public health studies involving characterization of genetic variations or DNA biomarkers as a result of the simple and noninvasive collection method. These studies, involving many samples, require a rapid, easy, and standardized extraction protocol. Moreover, for practicability, there is a necessity to collect urine at a moment different from the first void and to store it appropriately until analysis. The present study compared seven commercial kits to select the most appropriate urinary human DNA extraction procedure for epidemiological studies. DNA yield has been determined using different quantification methods: two classical, i.e., NanoDrop and PicoGreen, and two species-specific real-time quantitative (q)PCR assays, as DNA extracted from urine contains, besides human, microbial DNA also, which largely contributes to the total DNA yield. In addition, the kits giving a good yield were also tested for the presence of PCR inhibitors. Further comparisons were performed regarding the sampling time and the storage conditions. Finally, as a proof-of-concept, an important gene related to smoking has been genotyped using the developed tools. We could select one well-performing kit for the human DNA extraction from urine suitable for molecular diagnostic real-time qPCR-based assays targeting genetic variations, applicable to large-scale studies. In addition, successful genotyping was possible using DNA extracted from urine stored at -20°C for several months, and an acceptable yield could also be obtained from urine collected at different moments during the day, which is particularly important for public health studies.

Reinventing clinical trials: a review of innovative biomarker trial designs in cancer therapies

INTRODUCTION

Recently, new clinical trial designs involving biomarkers have been studied and proposed in cancer clinical research, in the hope of incorporating the rapid growing basic research into clinical practices.

SOURCES OF DATA

Journal articles related to various biomarkers and their role in cancer clinical trial, articles and books about statistical issues in trial design, and regulatory website, documents, and guidance for submission of targeted cancer therapies.

AREAS OF AGREEMENT

The drug development process involves four phases. The confirmatory Phase III is essential in regulatory approval of a special treatment.

AREAS OF CONTROVERSY

Regulatory agency has restrictions on confirmatory trials 'using adaptive designs'. No rule of thumb to pick the most appropriate design for biomarker-related trials.

GROWING POINTS

Statistical issues to solve in new designs. Regulatory acceptance of the 'newly proposed trial designs'.

AREAS TIMELY FOR DEVELOPING RESEARCH

Biomarker-related trial designs that can resolve the statistical issues and satisfy the regulatory requirement.

Personalized health care beyond oncology: new indications for immunoassay-based companion diagnostics

Personalized health care (PHC) is an evolving field of medicine aimed at providing the right therapy to the right patient at the right time. This approach often incorporates the use of companion diagnostics (CDx) assays that provide information essential for the safe and effective use of the corresponding drug. In addition to oncology, many other therapy areas, such as cardiovascular, neurological, and infectious and inflammatory diseases, may benefit from PHC, owing to disease complexity and heterogeneity. Furthermore, although most U.S. Food and Drug Administration–approved CDx are based on molecular‐based technologies, immunoassays can provide a significant contribution to the evolution of CDx in patient management. In this review we discuss how the incorporation of biomarker immunoassays into routine diagnostic testing may allow early and definitive detection of Alzheimer's disease and enable population enrichment in clinical trials. In addition, we will describe how biomarker‐based CDx immunoassays have potential utility for stratifying patients with asthma based on their potential response to therapy and for selecting treatment according to phenotypic profile. Continued research into the underlying disease pathology and development of accurate and reliable diagnostic assays may ensure that PHC becomes the future standard for many indications.

Celebrating the 30th anniversary of genetic epidemiology: how to define our scope?

We review the scope of the scientific discipline genetic epidemiology by considering the steps of genetic epidemiologic research. Starting from the classical definition of genetic epidemiology as provided by Morton and Chung [1978, ISBN-13: 9780125080507], we propose a slightly modernized definition of the term genetic epidemiology.

The clinical value of whole blood point-of-care biomarkers in large animal emergency and critical care medicine

OBJECTIVE

To summarize the current medical literature and provide a clinical perspective of whole blood point-of-care (POC) biomarkers in large animal emergency and critical care practice.

DATA SOURCES

Original studies, reviews, and textbook chapters in the human and veterinary medical fields.

SUMMARY

POC biomarkers are tests used to monitor normal or disease processes at or near the patient. In both human and veterinary medicine these tools are playing an increasingly important role in the management of critical diseases. The most important whole blood POC biomarkers available for veterinary practitioners include l-lactate, cardiac troponin I, serum amyloid A, triglyceride, creatinine, and glucose, although many other tests are available or on the horizon.

CONCLUSION

Whole blood POC biomarkers enable clinicians to provide improved management of critical diseases in large animals. These tools are especially useful for establishing a diagnosis, guiding therapy, and estimating disease risk and prognosis.

Adaptive designs for subpopulation analysis optimizing utility functions

If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.

Impact of Biomarkers on Personalized Medicine

The field of personalized medicine that involves the use of measuring biomarkers in clinical samples is an area of high interest and one that has tremendous impact on drug development. With the emergence of more sensitive and specific technologies that are now able to be run in clinical settings and the ability to accurately measure biomarkers, there is a need to understand how biomarkers are defined, how they are used in clinical trials, and most importantly how they are used in conjunction with drug treatment. Biomarker approaches have entered into early clinical trials and are increasingly being used to develop new diagnostics that help to differentiate or stratify the likely outcomes of therapeutic intervention. Tremendous efforts have been made to date to discover novel biomarkers for use in clinical practice. Still, the number of markers that make it into clinical practice is rather low. In the next following chapters, we will explain the various classifications of biomarkers, how they are applied, measured, and used in personalized medicine specifically focusing on how they are used in de-risking the 10 plus years drug development process and lastly how they are validated and transformed into companion diagnostic assays.

The European Medicines Agency experience with biomarker qualification

Since the launch of the qualification process in 2009, the CHMP reviewed/is reviewing 48 requests for qualification advice or opinion (as of Sept 2013) related to biomarkers (BM) or other novel drug development tools (e.g. patient reported outcome measures, modeling, and statistical methods). The qualification opinions are available on the EMA website (Qualification of novel methodologies for medicine development, http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp&mid=WC0b01ac0580022bb0#section2 , 2013). Also there is a trend of increasing numbers of qualification requests to CHMP, indicative of the pace that targeted drug development and personalized medicine is gaining and the need to bring the new tools from research to drug development and clinical use. This chapter will focus on the regulatory experience gained so far from the CHMP qualification procedure. Basic qualification principles will be presented. Through qualification examples, we will elaborate on common grounds and divergences between the different stakeholders.

Prognostication of patients with clear cell renal cell carcinomas based on quantification of DNA methylation levels of CpG island methylator phenotype marker genes

Background

The CpG island methylator phenotype (CIMP) of clear cell renal cell carcinomas (ccRCCs) is characterized by accumulation of DNA methylation at CpG islands and poorer patient outcome. The aim of this study was to establish criteria for prognostication of patients with ccRCCs using the ccRCC-specific CIMP marker genes.

Methods

DNA methylation levels at 299 CpG sites in the 14 CIMP marker genes were evaluated quantitatively in tissue specimens of 88 CIMP-negative and 14 CIMP-positive ccRCCs in a learning cohort using the MassARRAY system. An additional 100 ccRCCs were also analyzed as a validation cohort.

Results

Receiver operating characteristic curve analysis showed that area under the curve values for the 23 CpG units including the 32 CpG sites in the 7 CIMP-marker genes, i.e. FAM150A, ZNF540, ZNF671, ZNF154, PRAC, TRH and SLC13A5, for discrimination of CIMP-positive from CIMP-negative ccRCCs were larger than 0.95. Criteria combining the 23 CpG units discriminated CIMP-positive from CIMP-negative ccRCCs with 100% sensitivity and specificity in the learning cohort. Cancer-free and overall survival rates of patients with CIMP-positive ccRCCs diagnosed using the criteria combining the 23 CpG units in a validation cohort were significantly lower than those of patients with CIMP-negative ccRCCs (P = 1.41 × 10⁻⁵ and 2.43 × 10⁻¹³, respectively). Patients with CIMP-positive ccRCCs in the validation cohort had a higher likelihood of disease-related death (hazard ratio, 75.8; 95% confidence interval, 7.81 to 735; P = 1.89 × 10⁻⁴) than those with CIMP-negative ccRCCs.

Conclusions

The established criteria are able to reproducibly diagnose CIMP-positive ccRCCs and may be useful for personalized medicine for patients with ccRCCs.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-772) contains supplementary material, which is available to authorized users.

Evaluation of a chemoresponse assay as a predictive marker in the treatment of recurrent ovarian cancer: further analysis of a prospective study

BACKGROUND

Recently, a prospective study reported improved clinical outcomes for recurrent ovarian cancer patients treated with chemotherapies indicated to be sensitive by a chemoresponse assay, compared with those patients treated with non-sensitive therapies, thereby demonstrating the assay's prognostic properties. Due to cross-drug response over different treatments and possible association of in vitro chemosensitivity of a tumour with its inherent biology, further analysis is required to ascertain whether the assay performs as a predictive marker as well.

METHODS

Women with persistent or recurrent epithelial ovarian cancer (n=262) were empirically treated with one of 15 therapies, blinded to assay results. Each patient's tumour was assayed for responsiveness to the 15 therapies. The assay's ability to predict progression-free survival (PFS) was assessed by comparing the association when the assayed therapy matches the administered therapy (match) with the association when the assayed therapy is randomly selected, not necessarily matching the administered therapy (mismatch).

RESULTS

Patients treated with assay-sensitive therapies had improved PFS vs patients treated with non-sensitive therapies, with the assay result for match significantly associated with PFS (hazard ratio (HR)=0.67, 95% confidence interval (CI)=0.50-0.91, P=0.009). On the basis of 3000 simulations, the mean HR for mismatch was 0.81 (95% range=0.66-0.99), with 3.4% of HRs less than 0.67, indicating that HR for match is lower than for mismatch. While 47% of tumours were non-sensitive to all assayed therapies and 9% were sensitive to all, 44% displayed heterogeneity in assay results. Improved outcome was associated with the administration of an assay-sensitive therapy, regardless of homogeneous or heterogeneous assay responses across all of the assayed therapies.

CONCLUSIONS

These analyses provide supportive evidence that this chemoresponse assay is a predictive marker, demonstrating its ability to discern specific therapies that are likely to be more effective among multiple alternatives.

Genetic prediction in the Genetic Analysis Workshop 18 sequencing data

High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.

Prognostic and predictive biomarkers: tools in personalized oncology

Oncology indispensably leads us to personalized medicine, which allows an individual approach to be taken with each patient. Personalized oncology is based on pharmacogenomics and the effect of genetic differences in individuals (germline and somatic) on the way cancer patients respond to chemotherapeutics. Biomarkers detected using molecular biology tools allow the molecular characterization of cancer signatures and provide information relevant for personalized treatment. Biomarkers can be divided into two main subgroups: prognostic and predictive. The aim of the application of prognostic biomarkers, which provide information on the overall cancer outcome in patients, is to facilitate cancer diagnosis, usually with no need for putting invasive methods into use. Predictive biomarkers help to optimize therapy decisions, as they provide information on the likelihood of response to a given chemotherapeutic. Among the prognostic factors that identify patients with different outcome risks (e.g., recurrence of the disease), the following factors can be distinguished: somatic and germline mutations, changes in DNA methylation that lead to the enhancement or suppression of gene expression, the occurrence of elevated levels of microRNA (miRNA) capable of binding specific messenger RNA (mRNA) molecules, which affects gene expression, as well as the presence of circulating tumor cells (CTCs) in blood, which leads to a poor prognosis for the patient. Biomarkers for personalized oncology are used mainly in molecular diagnostics of chronic myeloid leukemia, colon, breast and lung cancer, and recently in melanoma. They are successfully used in the evaluation of the benefits that can be achieved through targeted therapy or in the evaluation of toxic effects of the chemotherapeutic used in the therapy.

[The relevance of biomarkers for personalised medicine]

In this paper the relevance of biomarkers for the benefit/risk assessment of pharmaceuticals and the validation of biomarkers within clinical trials is presented. For this purpose the most important study designs for validation are described and discussed. Using the example of the development of rosuvastatin and the validation of hs-CRP (JUPITER study), aspects that are necessary for the planning of a joint development program for a pharmaceutical drug and a biomarker are discussed. In addition, alternatives considering how the validation of the biomarker could be involved in the entire development program of the pharmaceutical drug are presented.