Personalized medicine using DNA biomarkers: a review

Systematic review: serum autoantibodies in the early detection of gastric cancer

Antibodies against tumor-associated antigens have been found in serum of patients with various types of cancers and may serve as biomarkers for early detection of gastric cancer as well. This systematic review aims to give an overview about known autoantibodies and their diagnostic value in gastric cancer. We conducted a systematic literature search in two databases to identify studies which performed serological testing for autoantibodies in gastric cancer patients and controls. Data on study characteristics and results were extracted independently by two reviewers. Overall, 39 articles reporting the detection of 34 different autoantibodies met the inclusion criteria for this review. The most common antibody detection method was enzyme-linked immunosorbent assay and the most frequently assessed autoantibody was anti-p53, which was tested in 13 studies. Most antibodies were assessed in only one study and only few authors have evaluated the diagnostic value of combinations of multiple autoantibodies. For single autoantibodies, specificity was generally very high (median: 99.15%), but sensitivity was mostly rather low (median: 12.35%). For some autoantibody combinations, substantially higher sensitivity at reasonably high levels of specificity could be achieved. Development of extended and optimized multimarker panels of autoantibodies might be a promising approach for gastric cancer early detection.

Solid phase nucleic acid extraction technique in a microfluidic chip using a novel non-chaotropic agent: dimethyl adipimidate

Here, we present a silicon microfluidic system for the purification and extraction of nucleic acids from human body fluid samples utilizing a dimethyl adipimidate (DMA)-based solid-phase extraction method. We propose DMA, which has been used as an amino-reactive cross-linking agent within cells and proteins, as a non-chaotropic reagent for the capture of nucleic acids to overcome the limitations of existing chaotropic and non-chaotropic techniques such as low binding efficiency, PCR inhibition and so on. DMA contains bi-functional imidoesters that form reversible cross-linking structures with DNA therefore providing a high surface-area to volume ratio for capturing DNA without structurally modifying microfluidic channels. In this work, we have first demonstrated highly efficient capture and purification of genomic DNA (T24 cell line) with DMA using a label-free silicon microring resonator sensor device. In addition, we observed the improvement of the DNA amplification efficiency by using the proposed technique for both the genetic (HRAS) and epigenetic (RARβ) analysis of DNA biomarkers. Particularly, we confirmed that the DMA-based solid-phase extraction technique can be applied for the extraction of genomic DNA with higher purity (p < 0.001) using human body fluids (blood and urine) in silicon microfluidic devices compared to other chaotropic methods. Therefore, the proposed technique would be able to harmonize with a micro-total analysis system platform for the analysis of genetic and epigenetic DNA biomarkers related to human diseases in the field of point-of-care (POC) diagnostic applications.

Molecular targets of HPV oncoproteins: potential biomarkers for cervical carcinogenesis

Cervical cancer is the second most common cancer among women worldwide and is responsible for 275,000 deaths each year. Persistent infection with high-risk human papillomavirus (HR-HPV) is an essential factor for the development of cervical cancer. Although the process is not fully understood, molecular mechanisms caused by HPV infection are necessary for its development and reveal a large number of potential biomarkers for diagnosis and prognosis. These molecules are host genes and/or proteins, and cellular microRNAs involved in cell cycle regulation that result from disturbed expression of HR-HPV E5, E6 and E7 oncoproteins. One of the current challenges in medicine is to discover potent biomarkers that can correctly diagnose cervical premalignant lesions and standardize clinical management. Currently, studies are showing that some of these molecules are potential biomarkers of cervical carcinogenesis, and it is possible to carry out a more accurate diagnosis and provide more appropriate follow-up treatment for women with cervical dysplasia. In this paper, we review recent research studies on cell cycle molecules deregulated by HPV infections, as well as their potential use for cervical cancer screening.

Functional molecular units for guiding biomarker panel design

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Implementation of personalized self-management support using the self-management screening questionnaire SeMaS; a study protocol for a cluster randomized trial

Background

The number of patients with one or more chronic diseases is rising. In several standards of care there is a focus on enhancing self-management. We applied the concept of personalization on self-management support and developed a self-management screening questionnaire (SeMaS). The main research objective is to assess the effectiveness of the SeMaS questionnaire and subsequent personalized self-management on patients’ self-management behaviors.

Methods/Design

A cluster randomized controlled trial will be set up in 15 general practices in the Netherlands. The practices are all group practices, and member of one care group. The practices will be assigned to the control or intervention arms by stratified randomization. The strata are determined by the participation of the practice nurses in a course for behavioral change, and the nurse’s workload. Patients can be included if they are over 18 years of age, have at least one chronic condition and have a checkup appointment with the practice nurse in the inclusion period. The intervention consists of screening patients with the SeMaS questionnaire, producing a graphic profile with the abilities or barriers for self-management. Patients will receive tailored feedback. Practice nurses are trained in using the profile to enhance self-management of the patient and provide personalized self-management support. The use of individual care plans and self-management interventions is stimulated. In the control arm patients will receive care as usual. Patients of both trial arms will be asked to fill in the SeMaS questionnaire and additional questionnaires at inclusion and after 6 months. The primary outcome is the difference in the level of patient activation (PAM-13) between baseline and 6 months. Secondary outcomes include patient measures for lifestyle factors (exercise, diet, smoking), and process measures from medical record data analysis.

Discussion

This manuscript presents the protocol for a cluster randomized clinical trial of personalized self-management support using the SeMaS questionnaire in chronically ill patients in primary care. By carrying out this study, scientific evidence is built for the effectiveness of personalized self-management support.

Trial registration

The Netherlands National Trial Register: NTR3960.

Actionable gene expression-based patient stratification for molecular targeted therapy in hepatocellular carcinoma

Background

The effectiveness of molecular targeted agents is modest in hepatocellular carcinoma (HCC). Efficacy of molecular targeted therapies has been better in cancer patients with high expression of actionable molecules defined as cognate target molecules. However, patient stratification based on the actionable molecules dictating the effectiveness of targeted drugs has remained understudied in HCC.

Experimental Design & Results

Paired tumor and non-tumoral tissues derived from a total of 130 HCC patients were studied. Real-time RT-PCR was used to analyze the mRNA expression of actionable molecules in the tissues. mRNA levels of EGFR, VEGFR2, PDGFRβ, FGFR1, and mTOR were up-regulated in tumors compared to non-tumors in 35.4, 42.3, 61.5, 24.6, and 50.0% of patients, respectively. Up-regulation of EGFR was observed at early stage and tended to gradually decrease toward late stages (BCLC stage A: 41.9%; B: 30.8%; C: 17.6%). Frequency of VEGFR2 expression in tumors at stage C was lower than that in the other stages (BCLC stage A: 45.9%; B: 41.0%; C: 29.4%). PDGFRβ and mTOR were observed to be up-regulated in more than 50% of tumors in all the stages whereas FGFR1 was up-regulated in only about 20% of HCC irrespective of stages. A cluster analysis of actionable gene expression revealed that HCC can be categorized into different subtypes that predict the effectiveness of molecular targeted agents and combination therapies in clinical trials. Analysis of in vitro sensitivity to sorafenib demonstrated that HCC cells with up-regulation of PDGFRβ and c-Raf mRNA are more susceptible to sorafenib treatment in a dose and time-dependent manner than cells with low expression of the genes.

Conclusions

mRNA expression analysis of actionable molecules could provide the rationale for new companion diagnostics-based therapeutic strategies in the treatment of HCC.

Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases

High throughput sequencing technologies have become essential in studies on genomics, epigenomics, and transcriptomics. Although sequencing information has traditionally been elucidated using a low throughput technique called Sanger sequencing, high throughput sequencing technologies are capable of sequencing multiple DNA molecules in parallel, enabling hundreds of millions of DNA molecules to be sequenced at a time. This advantage allows high throughput sequencing to be used to create large data sets, generating more comprehensive insights into the cellular genomic and transcriptomic signatures of various diseases and developmental stages. Within high throughput sequencing technologies, whole exome sequencing can be used to identify novel variants and other mutations that may underlie many genetic cardiac disorders, whereas RNA sequencing can be used to analyze how the transcriptome changes. Chromatin immunoprecipitation sequencing and methylation sequencing can be used to identify epigenetic changes, whereas ribosome sequencing can be used to determine which mRNA transcripts are actively being translated. In this review, we will outline the differences in various sequencing modalities and examine the main sequencing platforms on the market in terms of their relative read depths, speeds, and costs. Finally, we will discuss the development of future sequencing platforms and how these new technologies may improve on current sequencing platforms. Ultimately, these sequencing technologies will be instrumental in further delineating how the cardiovascular system develops and how perturbations in DNA and RNA can lead to cardiovascular disease.

Characteristics of pharmacogenomics/biomarker-guided clinical trials for regulatory approval of anti-cancer drugs in Japan

Pharmacogenomics (PGx) or biomarker (BM) has the potential to facilitate the development of safer and more effective drugs in terms of their benefit/risk profiles by stratifying population into categories such as responders/non-responders and high-/low-risks to drug-induced serious adverse reactions. In the past decade, practical use of PGx or BM has advanced the field of anti-cancer drug development. To identify the characteristics of the PGx/BM-guided clinical trials for regulatory approval of anti-cancer drugs in Japan, we collected information on design features of 'key trials' in the review reports of anti-cancer drugs that were approved after the implementation of the 'Revised Guideline for the Clinical Evaluation of Anti-cancer drugs' in April 2006. On the basis of the information available on the regulatory review data for the newly approved anti-cancer drugs in Japan, this article aims to explain the limitations and points to consider in the study design of PGx/BM-guided clinical trials.

Risk estimation and risk prediction using machine-learning methods

After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.